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Archive for the ‘privacy’ Category

Development, humanitarian and human rights organizations increasingly collect and use digital data at the various stages of their programming. This type of data has the potential to yield great benefit, but it can also increase individual and community exposure to harm and privacy risks. How can we as a sector better balance data collection and open data sharing with privacy and security, especially when it involves the most vulnerable?

A number of donors, humanitarian and development organizations (including Oxfam, CRS, UN bodies and others) have developed or are in the process of developing guidelines to help them to be more responsible about collection, use, sharing and retention of data from those who participate in their programs.

I’m part of a team (including mStar, Sonjara, Georgetown University, the USAID Global Development Lab, and an advisory committee that includes several shining stars from the ‘responsible data’ movement) that is conducting research on existing practices, policies, systems, and legal frameworks through which international development data is collected, used, shared, and released. Based on this research, we’ll develop ‘responsible data’ practice guidelines for USAID that aim to help:

  • Mitigate privacy and security risks for beneficiaries and others
  • Improve performance and development outcomes through use of data
  • Promote transparency, accountability and public good through open data

The plan is to develop draft guidelines and then to test their application on real programs.

We are looking for digital development projects to assess how our draft guidelines would work in real world settings. Once the projects are selected, members of the research team will visit them to better understand “on-the-ground” contexts and project needs. We’ll apply draft practice guidelines to each case with the goal of identifying what parts of the guidelines are useful/ applicable, and where the gaps are in the guidelines. We’ll also capture feedback from the project management team and partners on implications for project costs and timelines, and we’ll document existing digital data-related good practices and lessons. These findings will further refine USAID’s Responsible Data Practice guidelines.

What types of projects are we looking for?

  • Ongoing or recently concluded projects that are using digital technologies to collect, store, analyze, manage, use and share individuals’ data.
  • Cases where data collected is sensitive or may put project participants at risk.
  • The project should have informal or formal processes for privacy/security risk assessment and mitigation especially with respect to field implementation of digital technologies (listed above) as part of their program. These may be implicit or explicit (i.e. documented or written). They potentially include formal review processes conducted by ethics review boards or institutional review boards (IRBs) for projects.
  • All sectors of international development and all geographies are welcome to submit case studies. We are looking for diversity in context and programming.
  • We prefer case studies from USAID-funded projects but are open to receiving case studies from other donor-supported projects.

If you have a project or an activity that falls into the above criteria, please let us know here. We welcome multiple submissions from one organization; just reuse the form for each proposed case study.

Please submit your projects by February 15, 2017.

And please share this call with others who may be interested in contributing case studies.

Click here to submit your case study.

Also feel free to get in touch with me if you have questions about the project or the call!

 

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This post is co-authored by Emily Tomkys, Oxfam GB; Danna Ingleton, Amnesty International; and me (Linda Raftree, Independent)

At the MERL Tech conference in DC this month, we ran a breakout session on rethinking consent in the digital age. Most INGOs have not updated their consent forms and policies for many years, yet the growing use of technology in our work, for many different purposes, raises many questions and insecurities that are difficult to address. Our old ways of requesting and managing consent need to be modernized to meet the new realities of digital data and the changing nature of data. Is informed consent even possible when data is digital and/or opened? Do we have any way of controlling what happens with that data once it is digital? How often are organizations violating national and global data privacy laws? Can technology be part of the answer?

Let’s take a moment to clarify what kind of consent we are talking about in this post. Being clear on this point is important because there are many synchronous conversations on consent in relation to technology. For example there are people exploring the use of the consent frameworks or rhetoric in ICT user agreements – asking whether signing such user agreements can really be considered consent. There are others exploring the issue of consent for content distribution online, in particular personal or sensitive content such as private videos and photographs. And while these (and other) consent debates are related and important to this post, what we are specifically talking about is how we, our organizations and projects, address the issue of consent when we are collecting and using data from those who participate in programs or monitoring, evaluation, research and learning (MERL) that we are implementing.

This diagram highlights that no matter how someone is engaging with the data, how they do so and the decisions they make will impact on what is disclosed to the data subject.

No matter how someone is engaging with data, how they do so and the decisions they make will impact on what is disclosed to the data subject.

This is as timely as ever because introducing new technologies and kinds of data means we need to change how we build consent into project planning and implementation. In fact, it gives us an amazing opportunity to build consent into our projects in ways that our organizations may not have considered in the past. While it used to be that informed consent was the domain of frontline research staff, the reality is that getting informed consent – where there is disclosure, voluntariness, comprehension and competence of the data subject –  is the responsibility of anyone ‘touching’ the data.

Here we share examples from two organizations who have been exploring consent issues in their tech work.

Over the past two years, Girl Effect has been incorporating a number of mobile and digital tools into its programs. These include both the Girl Effect Mobile (GEM) and the Technology Enabled Girl Ambassadors (TEGA) programs.

Girl Effect Mobile is a global digital platform that is active in 49 countries and 26 languages. It is being developed in partnership with Facebook’s Free Basics initiative. GEM aims to provide a platform that connects girls to vital information, entertaining content and to each other. Girl Effect’s digital privacy, safety and security policy directs the organization to review and revise its terms and conditions to ensure that they are ‘girl-friendly’ and respond to local context and realities, and that in addition to protecting the organization (as many T&Cs are designed to do), they also protect girls and their rights. The GEM terms and conditions were initially a standard T&C. They were too long to expect girls to look at them on a mobile, the language was legalese, and they seemed one-sided. So the organization developed a new T&C with simplified language and removed some of the legal clauses that were irrelevant to the various contexts in which GEM operates. Consent language was added to cover polls and surveys, since Girl Effect uses the platform to conduct research and for its monitoring, evaluation and learning work. In addition, summary points are highlighted in a shorter version of the T&Cs with a link to the full T&Cs. Girl Effect also develops short articles about online safety, privacy and consent as part of the GEM content as a way of engaging girls with these ideas as well.

TEGA is a girl-operated mobile-enabled research tool currently operating in Northern Nigeria. It uses data-collection techniques and mobile technology to teach girls aged 18-24 how to collect meaningful, honest data about their world in real time. TEGA provides Girl Effect and partners with authentic peer-to-peer insights to inform their work. Because Girl Effect was concerned that girls being interviewed may not understand the consent they were providing during the research process, they used the mobile platform to expand on the consent process. They added a feature where the TEGA girl researchers play an audio clip that explains the consent process. Afterwards, girls who are being interviewed answer multiple choice follow up questions to show whether they have understood what they have agreed to. (Note: The TEGA team report that they have incorporated additional consent features into TEGA based on examples and questions shared in our session).

Oxfam, in addition to developing out their Responsible Program Data Policy, has been exploring ways in which technology can help address contemporary consent challenges. The organization had doubts on how much its informed consent statement (which explains who the organization is, what the research is about and why Oxfam is collecting data as well as asks whether the participant is willing to be interviewed) was understood and whether informed consent is really possible in the digital age. All the same, the organization wanted to be sure that the consent information was being read out in its fullest by enumerators (the interviewers). There were questions about what the variation might be on this between enumerators as well as in different contexts and countries of operation. To explore whether communities were hearing the consent statement fully, Oxfam is using mobile data collection with audio recordings in the local language and using speed violations to know whether the time spent on the consent page is sufficient, according to the length of the audio file played. This is by no means foolproof but what Oxfam has found so far is that the audio file is often not played in full and or not at all.

Efforts like these are only the beginning, but they help to develop a resource base and stimulate more conversations that can help organizations and specific projects think through consent in the digital age.

Additional resources include this framework for Consent Policies developed at a Responsible Data Forum gathering.

Because of how quickly technology and data use is changing, one idea that was shared was that rather than using informed consent frameworks, organizations may want to consider defining and meeting a ‘duty of care’ around the use of the data they collect. This can be somewhat accomplished through the creation of organizational-level Responsible Data Policies. There are also interesting initiatives exploring new ways of enabling communities to define consent themselves – like this data licenses prototype.

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The development and humanitarian sectors really need to take notice, adapt and update their thinking constantly to keep up with technology shifts. We should also be doing more sharing about these experiences. By working together on these types of wicked challenges, we can advance without duplicating our efforts.

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This post was written with input from Maliha Khan, Independent Consultant; Emily Tomkys, Oxfam GB; Siobhan Green, Sonjara and Zara Rahman, The Engine Room.

A friend reminded me earlier this month at the MERL Tech Conference that a few years ago when we brought up the need for greater attention to privacy, security and ethics when using ICTs and digital data in humanitarian and development contexts, people pointed us to Tor, encryption and specialized apps. “No, no, that’s not what we mean!” we kept saying. “This is bigger. It needs to be holistic. It’s not just more tools and tech.”

So, even if as a sector we are still struggling to understand and address all the different elements of what’s now referred to as “Responsible Data” (thanks to the great work of the Engine Room and key partners), at least we’ve come a long way towards framing and defining the areas we need to tackle. We understand the increasing urgency of the issue that the volume of data in the world is increasing exponentially and the data in our sector is becoming more and more digitalized.

This year’s MERL Tech included several sessions on Responsible Data, including Responsible Data Policies, the Human Element of the Data Cycle, The Changing Nature of Informed Consent, Remote Monitoring in Fragile Environments and plenary talks that mentioned ethics, privacy and consent as integral pieces of any MERL Tech effort.

The session on Responsible Data Policies was a space to share with participants why, how, and what policies some organizations have put in place in an attempt to be more responsible. The presenters spoke about the different elements and processes their organizations have followed, and the reasoning behind the creation of these policies. They spoke about early results from the policies, though it is still early days when it comes to implementing them.

What do we mean by Responsible Data?

Responsible data is about more than just privacy or encryption. It’s a wider concept that includes attention to the data cycle at every step, and puts the rights of people reflected in the data first:

  • Clear planning and purposeful collection and use of data with the aim of improving humanitarian and development approaches and results for those we work with and for
  • Responsible treatment of the data and respectful and ethical engagement with people we collect data from, including privacy and security of data and careful attention to consent processes and/or duty of care
  • Clarity on data sharing – what data, from whom and with whom and under what circumstances and conditions
  • Attention to transparency and accountability efforts in all directions (upwards, downwards and horizontally)
  • Responsible maintenance, retention or destruction of data.

Existing documentation and areas to explore

There is a huge bucket of concepts, frameworks, laws and policies that already exist in various other sectors and that can be used, adapted and built on to develop responsible approaches to data in development and humanitarian work. Some of these are in conflict with one another, however, and those conflicts need to be worked out or at least recognized if we are to move forward as a sector and/or in our own organizations.

Some areas to explore when developing a Responsible Data policy include:

  • An organization’s existing policies and practices (IT and equipment; downloading; storing of official information; confidentiality; monitoring, evaluation and research; data collection and storage for program administration, finance and audit purposes; consent and storage for digital images and communications; social media policies).
  • Local and global laws that relate to collection, storage, use and destruction of data, such as: Freedom of information acts (FOIA); consumer protection laws; data storage and transfer regulations; laws related to data collection from minors; privacy regulations such as the latest from the EU.
  • Donor grant requirements related to data privacy and open data, such as USAID’s Chapter 579 or International Aid Transparency Initiative (IATI) stipulations.

Experiences with Responsible Data Policies

At the MERL Tech Responsible Data Policy session, organizers and participants shared their experiences. The first step for everyone developing a policy was establishing wide agreement and buy-in for why their organizations should care about Responsible Data. This was done by developing Values and Principles that form the foundation for policies and guidance.

