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Archive for the ‘wait… what?’ Category

In the search for evidence of impact, donors and investors are asking that more and more data be generated by grantees and those they serve. Some of those driving this conversation talk about the “opportunity cost” of not collecting, opening and sharing as much data as possible. Yet we need to also talk about the real and tangible risks of data collecting and sharing and the long-term impacts of reduced data privacy and security rights, especially for the vulnerable individuals and groups with whom we work.

This week I’m at the Global Philanthropy Forum Conference in the heart of Silicon Valley speaking on a panel titled “Civil Liberties and Data Philanthropy: When NOT to Ask for More.” It’s often donor requests for innovation or for proof of impact that push implementors to collect more and more data. So donors and investors have a critical role to play in encouraging greater respect and protection of the data of vulnerable individuals and groups. Philanthropists, grantees, and investees can all help to reduce these risks by bringing a values-based responsible data approach to their work.

Here are three suggestions for philanthropists on how to contribute to more responsible data management:

1) Enhance your own awareness and expertise on the potential benefits and harms associated with data. 

  • Adopt processes that take a closer look at the possible risks and harms of collecting and holding data and how to mitigate them. Ensure those aspects are reviewed and considered during investments and grant making.
  • Conduct risk-benefits-harms assessments early in the program design and/or grant decision-making processes. This type of assessment helps lay out the benefits of collecting and using data, identifies the data-related harms we might we be enabling, and asks us to determine how we are intentionally mitigating harm during the design of our data collection, use and sharing. Importantly, this process also asks us to also identify who is benefiting from data collection and who is taking on the burden of risk. It then aims to assess whether the benefits of having data outweigh the potential harms. Risks-benefits-harms assessments also help us to ensure we are doing a contextual assessment, which is important because every situation is different. When these assessments are done in a participatory way, they tend to be even more useful and accurate ways to reduce risks in data collection and management.
  • Hire people within your teams who can help provide technical support to grantees when needed in a friendly — not a punitive — way. Building in a ‘data responsibility by design’ approach can help with that. We need to think about the role of data during the early stages of design. What data is collected? Why? How? By and from whom? What are the potential benefits, risks, and harms of gathering, holding, using and sharing that data? How can we reduce the amount of data that we collect and mitigate potential harms?
  • Be careful with data on your grantees. If you are working with organizations who (because of the nature of their mission) are at risk themselves, it’s imperative that you protect their privacy and don’t expose them to harm by collecting too much data from them or about them. Here’s a good guide for human rights donors on protecting sensitive data.

2) Use your power and influence to encourage grantees and investees to handle data more responsibly. If donors are going to push for more data collection, they should also be signaling to grantees and investees that responsible data management matters and encouraging them to think about it in proposals and more broadly in their work.

  • Strengthen grantee capacity as part of the process of raising data management standards. Lower-resourced organizations may not be able to meet higher data privacy requirements, so donors should think about how they can support rather than exclude smaller organizations with less capacity as we all work together to raise data management standards.
  • Invest holistically in both grants and grantees. This starts by understanding grantees’ operational, resource, and technical constraints as well as the real security risks posed to grantee staff, data collectors, and data subjects. For this to work, donors need to create genuinely safe spaces for grantees to voice their concerns and discuss constraints that may limit their ability to safely collect the data that donors are demanding.
  • Invest in grantees’ IT and other systems and provide operational funds that enable these systems to work. There is never enough funding for IT systems, and this puts the data of vulnerable people and groups at risk. One reason that organizations struggle to fund systems and improve data management is because they can’t bill overhead. Perverse incentives prevent investments in responsible data. Donors can work through this and help find solutions.
  • Don’t punish organizations that include budget for better data use, protection and security in their proposals. It takes money and staff and systems to manage data in secure ways. Yet stories abound in the sector about proposals that include these elements being rejected because they turn out to be more expensive. It’s critical to remember that safeguarding of all kinds takes resources!
  • Find out what kind of technical or systems support grantees/investees need to better uphold ethical data use and protection and explore ways that you can provide additional funds and resources to strengthen this area in those grantees and across the wider sector.
  • Remember that we are talking about long-term organizational behavior change. It is urgent to get moving on improving how we all handle data — but this will take some time. It’s not a quick fix because the skills are in short supply and high demand right now as a result of the GDPR and related laws that are emerging in other countries around the world.
  • Don’t ask grantees to collect data that might make vulnerable individuals or groups wary of them. Data is an extension of an individual. Trust in how an organization collects and manages an individual’s data leads to trust in an organization itself. Organizations need to be trusted in order to do our work, and collection of highly sensitive data, misuse of data or a data breach can really break that trust compact and reduce an organization’s impact.