Oxfam’s Responsible Data policy has a focus on rights, since Oxfam is a rights-based organization. The organization’s existing values made it clear that ethical use and treatment of data was something the organization must consider to hold true to its ethos. It took around six months to get all of the global affiliates to agree on the Responsible Program Data policy, a quick turnaround compared to other globally agreed documents because all the global executive directors recognized that this policy was critical. A core point for Oxfam was the belief that digital identities and access will become increasingly important for inclusion in the future, and so the organization did not want to stand in the way of people being counted and heard. However, it wanted to be sure that this was done in a way that balanced and took privacy and security into consideration.

The policy is a short document that is now in the process of operationalization in all the countries where Oxfam works. Because many of Oxfam’s affiliate headquarters reside in the European Union, it needs to consider the new EU regulations on data, which are extremely strict, for example, providing everyone with an option for withdrawing consent. This poses a challenge for development agencies who normally do not have the type of detailed databases on ‘beneficiaries’ as they do on private donors. Shifting thinking about ‘beneficiaries’ and treating them more as clients may be in order as one result of these new regulations. As Oxfam moves into implementation, challenges continue to arise. For example, data protection in Yemen is different than data protection in Haiti. Knowing all the national level laws and frameworks and mapping these out alongside donor requirements and internal policies is extremely complicated, and providing guidance to country staff is difficult given that each country has different laws.

Girl Effect’s policy has a focus on privacy, security and safety of adolescent girls, who are the core constituency of the organization. The policy became clearly necessary because although the organization had a strong girl safeguarding policy and practice, the effect of digital data had not previously been considered, and the number of programs that involve digital tools and data is increasing. The Girl Effect policy currently has four core chapters: privacy and security during design of a tool, service or platform; content considerations; partner vetting; and MEAL considerations. Girl Effect looks at not only the privacy and security elements, but also aims to spur thinking about potential risks and unintended consequences for girls who access and use digital tools, platforms and content. One core goal is to stimulate implementers to think through a series of questions that help them to identify risks. Another is to establish accountability for decisions around digital data.

The policy has been in process of implementation with one team for a year and will be updated and adapted as the organization learns. It has proven to have good uptake so far from team members and partners, and has become core to how the teams and the wider organization think about digital programming. Cost and time for implementation increase with the incorporation of stricter policies, however, and it is challenging to find a good balance between privacy and security, the ability to safely collect and use data to adapt and improve tools and platforms, and user friendliness/ease of use.

Catholic Relief Services has an existing set of eight organizational principles: Sacredness and Dignity of the human person; Rights and responsibilities; Social Nature of Humanity; The Common Good; Subsidiarity; Solidarity; Option for the Poor; Stewardship. It was a natural fit to see how these values that are already embedded in the organization could extend to the idea of Responsible Data. Data is an extension of the human person, therefore it should be afforded the same respect as the individual. The principle of ‘common good’ easily extends to responsible data sharing. The notion of subsidiarity says that decision-making should happen as close as possible to the place where the impact of the decision will be the strongest, and this is nicely linked with the idea of sharing data back with communities where CRS works and engaging them in decision-making. The option for the poor urges CRS to place a preferential value on privacy, security and safety of the data of the poor over the data demands of other entities.

The organization is at the initial phase of creating its Responsible Data Policy. The process includes the development of the values and principles, two country learning visits to understand the practices of country programs and their concerns about data, development of the policy, and a set of guidelines to support staff in following the policy.

USAID recently embarked on its process of developing practical Responsible Data guidance to pair with its efforts in the area of open data. (See ADS 579). More information will be available soon on this initiative.

Where are we now?

Though several organizations are moving towards the development of policies and guidelines, it was clear from the session that uncertainties are the order of the day, as Responsible Data is an ethical question, often relying on tradeoffs and decisions that are not hard and fast. Policies and guidelines generally aim to help implementers ask the right questions, sort through a range of possibilities and weigh potential risks and benefits.

Another critical aspect that was raised at the MERL Tech session was the financial and staff resources that can be required to be responsible about data. On the other hand, for those organizations receiving funds from the European Union or residing in the EU or the UK (where despite Brexit, organizations will likely need to comply with EU Privacy Regulations), the new regulations mean that NOT being responsible about data may result in hefty fines and potential legal action.

Going from policy to implementation is a challenge that involves both capacity strengthening in this new area as well as behavior change and a better understanding of emerging concepts and multiple legal frameworks. The nuances by country, organization and donor make the process difficult to get a handle on.

Because staff and management are already overburdened, the trick to developing and implementing Responsible Data Policies and Practice will be finding ways to strengthen staff capacity and to provide guidance in ways that do not feel overwhelmingly complex. Though each situation will be different, finding ongoing ways to share resources and experiences so that we can advance as a sector will be one key step for moving forward.

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Over the past 4 years I’ve had the opportunity to look more closely at the role of ICTs in Monitoring and Evaluation practice (and the privilege of working with Michael Bamberger and Nancy MacPherson in this area). When we started out, we wanted to better understand how evaluators were using ICTs in general, how organizations were using ICTs internally for monitoring, and what was happening overall in the space. A few years into that work we published the Emerging Opportunities paper that aimed to be somewhat of a landscape document or base report upon which to build additional explorations.

As a result of this work, in late April I had the pleasure of talking with the OECD-DAC Evaluation Network about the use of ICTs in Evaluation. I drew from a new paper on The Role of New ICTs in Equity-Focused Evaluation: Opportunities and Challenges that Michael, Veronica Olazabal and I developed for the Evaluation Journal. The core points of the talk are below.

*****

In the past two decades there have been 3 main explosions that impact on M&E: a device explosion (mobiles, tablets, laptops, sensors, dashboards, satellite maps, Internet of Things, etc.); a social media explosion (digital photos, online ratings, blogs, Twitter, Facebook, discussion forums, What’sApp groups, co-creation and collaboration platforms, and more); and a data explosion (big data, real-time data, data science and analytics moving into the field of development, capacity to process huge data sets, etc.). This new ecosystem is something that M&E practitioners should be tapping into and understanding.

In addition to these ‘explosions,’ there’s been a growing emphasis on documentation of the use of ICTs in Evaluation alongside a greater thirst for understanding how, when, where and why to use ICTs for M&E. We’ve held / attended large gatherings on ICTs and Monitoring, Evaluation, Research and Learning (MERL Tech). And in the past year or two, it seems the development and humanitarian fields can’t stop talking about the potential of “data” – small data, big data, inclusive data, real-time data for the SDGs, etc. and the possible roles for ICT in collecting, analyzing, visualizing, and sharing that data.

The field has advanced in many ways. But as the tools and approaches develop and shift, so do our understandings of the challenges. Concern around more data and “open data” and the inherent privacy risks have caught up with the enthusiasm about the possibilities of new technologies in this space. Likewise, there is more in-depth discussion about methodological challenges, bias and unintended consequences when new ICT tools are used in Evaluation.

Why should evaluators care about ICT?

There are 2 core reasons that evaluators should care about ICTs. Reason number one is practical. ICTs help address real world challenges in M&E: insufficient time, insufficient resources and poor quality data. And let’s be honest – ICTs are not going away, and evaluators need to accept that reality at a practical level as well.

Reason number two is both professional and personal. If evaluators want to stay abreast of their field, they need to be aware of ICTs. If they want to improve evaluation practice and influence better development, they need to know if, where, how and why ICTs may (or may not) be of use. Evaluation commissioners need to have the skills and capacities to know which new ICT-enabled approaches are appropriate for the type of evaluation they are soliciting and whether the methods being proposed are going to lead to quality evaluations and useful learnings. One trick to using ICTs in M&E is understanding who has access to what tools, devices and platforms already, and what kind of information or data is needed to answer what kinds of questions or to communicate which kinds of information. There is quite a science to this and one size does not fit all. Evaluators, because of their critical thinking skills and social science backgrounds, are very well placed to take a more critical view of the role of ICTs in Evaluation and in the worlds of aid and development overall and help temper expectations with reality.

Though ICTs are being used along all phases of the program cycle (research/diagnosis and consultation, design and planning, implementation and monitoring, evaluation, reporting/sharing/learning) there is plenty of hype in this space.

Screen Shot 2016-05-25 at 3.14.31 PM

There is certainly a place for ICTs in M&E, if introduced with caution and clear analysis about where, when and why they are appropriate and useful, and evaluators are well-placed to take a lead in identifying and trailing what ICTs can offer to evaluation. If they don’t, others are going to do it for them!

Promising areas

There are four key areas (I’ll save the nuance for another time…) where I see a lot of promise for ICTs in Evaluation:

1. Data collection. Here I’d divide it into 3 kinds of data collection and note that the latter two normally also provide ‘real time’ data:

  • Structured data gathering – where enumerators or evaluators go out with mobile devices to collect specific types of data (whether quantitative or qualitative).
  • Decentralized data gathering – where the focus is on self-reporting or ‘feedback’ from program participants or research subjects.
  • Data ‘harvesting’ – where data is gathered from existing online sources like social media sites, What’sApp groups, etc.
  • Real-time data – which aims to provide data in a much shorter time frame, normally as monitoring, but these data sets may be useful for evaluators as well.

2. New and mixed methods. These are areas that Michael Bamberger has been looking at quite closely. New ICT tools and data sources can contribute to more traditional methods. But triangulation still matters.

  • Improving construct validity – enabling a greater number of data sources at various levels that can contribute to better understanding of multi-dimensional indicators (for example, looking at changes in the volume of withdrawals from ATMs, records of electronic purchases of agricultural inputs, satellite images showing lorries traveling to and from markets, and the frequency of Tweets that contain the words hunger or sickness).
  • Evaluating complex development programs – tracking complex and non-linear causal paths and implementation processes by combining multiple data sources and types (for example, participant feedback plus structured qualitative and quantitative data, big data sets/records, census data, social media trends and input from remote sensors).
  • Mixed methods approaches and triangulation – using traditional and new data sources (for example, using real-time data visualization to provide clues on where additional focus group discussions might need to be done to better understand the situation or improve data interpretation).
  • Capturing wide-scale behavior change – using social media data harvesting and sentiment analysis to better understand wide-spread, wide-scale changes in perceptions, attitudes, stated behaviors and analyzing changes in these.
  • Combining big data and real-time data – these emerging approaches may become valuable for identifying potential problems and emergencies that need further exploration using traditional M&E approaches.

3. Data Analysis and Visualization. This is an area that is less advanced than the data collection area – often it seems we’re collecting more and more data but still not really using it! Some interesting things here include:

  • Big data and data science approaches – there’s a growing body of work exploring how to use predictive analytics to help define what programs might work best in which contexts and with which kinds of people — (how this connects to evaluation is still being worked out, and there are lots of ethical aspects to think about here too — most of us don’t like the idea of predictive policing, and in some ways you could end up in a situation that is not quite what was aimed at.) With big data, you’ll often have a hypothesis and you’ll go looking for patterns in huge data sets. Whereas with evaluation you normally have particular questions and you design a methodology to answer them — it’s interesting to think about how these two approaches are going to combine.
  • Data Dashboards – these are becoming very popular as people try to work out how to do a better job of using the data that is coming into their organizations for decision making. There are some efforts at pulling data from community level all the way up to UN representatives, for example, the global level consultations that were done for the SDGs or using “near real-time data” to share with board members. Other efforts are more focused on providing frontline managers with tools to better tweak their programs during implementation.
  • Meta-evaluation – some organizations are working on ways to better draw conclusions from what we are learning from evaluation around the world and to better visualize these conclusions to inform investments and decision-making.

4. Equity-focused Evaluation. As digital devices and tools become more widespread, there is hope that they can enable greater inclusion and broader voice and participation in the development process. There are still huge gaps however — in some parts of the world 23% less women have access to mobile phones — and when you talk about Internet access the gap is much much bigger. But there are cases where greater participation in evaluation processes is being sought through mobile. When this is balanced with other methods to ensure that we’re not excluding the very poorest or those without access to a mobile phone, it can help to broaden out the pool of voices we are hearing from. Some examples are:

  • Equity-focused evaluation / participatory evaluation methods – some evaluators are seeking to incorporate more real-time (or near real-time) feedback loops where participants provide direct feedback via SMS or voice recordings.
  • Using mobile to directly access participants through mobile-based surveys.
  • Enhancing data visualization for returning results back to the community and supporting community participation in data interpretation and decision-making.

Challenges

Alongside all the potential, of course there are also challenges. I’d divide these into 3 main areas:

1. Operational/institutional

Some of the biggest challenges to improving the use of ICTs in evaluation are institutional or related to institutional change processes. In focus groups I’ve done with different evaluators in different regions, this was emphasized as a huge issue. Specifically:

  • Potentially heavy up-front investment costs, training efforts, and/or maintenance costs if adopting/designing a new system at wide scale.
  • Tech or tool-driven M&E processes – often these are also donor driven. This happens because tech is perceived as cheaper, easier, at scale, objective. It also happens because people and management are under a lot of pressure to “be innovative.” Sometimes this ends up leading to an over-reliance on digital data and remote data collection and time spent developing tools and looking at data sets on a laptop rather than spending time ‘on the ground’ to observe and engage with local organizations and populations.
  • Little attention to institutional change processes, organizational readiness, and the capacity needed to incorporate new ICT tools, platforms, systems and processes.
  • Bureaucracy levels may mean that decisions happen far from the ground, and there is little capacity to make quick decisions, even if real-time data is available or the data and analysis are provided frequently to decision-makers sitting at a headquarters or to local staff who do not have decision-making power in their own hands and must wait on orders from on high to adapt or change their program approaches and methods.
  • Swinging too far towards digital due to a lack of awareness that digital most often needs to be combined with human. Digital technology always works better when combined with human interventions (such as visits to prepare folks for using the technology, making sure that gatekeepers; e.g., a husband or mother-in-law is on-board in the case of women). A main message from the World Bank 2016 World Development Report “Digital Dividends” is that digital technology must always be combined with what the Bank calls “analog” (a.k.a. “human”) approaches.

B) Methodological

Some of the areas that Michael and I have been looking at relate to how the introduction of ICTs could address issues of bias, rigor, and validity — yet how, at the same time, ICT-heavy methods may actually just change the nature of those issues or create new issues, as noted below:

  • Selection and sample bias – you may be reaching more people, but you’re still going to be leaving some people out. Who is left out of mobile phone or ICT access/use? Typical respondents are male, educated, urban. How representative are these respondents of all ICT users and of the total target population?
  • Data quality and rigor – you may have an over-reliance on self-reporting via mobile surveys; lack of quality control ‘on the ground’ because it’s all being done remotely; enumerators may game the system if there is no personal supervision; there may be errors and bias in algorithms and logic in big data sets or analysis because of non-representative data or hidden assumptions.
  • Validity challenges – if there is a push to use a specific ICT-enabled evaluation method or tool without it being the right one, the design of the evaluation may not pass the validity challenge.
  • Fallacy of large numbers (in cases of national level self-reporting/surveying) — you may think that because a lot of people said something that it’s more valid, but you might just be reinforcing the viewpoints of a particular group. This has been shown clearly in research by the World Bank on public participation processes that use ICTs.
  • ICTs often favor extractive processes that do not involve local people and local organizations or provide benefit to participants/local agencies — data is gathered and sent ‘up the chain’ rather than shared or analyzed in a participatory way with local people or organizations. Not only is this disempowering, it may impact on data quality if people don’t see any point in providing it as it is not seen to be of any benefit.
  • There’s often a failure to identify unintended consequences or biases arising from use of ICTs in evaluation — What happens when you introduce tablets for data collection? What happens when you collect GPS information on your beneficiaries? What risks might you be introducing or how might people react to you when you are carrying around some kind of device?

C) Ethical and Legal

This is an area that I’m very interested in — especially as some donors have started asking for the raw data sets from any research, studies or evaluations that they are funding, and when these kinds of data sets are ‘opened’ there are all sorts of ramifications. There is quite a lot of heated discussion happening here. I was happy to see that DFID has just conducted a review of ethics in evaluationSome of the core issues include:

  • Changing nature of privacy risks – issues here include privacy and protection of data; changing informed consent needs for digital data/open data; new risks of data leaks; and lack of institutional policies with regard to digital data.
  • Data rights and ownership: Here there are some issues with proprietary data sets, data ownership when there are public-private partnerships, the idea of data philanthropy’ when it’s not clear whose data is being donated, personal data ‘for the public good’, open data/open evaluation/ transparency, poor care taken when vulnerable people provide personally identifiable information; household data sets ending up in the hands of those who might abuse them, the increasing impossibility of data anonymization given that crossing data sets often means that re-identification is easier than imagined.
  • Moving decisions and interpretation of data away from ‘the ground’ and upwards to the head office/the donor.
  • Little funding for trialing/testing the validity of new approaches that use ICTs and documenting what is working/not working/where/why/how to develop good practice for new ICTs in evaluation approaches.

Recommendations: 12 tips for better use of ICTs in M&E

Despite the rapid changes in the field in the 2 years since we first wrote our initial paper on ICTs in M&E, most of our tips for doing it better still hold true.

  1. Start with a high-quality M&E plan (not with the tech).
    • But also learn about the new tech-related possibilities that are out there so that you’re not missing out on something useful!
  2. Ensure design validity.
  3. Determine whether and how new ICTs can add value to your M&E plan.
    • It can be useful to bring in a trusted tech expert in this early phase so that you can find out if what you’re thinking is possible and affordable – but don’t let them talk you into something that’s not right for the evaluation purpose and design.
  4. Select or assemble the right combination of ICT and M&E tools.
    • You may find one off the shelf, or you may need to adapt or build one. This is a really tough decision, which can take a very long time if you’re not careful!
  5. Adapt and test the process with different audiences and stakeholders.
  6. Be aware of different levels of access and inclusion.
  7. Understand motivation to participate, incentivize in careful ways.
    • This includes motivation for both program participants and for organizations where a new tech-enabled tool/process might be resisted.
  8. Review/ensure privacy and protection measures, risk analysis.
  9. Try to identify unintended consequences of using ICTs in the evaluation.
  10. Build in ways for the ICT-enabled evaluation process to strengthen local capacity.
  11. Measure what matters – not what a cool ICT tool allows you to measure.
  12. Use and share the evaluation learnings effectively, including through social media.

 

 

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Crowdsourcing our Responsible Data questions, challenges and lessons. (Photo courtesy of Amy O'Donnell).

Crowdsourcing our Responsible Data questions, challenges and lessons. (Photo by Amy O’Donnell).

At Catholic Relief Services’ ICT4D Conference in May 2016, I worked with Amy O’Donnell  (Oxfam GB) and Paul Perrin (CRS) to facilitate a participatory session that explored notions of Digital Privacy, Security and Safety. We had a full room, with a widely varied set of experiences and expertise.

The session kicked off with stories of privacy and security breaches. One person told of having personal data stolen when a federal government clearance database was compromised. We also shared how a researcher in Denmark scraped very personal data from the OK Cupid online dating site and opened it up to the public.

A comparison was made between the OK Cupid data situation and the work that we do as development professionals. When we collect very personal information from program participants, they may not expect that their household level income, health data or personal habits would be ‘opened’ at some point.

Our first task was to explore and compare the meaning of the terms: Privacy, Security and Safety as they relate to “digital” and “development.”

What do we mean by privacy?

The “privacy” group talked quite a bit about contextuality of data ownership. They noted that there are aspects of privacy that cut across different groups of people in different societies, and that some aspects of privacy may be culturally specific. Privacy is concerned with ownership of data and protection of one’s information, they said. It’s about who owns data and who collects and protects it and notions of to whom it belongs. Private information is that which may be known by some but not by all. Privacy is a temporal notion — private information should be protected indefinitely over time. In addition, privacy is constantly changing. Because we are using data on our mobile phones, said one person, “Safaricom knows we are all in this same space, but we don’t know that they know.”

Another said that in today’s world, “You assume others can’t know something about you, but things are actually known about you that you don’t even know that others can know. There are some facts about you that you don’t think anyone should know or be able to know, but they do.” The group mentioned website terms and conditions, corporate ownership of personal data and a lack of control of privacy now. Some felt that we are unable to maintain our privacy today, whereas others felt that one could opt out of social media and other technologies to remain in control of one’s own privacy. The group noted that “privacy is about the appropriate use of data for its intended purpose. If that purpose shifts and I haven’t consented, then it’s a violation of privacy.”

What do we mean by security?

The Security group considered security to relate to an individual’s information. “It’s your information, and security of it means that what you’re doing is protected, confidential, and access is only for authorized users.” Security was also related to the location of where a person’s information is hosted and the legal parameters. Other aspects were related to “a barrier – an anti-virus program or some kind of encryption software, something that protects you from harm…. It’s about setting roles and permissions on software and installing firewalls, role-based permissions for accessing data, and cloud security of individuals’ data.” A broader aspect of security was linked to the effects of hacking that lead to offline vulnerability, to a lack of emotional security or feeling intimidated in an online space. Lastly, the group noted that “we, not the systems, are the weakest link in security – what we click on, what we view, what we’ve done. We are our own worst enemies in terms of keeping ourselves and our data secure.”

What do we mean by safety?

The Safety group noted that it’s difficult to know the difference between safety and security. “Safety evokes something highly personal. Like privacy… it’s related to being free from harm personally, physically and emotionally.” The group raised examples of protecting children from harmful online content or from people seeking to harm vulnerable users of online tools. The aspect of keeping your online financial information safe, and feeling confident that a service was ‘safe’ to use was also raised. Safety was considered to be linked to the concept of risk. “Safety engenders a level of trust, which is at the heart of safety online,” said one person.

In the context of data collection for communities we work with – safety was connected to data minimization concepts and linked with vulnerability, and a compounded vulnerability when it comes to online risk and safety. “If one person’s data is not safely maintained it puts others at risk,” noted the group. “And pieces of information that are innocuous on their own may become harmful when combined.” Lastly, the notion of safety as related to offline risk or risk to an individual due to a specific online behavior or data breach was raised.

It was noted that in all of these terms: privacy, security and safety, there is an element of power, and that in this type of work, a power relations analysis is critical.

The Digital Data Life Cycle

After unpacking the above terms, Amy took the group through an analysis of the data life cycle (courtesy of the Engine Room’s Responsible Data website) in order to highlight the different moments where the three concepts (privacy, security and safety) come into play.

Screen Shot 2016-05-25 at 6.51.50 AM

  • Plan/Design
  • Collect/Find/Acquire
  • Store
  • Transmit
  • Access
  • Share
  • Analyze/use
  • Retention
  • Disposal
  • Afterlife

Participants added additional stages in the data life cycle that they passed through in their work (coordinate, monitor the process, monitor compliance with data privacy and security policies). We placed the points of the data life cycle on the wall, and invited participants to:

  • Place a pink sticky note under the stage in the data life cycle that resonates or interests them most and think about why.
  • Place a green sticky note under the stage that is the most challenging or troublesome for them or their organizations and think about why.
  • Place a blue sticky note under the stage where they have the most experience, and to share a particular experience or tip that might help others to better manage their data life cycle in a private, secure and safe way.