3) Think about the responsibility you have for what you do, what you fund, and the type of society that we live in. Support awareness and compliance with new regulations and legislation that can protect privacy. Don’t use “innovation” as an excuse for putting historically marginalized individuals and groups at risk or for allowing our societies to advance in ways that only benefit the wealthiest. Question the current pathway of the “Fourth Industrial Revolution” and where it may take us.

I’m sure I’m leaving out some things. What do you think donors and the wider philanthropic community can do to enhance responsible data management and digital safeguarding?

 

 

 

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The recently announced World Food Programme (WFP) partnership with Palantir, IRIN’s article about it, reactions from the Responsible Data Forum, and WFP’s resulting statement inspired us to pull together a Technology Salon in New York City to discuss the ethics of humanitarian data sharing.

(See this crowdsourced document for more background on the WFP-Palantir partnership and resources for thinking about the ethics of data sharing. Also here is an overview of WFP’s SCOPE system for beneficiary identification, management and tracking.)

Our lead discussants were: Laura Walker McDonald, Global Alliance for Humanitarian Innovation; Mark Latonero, Research Lead for Data & Human Rights, Data & Society; Nathaniel Raymond, Jackson Institute of Global Affairs, Yale University; and Kareem Elbayar, Partnerships Manager, Centre for Humanitarian Data at the United Nations Office for the Coordination of Humanitarian Affairs. We were graciously hosted by The Gov Lab.

What are the concerns about humanitarian data sharing and with Palantir?

Some of the initial concerns expressed by Salon participants about humanitarian data sharing included: data privacy and the permanence of data; biases in data leading to unwarranted conclusions and assumptions; loss of stakeholder engagement when humanitarians move to big data and techno-centric approaches; low awareness and poor practices across humanitarian organizations on data privacy and security; tensions between security of data and utility of data; validity and reliability of data; lack of clarity about the true purposes of data sharing; the practice of ‘ethics outsourcing’ (testing things in places where there is a perceived ‘lower ethical standard;’ and less accountability); use of humanitarian data to target and harm aid recipients; disempowerment and extractive approaches to data; lack of checks and balances for safe and productive data sharing; difficulty of securing meaningful consent; and the links between data and surveillance by malicious actors, governments, private sector, military or intelligence agencies.

Palantir’s relationships and work with police, the CIA, ICE, the NSA, the US military and wider intelligence community are one of the main concerns about this partnership. Some ask whether a company can legitimately serve philanthropy, development, social, human rights and humanitarian sectors while also serving the military and intelligence communities and whether it is ethical for those in the former to engage in partnerships with companies who serve the latter. Others ask if WFP and others who partner with Palantir are fully aware of the company’s background, and if so, why these partnerships have been able to pass through due diligence processes. Yet others wonder if a company like Palantir can be trusted, given its background.

Below is a summary of the key points of the discussion, which happened on February 28, 2019. (Technology Salons are Chatham House affairs, so I have not attributed quotes in this post.)

Why were we surprised by this partnership/type of partnership?

Our first discussant asked why this partnership was a surprise to many. He emphasized the importance of stakeholder conversations, transparency, and wider engagement in the lead-up to these kinds of partnerships. “And I don’t mean in order to warm critics up to the idea, but rather to create a safe and trusted ecosystem. Feedback and accountability are really key to this.” He also highlighted that humanitarian organizations are not experts in advanced technologies and that it’s normal for them to bring in experts in areas that are not their forte. However, we need to remember that tech companies are not experts in humanitarian work and put the proper checks and balances in place. Bringing in a range of multidisciplinary expertise and distributed intelligence is necessary in a complex information environment. One possible approach is creating technology advisory boards. Another way to ensure more transparency and accountability is to conduct a human rights impact assessment. The next year will be a major test for these kinds of partnerships, given the growing concerns, he said.

One Salon participant said that the fact that the humanitarian sector engages in partnerships with the private sector is not a surprise at all, as the sector has worked through Public-Private Partnerships (PPPs) for several years now and they can bring huge value. The surprise is that WFP chose Palantir as the partner. “They are not the only option, so why pick them?” Another person shared that the WFP partnership went through a full legal review, and so it was not a surprise to everyone. However, communication around the partnership was not well planned or thought out and the process was not transparent and open. Others pointed out that although a legal review covers some bases, it does not assess the potential negative social impact or risk to ‘beneficiaries.’ For some the biggest surprise was WFP’s own surprise at the pushback on this particular partnership and its unsatisfactory reaction to the concerns raised about it. The response from responsible data advocates and the press attention to the WFP-Palantir partnership might be a turning point for the sector to encourage more awareness of the risks in working with certain types of companies. As many noted, this is not only a problem for WFP, it’s something that plagues the wider sector and needs to be addressed urgently.