Challenges, concerns and lessons

Design as well as policy are important!

  • Design drives everScreen Shot 2016-05-25 at 7.21.07 AMything else. We often start from the point of collection when really it’s at the design stage when we should think about the burden of data collection and define what’s the minimum we can ask of people? How we design – even how we get consent – can inform how the whole process happens.
  • When we get part-way through the data life cycle, we often wish we’d have thought of the whole cycle at the beginning, during the design phase.
  • In addition to good design, coordination of data collection needs to be thought about early in the process so that duplication can be reduced. This can also reduce fatigue for people who are asked over and over for their data.
  • Informed consent is such a critical issue that needs to be linked with the entire process of design for the whole data life cycle. How do you explain to people that you will be giving their data away, anonymizing, separating out, encrypting? There are often flow down clauses in some contracts that shifts responsibilities for data protection and security and it’s not always clear who is responsible for those data processes? How can you be sure that they are doing it properly and in a painstaking way?
  • Anonymization is also an issue. It’s hard to know to what level to anonymize things like call data records — to the individual? Township? District Level? And for how long will anonymization actually hold up?
  • The lack of good design and policy contributes to overlapping efforts and poor coordination of data collection efforts across agencies. We often collect too much data in poorly designed databases.
  • Policy is not enough – we need to do a much better job of monitoring compliance with policy.
  • Institutional Review Boards (IRBs) and compliance aspects need to be updated to the new digital data reality. At the same time, sometimes IRBs are not the right instrument for what we are aiming to achieve.

Data collection needs more attention.

  • Data collection is the easy part – where institutions struggle is with analyzing and doing something with the data we collect.
  • Organizations often don’t have a well-structured or systematic process for data collection.
  • We need to be clearer about what type of information we are collecting and why.
  • We need to update our data protection policy.

Reasons for data sharing are not always clear.

  • How can share data securely and efficiently without building duplicative systems? We should be thinking more during the design and collection phase about whether the data is going to be interoperable and who needs to access it.
  • How can we get the right balance in terms of data sharing? Some donors really push for information that can put people in real danger – like details of people who have participated in particular programs that would put them at risk with their home governments. Organizations really need to push back against this. It’s an education thing with donors. Middle management and intermediaries are often the ones that push for this type of data because they don’t really have a handle on the risk it represents. They are the weak points because of the demands they are putting on people. This is a challenge for open data policies – leaving it open to people leaves it to doing the laziest job possible of thinking about the potential risks for that data.
  • There are legal aspects of sharing too – such as the USAID open data policy where those collecting data have to share with the government. But we don’t have a clear understanding of what the international laws are about data sharing.
  • There are so many pressures to share data but they are not all fully thought through!

Data analysis and use of data are key weak spots for organizations.

  • We are just beginning to think through capturing lots of data.
  • Data is collected but not always used. Too often it’s extractive data collection. We don’t have the feedback loops in place, and when there are feedback loops we often don’t use the the feedback to make changes.
  • We forget often to go back to the people who have provided us with data to share back with them. It’s not often that we hold a consultation with the community to really involve them in how the data can be used.

Secure storage is a challenge.

  • We have hundreds of databases across the agency in various formats, hard drives and states of security, privacy and safety. Are we able to keep these secure?
  • We need to think more carefully about where we hold our data and who has access to it. Sometimes our data is held by external consultants. How should we be addressing that?

Disposing of data properly in a global context is hard!

  • Screen Shot 2016-05-25 at 7.17.58 AMIt’s difficult to dispose of data when there are multiple versions of it and a data footprint.
  • Disposal is an issue. We’re doing a lot of server upgrades and many of these are remote locations. How do we ensure that the right disposal process is going on globally, short of physically seeing that hard drives are smashed up!
  • We need to do a better job of disposal on personal laptops. I’ve done a lot of data collection on my personal laptop – no one has ever followed up to see if I’ve deleted it. How are we handling data handover? How do you really dispose of data?
  • Our organization hasn’t even thought about this yet!

Tips and recommendations from participants

  • Organizations should be using different tools. They should be using Pretty Good Privacy techniques rather than relying on free or commercial tools like Google or Skype.
  • People can be your weakest link if they are not aware or they don’t care about privacy and security. We send an email out to all staff on a weekly basis that talks about taking adequate measures. We share tips and stories. That helps to keep privacy and security front and center.
  • Even if you have a policy the hard part is enforcement, accountability, and policy reform. If our organizations are not doing direct policy around the formation of best practices in this area, then it’s on us to be sure we understand what is best practice, and to advocate for that. Let’s do what we can before the policy catches up.
  • The Responsible Data Forum and Tactical Tech have a great set of resources.
  • Oxfam has a Responsible Data Policy and Girl Effect have developed a Girls’ Digital Privacy, Security and Safety Toolkit that can also offer some guidance.

In conclusion, participants agreed that development agencies and NGOs need to take privacy, security and safety seriously. They can no longer afford to implement security at a lower level than corporations. “Times are changing and hackers are no longer just interested in financial information. People’s data is very valuable. We need to change and take security as seriously as corporates do!” as one person said.

 

 

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At our April 5th Salon in Washington, DC we had the opportunity to take a closer look at open data and privacy and discuss the intersection of the two in the framework of ‘responsible data’. Our lead discussants were Amy O’Donnell, Oxfam GB; Rob Baker, World Bank; Sean McDonald, FrontlineSMS. I had the pleasure of guest moderating.

What is Responsible Data?

We started out by defining ‘responsible data‘ and some of the challenges when thinking about open data in a framework of responsible data.

The Engine Room defines ‘responsible data’ as

the duty to ensure people’s rights to consent, privacy, security and ownership around the information processes of collection, analysis, storage, presentation and reuse of data, while respecting the values of transparency and openness.

Responsible Data can be like walking a tightrope, noted our first discussant, and you need to find the right balance between opening data and sharing it, all the while being ethical and responsible. “Data is inherently related to power – it can create power, redistribute it, make the powerful more powerful or further marginalize the marginalized. Getting the right balance involves asking some key questions throughout the data lifecycle from design of the data gathering all the way through to disposal of the data.

How can organizations be more responsible?

If an organization wants to be responsible about data throughout the data life cycle, some questions to ask include:

  • In whose interest is it to collect the data? Is it extractive or empowering? Is there informed consent?
  • What and how much do you really need to know? Is the burden of collecting and the liability of storing the data worth it when balanced with the data’s ability to represent people and allow them to be counted and served? Do we know what we’ll actually be doing with the data?
  • How will the data be collected and treated? What are the new opportunities and risks of collecting and storing and using it?
  • Why are you collecting it in the first place? What will it be used for? Will it be shared or opened? Is there a data sharing MOU and has the right kind of consent been secured? Who are we opening the data for and who will be able to access and use it?
  • What is the sensitivity of the data and what needs to be stripped out in order to protect those who provided the data?

Oxfam has developed a data deposit framework to help assess the above questions and make decisions about when and whether data can be open or shared.

(The Engine Room’s Responsible Development Data handbook offers additional guidelines and things to consider)

(See: https://wiki.responsibledata.io/Data_in_the_project_lifecycle for more about the data lifecycle)

Is ‘responsible open data’ an oxymoron?

Responsible Data policies and practices don’t work against open data, our discussant noted. Responsible Data is about developing a framework so that data can be opened and used safely. It’s about respecting the time and privacy of those who have provided us with data and reducing the risk of that data being hacked. As more data is collected digitally and donors are beginning to require organizations to hand over data that has been collected with their funding, it’s critical to have practical resources and help staff to be more responsible about data.

Some disagreed that consent could be truly informed and that open data could ever be responsible since once data is open, all control over the data is lost. “If you can’t control the way the data is used, you can’t have informed people. It’s like saying ‘you gave us permission to open your data, so if something bad happens to you, oh well….” Informed consent is also difficult nowadays because data sets are being used together and in ways that were not possible when informed consent was initially obtained.

Others noted that standard informed consent practices are unhelpful, as people don’t understand what might be done with their data, especially when they have low data literacy. Involving local communities and individuals in defining what data they would like to have and use could make the process more manageable and useful for those whose data we are collecting, using and storing, they suggested.

One person said that if consent to open data was not secured initially; the data cannot be opened, say, 10 years later. Another felt that it was one thing to open data for a purpose and something entirely different to say “we’re going to open your data so people can do fun things with it, to play around with it.”

But just what data are we talking about?

USAID was questioned for requiring grantees to share data sets and for leaning towards de-identification rather than raising the standard to data anonymity. One person noted that at one point the agency had proposed a 22-step process for releasing data and even that was insufficient for protecting program participants in a risky geography because “it’s very easy to figure out who in a small community recently received 8 camels.” For this reason, exclusions are an important part of open data processes, he said.

It’s not black or white, said another. Responsible open data is possible, but openness happens along a spectrum. You have financial data on the one end, which should be very open as the public has a right to know how its tax dollars are being spent. Human subjects research is on the other end, and it should not be totally open. (Author’s note: The Open Knowledge Foundation definition of open data says: “A key point is that when opening up data, the focus is on non-personal data, that is, data which does not contain information about specific individuals.” The distinction between personal data, such as that in household level surveys, and financial data on agency or government activities seems to be blurred or blurring in current debates around open data and privacy.) “Open data will blow up in your face if it’s not done responsibly,” he noted. “But some of the open data published via IATI (the International Aid Transparency Initiative) has led to change.”

A participant followed this comment up by sharing information from a research project conducted on stakeholders’ use of IATI data in 3 countries. When people knew that the open data sets existed they were very excited, she said. “These are countries where there is no Freedom of Information Act (FOIA), and where people cannot access data because no one will give it to them. They trusted the US Government’s data more than their own government data, and there was a huge demand for IATI data. People were very interested in who was getting what funding. They wanted information for planning, coordination, line ministries and other logistical purposes. So let’s not underestimate open data. If having open data sets means that governments, health agencies or humanitarian organizations can do a better job of serving people, that may make for a different kind of analysis or decision.”

‘Open by default’ or ‘open by demand’?

Though there are plenty of good intentions and rationales for open data, said one discussant, ‘open by default’ is a mistake. We may have quick wins with a reduction in duplicity of data collection, but our experiences thus far do not merit ‘open by default’. We have not earned it. Instead, he felt that ‘open by demand’ is a better idea. “We can put out a public list of the data that’s available and see what demand for data comes in. If we are proactive on what is available and what can be made available, and we monitor requests, we can avoid putting out information that no one is interested in. This would lower the overhead on what we are releasing. It would also allow us to have a conversation about who needs this data and for what.”

One participant agreed, positing that often the only reason that we collect data is to provide proof and evidence that we’re doing our job, spending the money given to us, and tracking back. “We tend to think that the only way to provide this evidence is to collect data: do a survey, talk to people, look at website usage. But is anyone actually using this data, this evidence to make decisions?”

Is the open data honeymoon over?

“We need to do a better job of understanding the impact at a wider level,” said another participant, “and I think it’s pretty light. Talking about open data is too general. We need to be more service oriented and problem driven. The conversation is very different when you are using data to solve a particular problem and you can focus on something tangible like service delivery or efficiency. Open data is expensive and not sustainable in the current setup. We need to figure this out.”

Another person shared results from an informal study on the use of open data portals around the world. He found around 2,500 open data portals, and only 3.8% of them use https (the secure version of http). Most have very few visitors, possibly due to poor Internet access in the countries whose open data they are serving up, he said. Several exist in countries with a poor Freedom House ranking and/or in countries at the bottom end of the World Bank’s Digital Dividends report. “In other words, the portals have been built for people who can’t even use them. How responsible is this?” he asked, “And what is the purpose of putting all that data out there if people don’t have the means to access it and we continue to launch more and more portals? Where’s all this going?”

Are we conflating legal terms?

Legal frameworks around data ownership were debated. Some said that the data belonged to the person or agency that collected it or paid for the cost of collecting in terms of copyright and IP. Others said that the data belonged to the individual who provided it. (Author’s note: Participants may have been referring to different categories of data, eg., financial data from government vs human subjects data.) The question was raised of whether informed consent for open data in the humanitarian space is basically a ‘contract of adhesion’ (a term for a legally binding agreement between two parties wherein one side has all the bargaining power and uses it to its advantage). Asking a person to hand over data in an emergency situation in order to enroll in a humanitarian aid program is akin to holding a gun to a person’s head in order to get them to sign a contract, said one person.

There’s a world of difference between ‘published data’ and ‘openly licensed data,’ commented our third discussant. “An open license is a complete lack of control, and you can’t be responsible with something you can’t control. There are ways to be responsible about the way you open something, but once it’s open, your responsibility has left the port.” ‘Use-based licensing’ is something else, and most IP is governed by how it’s used. For example, educational institutions get free access to data because they are educational institutions. Others pay and this subsidized their use of this data, he explained.

One person suggested that we could move from the idea of ‘open data’ to sub-categories related to how accessible the data would be and to whom and for what purposes. “We could think about categories like: completely open, licensed, for a fee, free, closed except for specific uses, etc.; and we could also specify for whom, whose data and for what purposes. If we use the term ‘accessible’ rather than ‘open’ perhaps we can attach some restrictions to it,” she said.

Is data an asset or a liability?

Our current framing is wrong, said one discussant. We should think of data as a toxic asset since as soon as it’s in our books and systems, it creates proactive costs and proactive risks. Threat modeling is a good approach, he noted. Data can cause a lot of harm to an organization – it’s a liability, and if it’s not used or stored according to local laws, an agency could be sued. “We’re far under the bar. We are not compliant with ‘safe harbor’ or ECOWAS regulations. There are libel questions and property laws that our sector is ignorant of. Our good intentions mislead us in terms of how we are doing things. There is plenty of room to build good practice here, he noted, for example through Civic Trusts. Another participant noted that insurance underwriters are already moving into this field, meaning that they see growing liability in this space.

How can we better engage communities and the grassroots?

Some participants shared examples of how they and their organizations have worked closely at the grassroots level to engage people and communities in protecting their own privacy and using open data for their own purposes. Threat modeling is an approach that helps improve data privacy and security, said one. “When we do threat modeling, we treat the data that we plan to collect as a potential asset. At each step of collection, storage, sharing process – we ask, ‘how will we protect those assets? What happens if we don’t share that data? If we don’t collect it? If we don’t delete it?’”

In one case, she worked with very vulnerable women working on human rights issues and together the group put together an action plan to protect its data from adversaries. The threats that they had predicted actually happened and the plan was put into action. Threat modeling also helps to “weed the garden once you plant it,” she said, meaning that it helps organizations and individuals keep an eye on their data, think about when to delete data, pay attention to what happens after data’s opened and dedicate some time for maintenance rather than putting all their attention on releasing and opening data.

More funding needs to be made available for data literacy for those whose data has been collected and/or opened. We need to help people think about what data is of use to them also. One person recalled hearing people involved in the creation of the Kenya Open Government Data portal say that the entire process was a waste of time because of low levels of use of any of the data. There are examples, however, of people using open data and verifying it at community level. For example, high school students in one instance found the data on all the so-called grocery stores in their community and went one-by-one checking into them, and identifying that some of these were actually liquor stores selling potato chips, not actual grocery stores. Having this information and engaging with it can be powerful for local communities’ advocacy work.

Are we the failure here? What are we going to do about it?

One discussant felt that ‘data’ and ‘information’ are often and easily conflated. “Data alone is not power. Information is data that is contextualized into something that is useful.” This brings into question the value of having so many data portals, and so much risk, when so little is being done to turn data into information that is useful to the people our sector says it wants to support and empower.

He gave the example of the Weather Channel, a business built around open data sets that are packaged and broadcast, which just got purchased for $2 billion. Channels like radio that would have provided information to the poor were not purchased, only the web assets, meaning that those who benefit are not the disenfranchised. “Our organizations are actually just like the Weather Channel – we are intermediaries who are interested in taking and using open data for public good.”

As intermediaries, we can add value in the dissemination of this open data, he said. If we have the skills, the intention and the knowledge to use it responsibly, we have a huge opportunity here. “However our enlightened intent has not yet turned this data into information and knowledge that communities can use to improve their lives, so are we the failure here? And if so, what are we doing about it? We could immediately begin engaging communities and seeing what is useful to them.” (See this article for more discussion on how ‘open’ may disenfranchise the poor.)

Where to from here?

Some points raised that merit further discussion and attention include:

  • There is little demand or use of open data (such as government data and finances) and preparing and maintaining data sets is costly – ‘open by demand’ may be a more appropriate approach than ‘open by default.’
  • There is a good deal of disagreement about whether data can be opened responsibly. Some of this disagreement may stem from a lack of clarity about what kind of data we are talking about when we talk about open data.
  • Personal data and human subjects data that was never foreseen to be part of “open data” is potentially being opened, bringing with it risks for those who share it as well as for those who store it.
  • Informed consent for personal/human subject data is a tricky concept and it’s not clear whether it is even possible in the current scenario of personal data being ‘opened’ and the lack of control over how it may be used now or in the future, and the increasing ease of data re-identification.
  • We may want to look at data as a toxic asset rather than a beneficial one, because of the liabilities it brings.
  • Rather than a blanket “open” categorization, sub-categorizations that restrict data sets in different ways might be a possibility.
  • The sector needs to improve its understanding of the legal frameworks around data and data collection, storage and use or it may start to see lawsuits in the near future.
  • Work on data literacy and community involvement in defining what data is of interest and is collected, as well as threat modeling together with community groups is a way to reduce risk and improve data quality, demand and use; but it’s a high-touch activity that may not be possible for every kind of organization.
  • As data intermediaries, we need to do a much better job as a sector to see what we are doing with open data and how we are using it to provide services and contextualized information to the poor and disenfranchised. This is a huge opportunity and we have not done nearly enough here.

The Technology Salon is conducted under Chatham House Rule so attribution has not been made in this post. If you’d like to attend future Salons, sign up here

 

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Our March 18th Technology Salon NYC covered the Internet of Things and Global Development with three experienced discussants: John Garrity, Global Technology Policy Advisor at CISCO and co-author of Harnessing the Internet of Things for Global Development; Sylvia Cadena, Community Partnerships Specialist, Asia Pacific Network Information Centre (APNIC) and the Asia Information Society Innovation Fund (ISIF); and Andy McWilliams, Creative Technologist at ThoughtWorks and founder and director of Art-A-Hack and Hardware Hack Lab.

By Wilgengebroed on Flickr [CC BY 2.0 (http://creativecommons.org/licenses/by/2.0)%5D, via Wikimedia Commons

What is the Internet of Things?

One key task at the Salon was clarifying what exactly is the “Internet of Things.” According to Wikipedia:

The Internet of Things (IoT) is the network of physical objects—devices, vehicles, buildings and other items—embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data.[1] The IoT allows objects to be sensed and controlled remotely across existing network infrastructure,[2] creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit;[3][4][5][6][7][8] when IoT is augmented with sensors and actuators, the technology becomes an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, smart homes, intelligent transportation and smart cities. Each thing is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure. Experts estimate that the IoT will consist of almost 50 billion objects by 2020.[9]

As one discussant explained, the IoT involves three categories of entities: sensors, actuators and computing devices. Sensors read data in from the world for computing devices to process via a decision logic which then generates some type of action back out to the world (motors that turn doors, control systems that operate water pumps, actions happening through a touch screen, etc.). Sensors can be anything from video cameras to thermometers or humidity sensors. They can be consumer items (like a garage door opener or a wearable device) or industrial grade (like those that keep giant machinery running in an oil field). Sensors are common in mobile phones, but more and more we see them being de-coupled from cell phones and integrated into or attached to all manner of other every day things. The boom in the IoT means that in whereas in the past, a person may have had one URL for their desktop computer, now they might be occupying several URLs:  through their phone, their iPad, their laptop, their Fitbit and a number of other ‘things.’

Why does IoT matter for Global Development?

Price points for sensors are going down very quickly and wireless networks are steadily expanding — not just wifi but macro cellular technologies. According to one lead discussant, 95% of the world is covered by 2G and two-thirds by 3G networks. Alongside that is a plethora of technology that is wide range and low tech. This means that all kinds of data, all over the world, are going to be available in massive quantities through the IoT. Some are excited about this because of how data can be used to track global development indicators, for example, the type of data being sought to measure the Sustainable Development Goals (SDGs). Others are concerned about the impact of data collected via the IoT on privacy.

What are some examples of the IoT in Global Development?

Discussants and others gave many examples of how the IoT is making its way into development initiatives, including:

  • Flow meters and water sensors to track whether hand pumps are working
  • Protecting the vaccine cold chain – with a 2G thermometer, an individual can monitor the cold chain for local use and the information also goes directly to health ministries and to donors
  • Monitoring the environment and tracking animals or endangered species
  • Monitoring traffic routes to manage traffic systems
  • Managing micro-irrigation of small shareholder plots from a distance through a feature phone
  • As a complement to traditional monitoring and evaluation (M&E) — a sensor on a cook stove can track how often a stove is actually used (versus information an individual might provide using recall), helping to corroborate and reduce bias
  • Verifying whether a teacher is teaching or has shown up to school using a video camera

The CISCO publication on the IoT and Global Development provides many more examples and an overview of where the area is now and where it’s heading.

How advanced is the IoT in the development space?

Currently, IoT in global development is very much a hacker space, according to one discussant. There are very few off the shelf solutions that development or humanitarian organizations can purchase and readily implement. Some social enterprises are ramping up activity, but there is no larger ecosystem of opportunities for off the shelf products.

Because the IoT in global development is at an early phase, challenges abound. Technical issues, power requirements, reliability and upkeep of sensors (which need to be calibrated), IP issues, security and privacy, technical capacity, and policy questions all need to be worked out. One discussant noted that these challenges carry on from the mobile for development (m4d) and information and communication technologies for development (ICT4D) work of the past.

Participants agreed that challenges are currently huge. For example, devices are homogeneous, making them very easy to hack and affect a lot of devices at once. No one has completely gotten their head around the privacy and consent issues, which are are very different than those of using FB. There are lots of interoperability issues also. As one person highlighted — there are over 100 different communication protocols being used today. It is more complicated than the old “BetaMax v VHS” question – we have no idea at this point what the standard will be for IoT.

For those who see the IoT as a follow-on from ICT4D and m4d, the big question is how to make sure we are applying what we’ve learned and avoiding the same mistakes and pitfalls. “We need to be sure we’re not committing the error of just seeing the next big thing, the next shiny device, and forgetting what we already know,” said one discussant. There is plenty of material and documentation on how to avoid repeating past mistakes, he noted. “Read ICT works. Avoid pilotitis. Don’t be tech-led. Use open source and so on…. Look at the digital principles and apply them to the IoT.”

A higher level question, as one person commented, is around the “inconvenient truth” that although ICTs drive economic growth at the macro level, they also drive income inequality. No one knows how the IoT will contribute or create harm on that front.

Are there any existing standards for the IoT? Should there be?

Because there is so much going on with the IoT – new interventions, different sectors, all kinds of devices, a huge variety in levels of use, from hacker spaces up to industrial applications — there are a huge range of standards and protocols out there, said one discussant. “We don’t really want to see governments picking winners or saying ‘we’re going to us this or that.’ We want to see the market play out and the better protocols to bubble up to the surface. What’s working best where? What’s cost effective? What open protocols might be most useful?”

Another discussant pointed out that there is a legacy predating the IOT: machine-to-machine (M2M), which has not always been Internet based. “Since this legacy is still there. How can we move things forward with regard to standardization and interoperability yet also avoid leaving out those who are using M2M?”

What’s up with IPv4 and IPv6 and the IoT? (And why haven’t I heard about this?)

Another crucial technical point raised is that of IPv4 and IPv6, something that not many Salon participant had heard of, but that will greatly impact on how the IoT rolls out and expands, and just who will be left out of this new digital divide. (Note: I found this video to be helpful for explaining IPv4 vs IPv6.)

“Remember when we used Netscape and we understood how an IP number translated into an IP address…?” asked one discussant. “Many people never get that lovely experience these days, but it’s important! There is a finite number of IP4 addresses and they are running out. Only Africa and Latin America have addresses left,” she noted.

IPv6 has been around for 20 years but there has not been a serious effort to switch over. Yet in order to connect the next billion and the multiple devices that they may bring online, we need more addresses. “Your laptop, your mobile, your coffee pot, your fridge, your TV – for many of us these are all now connected devices. One person might be using 10 IP addresses. Multiply that by millions of people, and the only thing that makes sense is switching over to IPv6,” she said.

There is a problem with the technical skills and the political decisions needed to make that transition happen. For much of the world, the IoT will not happen very smoothly and entire regions may be left out of the IoT revolution if high level decision makers don’t decide to move ahead with IPv6.

What are some of the other challenges with global roll-out of IoT?

In addition to the IPv4 – IPv6 transition, there are all kinds of other challenges with the IoT, noted one discussant. The technical skills required to make the transition that would enable IoT in some regions, for example Asia Pacific, are sorely needed. Engineers will need to understand how to make this shift happen, and in some places that is going to be a big challenge. “Things have always been connected to the Internet. There are just going to be lots more, different things connected to the Internet now.”

One major challenge is that there are huge ethical questions along with security and connectivity holes (as I will outline later in this summary post, and as discussed in last year’s salon on Wearable Technologies). In addition, noted one discussant, if we are designing networks that are going to collect data for diseases, for vaccines, for all kinds of normal businesses, and put the data in the cloud, developing countries need to have the ability to secure the data, the computing capacity to deal with it, and the skills to do their own data analysis.

“By pushing the IoT onto countries and not supporting the capacity to manage it, instead of helping with development, you are again creating a giant gap. There will be all kinds of data collected on climate change in the Pacific Island Countries, for example, but the countries don’t have capacity to deal with this data. So once more it will be a bunch of outsiders coming in to tell the Pacific Islands how to manage it, all based on conclusions that outsiders are making based on sensor data with no context,” alerted one discussant. “Instead, we should be counseling our people, our countries to figure out what they want to do with these sensors and with this data and asking them what they need to strengthen their own capacities.”

“This is not for the SDGs and ticking off boxes,” she noted. “We need to get people on the ground involved. We need to decentralize this so that people can make their own decisions and manage their own knowledge. This is where the real empowerment is – where local people and country leaders know how to collect data and use it to make their own decisions. The thing here is ownership — deploying your own infrastructure and knowing what to do with it.”

How can we balance the shiny devices with the necessary capacities?

Although the critical need to invest in and support country-level capacity to manage the IoT has been raised, this type of back-end work is always much less ‘sexy’ and less interesting for donors than measuring some development programming with a flashy sensor. “No one wants to fund this capacity strengthening,” said one discussant. “Everyone just wants to fund the shiny sensors. This chase after innovation is really damaging the impact that technology can actually have. No one just lets things sit and develop — to rest and brew — instead we see everyone rushing onto the next big thing. This is not a good thing for a small country that doesn’t have the capacity to jump right into it.”

All kinds of things can go wrong if people are not trained on how to manage the IoT. Devices can be hacked and they may be collecting and sharing data without an individuals’ knowledge (see Geoff Huston on The Internet of Stupid Things). Electrical short outs, common in places with poor electricity ecosystems, can also cause big problems. In addition, the Internet is affected by legacy systems – so we need interoperability that goes backwards, said one discussant. “If we don’t make at least a small effort to respect those legacy systems, we’re basically saying ‘if you don’t have the funding to update your system, you’re out.’ This then reinforces a power dynamic where countries need the international community to give them equipment, or they need to buy this or buy that, and to bring in international experts from the outside….’ The pressure on poor countries to make things work, to do new kinds of M&E, to provide evidence is huge. With that pressure comes a higher risk of falling behind very quickly. We are also seeing pilot projects that were working just fine without fancy tech being replaced by new fangled tech-type programs instead of being supported over the longer term,” she said.

Others agreed that the development sector’s fascination with shiny and new is detrimental. “There is very little concern for the long-term, the legacy system, future upgrades,” said one participant. “Once the blog post goes up about the cool project, the sensors go bad or stop working and no one even knows because people have moved on.” Another agreed, citing that when visiting numerous clinics for a health monitoring program in one country, the running joke among the M&E staff was “OK, now let’s go and find the broken solar panel.” “When I think of the IoT,” she said, “I think of a lot of broken devices in 5 years.” The aspect of eWaste and the IoT has not even begun to be examined or quantified, noted another.

It is increasingly important for governments to understand how the Internet works, because they are making policy about it. Manufacturers need to better understand how the tech works on the ground, especially in different contexts that they are not accustomed to working in. Users need a better understanding of all of this because their privacy is at risk. Legal frameworks around data and national laws need more attention as well. “When you are working with restrictive governments, your organization’s or start-up’s idea might actually be illegal or close to a sedition law and you may end up in jail,” noted one discussant.

What choices will organizations need to make regarding the IoT?

When it comes to actually making decisions on how involved an organization should and can be in supporting or using the IoT, one critical choice will be related the suites of devices, said our third discussant. Will it be a cloud device? A local computing device? A computer?

Organizations will need to decide if they want a vendor that gives them a package, or if they want a modular, interoperable approach of units. They will need to think about aspects like whether they want to go with proprietary or open source and will it be plug and play?

There are trade-offs here and key technical infrastructure choices will need to be made based on a certain level of expertise and experience. If organizations are not sure what they need, they may wish to get some advice before setting up a system or investing heavily.

As one discussant put it, “When I talk about the IOT, I often say to think about what the Internet was in the 90s. Think about that hazy idea we had of what the Internet was going to be. We couldn’t have predicted in the 90s what today’s internet would look like, and we’re in the same place with the IoT,” he said. “There will be seismic change. The state of the whole sector is immature now. There are very hard choices to make.”

Another aspect that’s representative of the IoT’s early stage, he noted, is that the discussion is all focusing on http and the Internet. “The IOT doesn’t necessarily even have to involve the Internet,” he said.

Most vendors are offering a solution with sensors to deploy, actuators to control and a cloud service where you log in to find your data. The default model is that the decision logic takes place there in the cloud, where data is stored. In this model, the cloud is in the middle, and the devices are around it, he said, but the model does not have to be that way.

Other models can offer more privacy to users, he said. “When you think of privacy and security – the healthcare maxim is ‘do no harm.’ However this current, familiar model for the IoT might actually be malicious.” The reason that the central node in the commercial model is the cloud is because companies can get more and more detailed information on what people are doing. IoT vendors and IoT companies are interested in extending their profiles of people. Data on what people do in their virtual life can now be combined with what they do in their private lives, and this has huge commercial value.

One option to look at, he shared, is a model that has a local connectivity component. This can be something like bluetooth mesh, for example. In this way, the connectivity doesn’t have to go to the cloud or the Internet at all. This kind of set-up may make more sense with local data, and it can also help with local ownership, he said. Everything that happens in the cloud in the commercial model can actually happen on a local hub or device that opens just for the community of users. In this case, you don’t have to share the data with the world. Although this type of a model requires greater local tech capacity and can have the drawback that it is more difficult to push out software updates, it’s an option that may help to enhance local ownership and privacy.

This requires a ‘person first’ concept of design. “When you are designing IOT systems, he said, “start with the value you are trying to create for individuals or organizations on the ground. And then implement the local part that you need to give local value. Then, only if needed, do you add on additional layers of the onion of connectivity, depending on the project.” The first priority here are the goals that the technology design will achieve for individual value, for an individual client or community, not for commercial use of people’s data.

Another point that this discussant highlighted was the need to conduct threat modeling and to think about unintended consequences. “If someone hacked this data – what could go wrong?” He suggested working backwards and thinking: “What should I take offline? How do I protect it better? How do I anonymize it better.”

In conclusion….

It’s critical to understand the purpose of an IoT project or initiative, discussants agreed, to understand if and why scale is needed, and to be clear about the drivers of a project. In some cases, the cloud is desirable for quicker, easier set up and updates to software. At the same time, if an initiative is going to be sustainable, then community and/or country capacity to run it, sustain it, keep it protected and private, and benefit from it needs to be built in. A big part of that capacity includes the ability to understand the different layers that surround the IoT and to make grounded decisions on the various trade-offs that will come to a head in the process of design and implementation. These skills and capacities need to be developed and supported within communities, countries and organizations if the IoT is to contribute ethically and robustly to global development.

Thanks to APNIC for sponsoring and supporting this Salon and to our friends at ThoughtWorks for hosting! If you’d like to join discussions like this one in cities around the world, sign up at Technology Salon

Salons are held under Chatham House Rule, therefore no attribution has been made in this post.

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Our December 2015 Technology Salon discussion in NYC focused on approaches to girls’ digital privacy, safety and security. By extension, the discussion included ways to reduce risk for other vulnerable populations. Our lead discussants were Ximena BenaventeGirl Effect Mobile (GEM) and Jonathan McKay, Praekelt Foundation. I also shared a draft Girls’ Digital Privacy, Safety and Security Policy and Toolkit I’ve been working on with both organizations over the past year.

Girls’ digital privacy, safety and security risks

Our first discussant highlighted why it’s important to think specifically about girls and digital security. In part, this is because different factors and vulnerabilities combine, exacerbating girls’ levels of risk. For example, girls living on less than $2 per day likely only have access to basic mobile phones, which are often borrowed from parents or siblings. The organization she works with always starts with deep research on aspects like ownership vs. borrowship and whether girls’ mobile usage is free/unlimited and un-supervised or controlled by gatekeepers such as parents, brothers, or other relatives. This helps to design better tools, services and platforms and to design for safety and security, she said. “Gatekeepers are very restrictive in many cases, but parental oversight is not necessarily a bad thing. We always work with parents and other gatekeepers as well as with girls themselves when we design and test.” When girls are living in more traditional or conservative societies, she said, we also need to think about how content might affect girls both online and offline. For example, “is content sufficiently progressive in terms of girls’ rights, yet safe for girls to read, comment on or discuss with friends and family without severe retaliation?”

Research suggests that girls who are more vulnerable offline (due to poverty or other forms of marginalization), are likely also more vulnerable to certain risks online, so we design with that in mind, she said. “When we started off on this project, our team members were experts in digital, but we had less experience with the safety and privacy aspects when it comes to girls living under $2/day or who were otherwise vulnerable. “Having additional guidance and developing a policy on this aspect has helped immensely – but has also slowed our processes down and sometimes made them more expensive,” she noted. “We had to go back to everything and add additional layers of security to make it as safe as possible for girls. We have also made sure to work very closely with our local partners to be sure that everyone involved in the project is aware of girls’ safety and security.”

Social media sites: Open, Closed, Private, Anonymous?

One issue that came up was safety for children and youth on social media networks. A Salon participant said his organization had thought about developing this type of a network several years back but decided in the end that the security risks outweighed the advantages. Participants discussed whether social media networks can ever be safe. One school of thought is that the more open a platform, the safer it is, as “there is no interaction in private spaces that cannot be constantly monitored or moderated.” Some worry about open sites, however, and set up smaller, closed, private groups that were closely monitored. “We work with victims of violence to share their stories and coping mechanisms, so, for us, private groups are a better option.”

Some suggested that anonymity on a social media site can protect girls and other vulnerable groups, however there is also research showing that Internet anonymity contributes to an increase in activities such as bullying and harassment. Some Salon participants felt that it was better to leverage existing platforms and try to use them safely. Others felt that there are no existing social media platforms that have enough security for girls or other vulnerable groups to use with appropriate levels of risk. “We sometimes recruit participants via existing social media platforms,” said one discussant, “but we move people off of those sites to our own more secure sites as soon as we can.”

Moderation and education on safety

Salon participants working with vulnerable populations said that they moderate their sites very closely and remove comments if users share personal information or use offensive language. “Some project budgets allow us to have a moderator check every 2 hours. For others, we sweep accounts once a day and remove offensive content within 24 hours.” One discussant uses moderation to educate the community. “We always post an explanation about why a comment was removed in order to educate the larger user base about appropriate ways to use the social network,” he said.

Close moderation becomes difficult and costly, however, as the user base grows and a platform scales. This means individual comments cannot be screened and pre-approved, because that would take too long and defeat the purpose of an engaging platform. “We need to acknowledge the very real tension between building a successful and engaging community and maintaining privacy and security,” said one Salon participant. “The more you lock it down and the more secure it is, the harder you find it is to create a real and active community.”

Another participant noted that they use their safe, closed youth platform to educate and reinforce messaging about what is safe and positive use of social media in hopes that young people will practice safe behaviors when they use other platforms. “We know that education and awareness raising can only go so far, however,” she said, “and we are not blind to that fact.” She expressed concern about risk for youth who speak out about political issues, because more and more governments are passing laws that punish critics and censor information. The organization, however, does not want to encourage youth to stop voicing opinions or participating politically.

Data breaches and project close-out

One Salon participant asked if organizations had examples of actual data breaches, and how they had handled them. Though no one shared examples, it was recommended that every organization have a contingency plan in place for accidental data leaks or a data breach or data hack. “You need to assume that you will get hacked,” said one person, “and develop your systems with that as a given.”

In addition to the day-to-day security issues, we need to think about project close-out, said one person. “Most development interventions are funded for a short, specific period of time. When a project finishes, you get a report, you do your M&E, and you move on. However, the data lives on, and the effects of the data live on. We really need to think more about budgeting for proper project wind-down and ensure that we are accountable beyond the lifetime of a project.”

Data security, anonymization, consent

Another question was related to using and keeping girls’ (and others’) data safe. “Consent to collect and use data on a website or via a mobile platform can be tricky, especially if we don’t know how to explain what we might do with the data,” said one Salon participant. Others suggested it would be better not to collect any data at all. “Why do we even need to collect this data? Who is it for?” he asked. Others countered that this data is often the only way to understand what people are doing on the site, to make adjustments and to measure impact.

One scenario was shared where several partner organizations discussed opening up a country’s cell phone data records to help contain a massive public health epidemic, but the privacy and security risks were too great, so the idea was scrapped. “Some said we could anonymize the data, but you can never really and truly anonymize data. It would have been useful to have a policy or a rubric that would have guided us in making that decision.”

Policy and Guidelines on Girls Privacy, Security and Safety

Policy guidelines related to aspects such as responsible data for NGOs, data security, privacy and other aspects of digital security in general do exist. (Here are some that we compiled along with some other resources). Most IT departments also have strict guidelines when it comes to donor data (in the case of credit card and account information, for example). This does not always cross over to program-level ICT or M&E efforts that involve the populations that NGOs are serving through their programming.

General awareness around digital security is increasing, in part due to recent major corporate data hacks (e.g., Target, Sony) and the Edward Snowden revelations from a few years back, but much more needs to be done to educate NGO staff and management on the type of privacy and security measures that need to be taken to protect the data and mitigate risk for those who participate in their programs.  There is an argument that NGOs should have specific digital privacy, safety and security policies that are tailored to their programming and that specifically focus on the types of digital risks that girls, women, children or other vulnerable people face when they are involved in humanitarian or development programs.

One such policy (focusing on vulnerable girls) and toolkit (its accompanying principles and values, guidelines, checklists and a risk matrix template); was shared at the Salon. (Disclosure: – This policy toolkit is one that I am working on. It should be ready to share in early 2016). The policy and toolkit take program implementers through a series of issues and questions to help them assess potential risks and tradeoffs in a particular context, and to document decisions and improve accountability. The toolkit covers:

  1. data privacy and security –using approaches like Privacy by Design, setting limits on the data that is collected, achieving meaningful consent.
  2. platform content and design –ensuring that content produced for girls or that girls produce or volunteer is not putting girls at risk.
  3. partnerships –vetting and managing partners who may be providing online/offline services or who may partner on an initiative and want access to data, monetizing of girls’ data.
  4. monitoring, evaluation, research and learning (MERL) – how will program implementers gather and store digital data when they are collecting it directly or through third parties for organizational MERL purposes.

Privacy, Security and Safety Implications

Our final discussant spoke about the implications of implementing the above-mentioned girls’ privacy, safety and security policy. He started out saying that the policy starts off with a manifesto: We will not compromise a girl in any way, nor will we opt for solutions that cut corners in terms of cost, process or time at the expense of her safety. “I love having this as part of our project manifesto, he said. “It’s really inspiring! On the flip side, however, it makes everything I do more difficult, time consuming and expensive!”

To demonstrate some of the trade-offs and decisions required when working with vulnerable girls, he gave examples of how the current project (implemented with girls’ privacy and security as a core principle) differed from that of a commercial social media platform and advertising campaign he had previously worked on (where the main concern was the reputation of the corporation, not that of the users of the platform and the potential risks they might put themselves in by using the platform).

Moderation

On the private sector platform, said the discussant, “we didn’t have the option of pre-moderating comments because of the budget and because we had 800 thousand users. To meet the campaign goals, it was more important for users to be engaged than to ensure content was safe. We focused on removing pornographic photos within 24 hours, using algorithms based on how much skin tone was in the photo.” In the fields of marketing and social media, it’s a fairly well-known issue that heavy-handed moderation kills platform engagement. “The more we educated and informed users about comment moderation, or removed comments, the deader the community became. The more draconian the moderation, the lower the engagement.”

The discussant had also worked on a platform for youth to discuss and learn about sexual health and practices, where he said that users responded angrily to moderators and comments that restricted their participation. “We did expose our participants to certain dangers, but we also knew that social digital platforms are more successful when they provide their users with sense of ownership and control. So we identified users that exhibited desirable behaviors and created a different tier of users who could take ownership (super users) to police and flag comments as inappropriate or temporarily banned users.” This allowed a 25% decrease in moderation. The organization discovered, however, that they had to be careful about how much power these super users had. “They ended up creating certain factions on the platform, and we then had to develop safeguards and additional mechanisms by which we moderated our super users!”

Direct Messages among users

In the private sector project example, engagement was measured by the number of direct or private messages sent between platform users. In the current scenario, however, said the discussant, “we have not allowed any direct messages between platform users because of the potential risks to girls of having places on the site that are hidden from moderators. So as you can see, we are removing some of our metrics by disallowing features because of risk. These activities are all things that would make the platform more engaging but there is a big fear that they could put girls at risk.”

Adopting a privacy, security, and safety policy

One discussant highlighted the importance of having privacy, safety and security policies before a project or program begins. “If you start thinking about it later on, you may have to go back and rebuild things from scratch because your security holes are in the design….” The way a database is set up to capture user data can make it difficult to query in the future or for users to have any control of what information is or is not being shared about them. “If you don’t set up the database with security and privacy in mind from the beginning, it might be impossible to make the platform safe for girls without starting from scratch all over again,” he said.

He also cautioned that when making more secure choices from the start, platform and tool development generally takes longer and costs more. It can be harder to budget because designers may not have experience with costing and developing the more secure options.

“A valuable lesson is that you have to make sure that what you’re trying to do in the first place is worth it if it’s going to be that expensive. It is worth a girls’ while to use a platform if she first has to wade through a 5-page terms and conditions on a small mobile phone screen? Are those terms and conditions even relevant to her personally or within her local context? Every click you ask a user to make will reduce their interest in reaching the platform. And if we don’t imagine that a girl will want to click through 5 screens of terms and conditions, the whole effort might not be worth it.” Clearly, aspects such as terms and conditions and consent processes need to be designed specifically to fit new contexts and new kinds of users.

Making responsible tradeoffs

The Girls Privacy, Security and Safety policy and toolkit shared at the Salon includes a risk matrix where project implementers rank the intensity and probability of risks as high, medium and low. Based on how a situation, feature or other potential aspect is ranked and the possibility to mitigate serious risks, decisions are made to proceed or not. There will always be areas with a certain level of risk to the user. The key is in making decisions and trade-offs that balance the level of risk with the potential benefits or rewards of the tool, service, or platform. The toolkit can also help project designers to imagine potential unintended consequences and mitigate risk related to them. The policy also offers a way to systematically and pro-actively consider potential risks, decide how to handle them, and document decisions so that organizations and project implementers are accountable to girls, peers and partners, and organizational leadership.

“We’ve started to change how we talk about user data in our organization,” said one discussant. “We have stopped thinking about it as something WE create and own, but more as something GIRLS own. Banks don’t own people’s money – they borrow it for a short time. We are trying to think about data that way in the conversations we’re having about data, funding, business models, proposals and partnerships. You don’t get to own your users’ data, we’re not going to share de-anonymized data with you. We’re seeing legislative data in some of the countries we work that are going that way also, so it’s good to be thinking about this now and getting prepared”

Take a look at our list of resources on the topic and add anything we may have missed!

 

Thanks to our friends at ThoughtWorks for hosting this Salon! If you’d like to join discussions like this one, sign up at Technology SalonSalons are held under Chatham House Rule, therefore no attribution has been made in this post.

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Screen Shot 2015-04-23 at 8.59.45 PMBy Mala Kumar and Linda Raftree

Our April 21st NYC Technology Salon focused on issues related to the LGBT ICT4D community, including how LGBTQI issues are addressed in the context of stakeholders and ICT4D staff. We examined specific concerns that ICT4D practitioners who identify as LGBTQI have, as well as how LGBTQI stakeholders are (or are not) incorporated into ICT4D projects, programs and policies. Among the many issues covered in the Salon, the role of the Internet and mobile devices for both community building and surveillance/security concerns played a central part in much of the discussion.

To frame the discussion, participants were asked to think about how LGBTQI issues within ICT4D (and more broadly, development) are akin to gender. Mainstreaming gender in development starts with how organizations treat their own staff. Implementing programs, projects and policies with a focus on gender cannot happen if the implementers do not first understand how to treat staff, colleagues and those closest to them (i.e. family, friends). Likewise, without a proper understanding of LGBTQI colleagues and staff, programs that address LGBTQI stakeholders will be ineffective.

The lead discussants of the Salon were Mala Kumar, writer and former UN ICT4D staff, Tania Lee, current IRC ICT4D Program Officer, and Robert Valadéz, current UN ICT4D staff. Linda Raftree moderated the discussion.

Unpacking LGBTQI

The first discussant pointed out how we as ICT4D/development practitioners think of the acronym LGBTQI, particularly the T and I – transgender and intersex. Often, development work focuses on the sexual identity portion of the acronym (the LGBQ), and not what is considered in Western countries as transgenderism.

As one participant said, the very label of “transgender” is hard to convey in many countries where “third gender” and “two-spirit gender” exist. These disagreements in terminology have – in Bangladesh and Nepal for example – resulted in creating conflict and division of interest within LGBTQI communities. In other countries, such as Thailand and parts of the Middle East, “transgenderism” can be considered more “normal” or societally acceptable than homosexuality. Across Africa, Latin America, North America and Europe, homosexuality is a better understood – albeit sometimes severely criminalized and socially rejected – concept than transgenderism.

One participant cited that in her previous first-hand work on services for lesbian, gay and bisexual people; often in North America, transgender communities are prioritized less in LGBTQI services. In many cases she saw in San Francisco, homeless youth would identify as anything in order to gain access to needed services. Only after the services were provided did the beneficiaries realize the consequences of self-reporting or incorrectly self-reporting.

Security concerns within Unpacking LGBTQI

For many people, the very notion of self-identifying as LGBTQI poses severe security risks. From a data collection standpoint, this results in large problems in accurate representation of populations. It also results in privacy concerns. As one discussant mentioned, development and ICT4D teams often do not have the technical capacity (i.e. statisticians, software engineers) to properly anonymize data and/or keep data on servers safe from hackers. On the other hand, the biggest threat to security may just be “your dad finding your phone and reading a text message,” as one person noted.

Being an LGBTQI staff in ICT4D

 Our second lead discussant spoke about being (and being perceived as) an LGBTQI staff member in ICT4D. She noted that many of the ICT4D hubs, labs, centers, etc. are in countries that are notoriously homophobic. Examples include Uganda (Kampala), Kenya (Nairobi), Nigeria (Abuja, Lagos), Kosovo and Ethiopia (Addis). This puts people who are interested in technology for development and are queer at a distinct disadvantage.

Some of the challenges she highlighted include that ICT4D attracts colleagues from around the world who are the most likely to be adept at computers and Internet usage, and therefore more likely to seek out and find information about other staff/colleagues online. If those who are searching are homophobic, finding “evidence” against colleagues can be both easy and easy to disseminate. Along those lines, ICT4D practitioners are encouraged (and sometimes necessitated) to blog, use social media, and keep an online presence. In fact, many people in ICT4D find posts and contracts this way. However, keeping online professional and personal presences completely separate is incredibly challenging. Since ICT4D practitioners are working with colleagues most likely to actually find colleagues online, queer ICT4D practitioners are presented with a unique dilemma.

ICT4D practitioners are arguably the set of people within development that are the best fitted to utilize technology and programmatic knowledge to self-advocate as LGBT staff and for LGBT stakeholder inclusion. However, how are queer ICT4D staff supposed to balance safety concerns and professional advancement limitations when dealing with homophobic staff? This issue is further compounded (especially in the UN, as one participant noted) by being awarded the commonly used project-based contracts, which give staff little to no job security, bargaining power or general protection when working overseas.

Security concerns within being an LGBTQI staff in ICT4D

A participant who works in North America for a Kenyan-based company said that none of her colleagues ever mentioned her orientation, even though they must have found her publicly viewable blog on gender and she is not able to easily disguise her orientation. She talked about always finding and connecting to the local queer community wherever she goes, often through the Internet, and tries to support local organizations working on LGBT issues. Still, she and several other participants and discussants emphasized their need to segment online personal and professional lives to remain safe.

Another participant mentioned his time working in Ethiopia. The staff from the center he worked with made openly hostile remarks about gays, which reinforced his need to stay closeted. He noticed that the ICT staff of the organization made a concerted effort to research people online, and that Facebook made it difficult, if not impossible, to keep personal and private lives separate.

Another person reiterated this point by saying that as a gay Latino man, and the first person in his family to go to university, grad school and work in a professional job, he is a role model to many people in his community. He wants to offer guidance and support, and used to do so with a public online presence. However, at his current internationally-focused job he feels the need to self-censor and has effectively limited talking about his public online presence, because he often interacts with high level officials who are hostile towards the LGBTQI community.

One discussant also echoed this idea, saying that she is becoming a voice for the queer South Asian community, which is important because much of LGBT media is very white. The tradeoff for becoming this voice is compromising her career in the field because she cannot accept a lot of posts because they do not offer adequate support and security.

Intersectionality

Several participants and discussants offered their own experiences on the various levels of hostility and danger involved with even being suspected as gay. One (female) participant began a relationship with a woman while working in a very conservative country, and recalled being terrified at being killed over the relationship. Local colleagues began to suspect, and eventually physically intervened by showing up at her house. This participant cited her “light skinned privilege” as one reason that she did not suffer serious consequences from her actions.

Another participant recounted his time with the US Peace Corps. After a year, he started coming out and dating people in host country. When one relationship went awry and he was turned into the police for being gay, nothing came of the charges. Meanwhile, he saw local gay men being thrown into – and sometimes dying in – jail for the same charges. He and some other participants noted their relative privilege in these situations because they are white. This participant said he felt that as a white male, he felt a sense of invincibility.

In contrast, a participant from an African country described his experience growing up and using ICTs as an escape because any physical indication he was gay would have landed him in jail, or worse. He had to learn how to change his mannerisms to be more masculine, had to learn how to disengage from social situations in real life, and live in the shadows.

One of the discussants echoed these concerns, saying that as a queer woman of color, everything is compounded. She was recruited for a position at a UN Agency in Kenya, but turned the post down because of the hostility towards gays and lesbians there. However, she noted that some queer people she has met – all white men from the States or Europe – have had overall positive experiences being gay with the UN.

Perceived as predators

One person brought up the “predator” stereotype often associated with gay men. He and his partner have had to turn down media opportunities where they could have served as role models for the gay community, especially poor, gay queer men of color, (who are one of the most difficult socioeconomic classes to reach) out of fear that this stereotype may impact on their being hired to work in organizations that serve children.

Monitoring and baiting by the government

One participant who grew up in Cameroon mentioned that queer communities in his country use the Internet cautiously, even though it’s the best resource to find other queer people. The reason for the caution is that government officials have been known to pose as queer people to bait real users for illegal gay activity.

Several other participants cited this same phenomenon in different forms. A recent article talked about Egypt using new online surveillance tactics to find LGBTQI people. Some believe that this type of surveillance will also happen in Nigeria, a notoriously hostile country towards LGBTQI persons and other places.

There was also discussion about what IP or technology is the safest for LGBTQI people. While the Internet can be monitored and traced back to a specific user, being able to connect from multiple access points and with varying levels of security creates a sense of anonymity that phones cannot provide. A person also generally carries phones, so if the government intercepts a message on either the originating or receiving device, implications of existing messages are immediate unless a user can convince the government the device was stolen or used by someone else. In contrast, phones are more easily disposable and in several countries do not require registration (or a registered SIM card) to a specific person.

In Ethiopia, the government has control over the phone networks and can in theory monitor these messages for LGBTQI activity. This poses a particular threat since there is already legal precedent for convictions of illegal activity based on text messages. In some countries, major telecom carriers are owned by a national government. In others, major telecom carries are national subsidiaries of an international company.

Another major concern raised relates back to privacy. Many major international development organizations do not have the capacity or ability to retain necessary software engineers, ICT architects and system operators, statisticians and other technology people to properly prevent Internet hacks and surveillance. In some cases, this work is illegal by national government policy, and thus also requires legal advocacy. The mere collection of data and information can therefore pose a security threat to staff and stakeholders – LGBTQI and allies, alike.

The “queer divide”

One discussant asked the group for data or anecdotal information related to the “queer divide.” A commonly understood problem in ICT4D work are divides – between genders, urban and rural, rich and poor, socially accepted and socially marginalized. There have also been studies to clearly demonstrate that people who are naturally extroverted and not shy benefit more from any given program or project. As such, is there any data to support a “queer divide” between those who are LGBTQI and those who are not, he wondered. As demonstrated in the above sections, many queer people are forced to disengage socially and retreat from “normal” society to stay safe.

Success stories, key organizations and resources

Participants mentioned organizations and examples of more progressive policies for LGBTQI staff and stakeholders (this list is not comprehensive, nor does it suggest these organizations’ policies are foolproof), including:

We also compiled a much more extensive list of resources on the topic here as background reading, including organizations, articles and research. (Feel free to add to it!)

What can we do moving forward?

  • Engage relevant organizations, such as Out in Tech and Lesbians who Tech, with specific solutions, such as coding privacy protocols for online communities and helping grassroots organizations target ads to relevant stakeholders.
  • Lobby smartphone manufacturers to increase privacy protections on mobile devices.
  • Lobby US and other national governments to introduce “Right to be forgotten” law, which allows Internet users to wipe all records of themselves and personal activity.
  • Support organizations and services that offer legal council to those in need.
  • Demand better and more comprehensive protection for LGBTQI staff, consultants and interns in international organizations.

Key questions to work on…

  • In some countries, a government owns telecom companies. In others, telecom companies are national subsidiaries of international corporations. In countries in which the government is actively or planning on actively surveying networks for LGBTQI activity, how does the type of telecom company factor in?
  • What datasets do we need on LGBTQI people for better programming?
  • How do we properly anonymize data collected? What are the standards of best practices?
  • What policies need to be in place to better protect LGBTQI staff, consultants and interns? What kind of sensitizing activities, trainings and programming need to be done for local staff and less LGBTQI sensitive international staff in ICT4D organizations?
  • How much capacity have ICT4D/international organizations lost as a result of their policies for LGBTQI staff and stakeholders?
  • What are the roles and obligations of ICT4D/international organizations to their LGBTQI staff, now and in the future?
  • What are the ICT4D and international development programmatic links with LGBT stakeholders and staff? How does LGBT stakeholders intersect with water? Public health? Nutrition? Food security? Governance and transparency? Human rights? Humanitarian crises? How does LGBT staff intersect with capacity? Trainings? Programming?
  • How do we safely and responsibility increase visibility of LGBTQI people around the world?
  • How do we engage tech companies that are pro-LGBTQI, including Google, to do more for those who cannot or do not engage with their services?
  • What are the economic costs of homophobia, and does this provide a compelling enough case for countries to stop systemic LGBTQI-phobic behavior?
  • How do we mainstream LGBTQI issues in bigger development conferences and discussions?

Thanks to the great folks at ThoughtWorks for hosting and providing a lovely breakfast to us! Technology Salons are carried out under Chatham House Rule, so no attribution has been made. If you’d like to join us for Technology Salons in future, sign up here!

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It’s been two weeks since we closed out the M&E Tech Conference in DC and the Deep Dive in NYC. For those of you who missed it or who want to see a quick summary of what happened, here are some of the best tweets from the sessions.

We’re compiling blog posts and related documentation and will be sharing more detailed summaries soon. In the meantime, enjoy a snapshot!

https://twitter.com/neuguy/status/515134807672909826

https://twitter.com/dalgoso/status/515136050793291776

https://twitter.com/neuguy/status/515166952378343425

https://twitter.com/neuguy/status/515184242595487744

https://twitter.com/schmutzie/status/515215243388014592

https://twitter.com/prefontaine/status/515222154670252032

https://twitter.com/richmanmax/status/515576201084411904

https://twitter.com/sandhya_c_rao/status/516343304448131072

https://twitter.com/dalgoso/status/519879358370955264

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