Organizations need think beyond reputational harm and consider harm to beneficiaries

“We spend too much time focusing on avoiding risk to institutions and too little time thinking about how to mitigate risk to beneficiaries,” said one person. WFP, for example, has some of the best policies and procedures out there, yet this partnership still passed their internal test. That is a scary thought, because it implies that other agencies who have weaker policies might be agreeing to even more risky partnerships. Are these policies and risk assessments, then, covering all the different types of risk that need consideration? Many at the Salon felt that due diligence and partnership policies focus almost exclusively on organizational and reputational risk with very little attention to the risk that vulnerable populations might face. It’s not just a question of having policies, however, said one person. “Look at the Oxfam Safeguarding situation. Oxfam had some of the best safeguarding policies, yet there were egregious violations that were not addressed by having a policy. It’s a question of power and how decisions get made, and where decision-making power lies and who is involved and listened to.” (Note: one person contacted me pre-Salon to say that there was pushback by WFP country-level representatives about the Palantir partnership, but that it still went ahead. This brings up the same issue of decision-making power, and who has power to decide on these partnerships and why are voices from the frontlines not being heard? Additionally, are those whose data is captured and put into these large data systems ever consulted about what they think?)

Organizations need to assess wider implications, risks, and unintended negative consequences

It’s not only WFP that is putting information into SCOPE, said one person. “Food insecure people have no choice about whether to provide their data if they wish to receive food.” Thus, the question of truly ‘informed consent’ arises. Implementing partners don’t have a lot of choice either, he said. “Implementing agencies are forced to input beneficiary data into SCOPE if they want to work in particular zones or countries.” This means that WFP’s systems and partnerships have an impact on the entire humanitarian community, and therefore these partnerships and systems need to be more broadly consulted about with the wider sector.  The optical and reputational impact to organizations aside from WFP is significant, as they may disagree with the Palantir partnership but they are now associated with it by default. This type of harm goes beyond the fear of exploitation of the data in WFP’s “data lake.” It becomes a risk to personnel on the ground who are then seen as collaborating with a CIA contractor by putting beneficiary biometric data into SCOPE. This can also deter food-insecure people from accessing benefits. Additionally, association with CIA or US military has led to humanitarian agencies and workers being targeted, attacked and killed. That is all in addition to the question on whether these kinds of partnerships violate humanitarian principles, such as that of impartiality.

“It’s critical to understand the role of rumor in humanitarian contexts,” said one discussant. “Affected populations are trying to figure out what is happening and there is often a lot of rumor going around.”  So, if Palantir has a reputation for giving data to the CIA, people may hear about that and then be afraid to access services for fear of having their data given to the CIA. This can lead to retaliation against humanitarians and humanitarian organizations and escalate their risk of operating. Risk assessments need to go beyond the typical areas of reputation or financial risk. We also need to think about how these partnerships can affect humanitarian access and community trust and how rumors can have wide ripple effects.

The whole sector needs to put better due diligence systems in place. As it is now, noted one person, often it’s someone who doesn’t know much about data who writes up a short summary of the partnership, and there is limited review. “We’ve been struggling for 10 years to get our offices to use data. Now we’re in a situation where they’re just picking up a bunch of data and handing it over to private companies.”

UN immunities and privileges lead to a lack of accountability

The fact that UN agencies have immunities and privileges, means that laws such as the EU’s General Data Protection Regulation (GDPR) do not apply to them and they are left to self-regulate. Additionally, there is no common agreement among UN Agencies on how GDPR applies, and each UN agency interprets it on their own. As one person noted “There is a troubling sense of exceptionalism and lack of accountability in some of these agencies because ‘a beneficiary cannot take me to court.’” An interesting point, however, is that while UN agencies are immune, those contracted as their data processors are not immune — so data processors beware!

Demographically Identifiable Information (DII) can lead to serious group harm

The WFP has stated that personally identifiable information (PII) is not technically accessible to Palantir via this partnership. However, some at the Salon consider that the WFP failed in their statement about the partnership when they used the absence of PII as a defense. Demographically Identifiable Information (DII) and the activity patterns that are visible even in commodity data can be extrapolated as training data for future data modeling. “This is prospective modeling of action-based intelligence patterns as part of multiple screeners of intel,” said one discussant. He went on to explain that privacy discussions have moved from centering on property rights in the 19th Century, to individual rights in the 20th Century, to group rights in the 21st Century. We can use existing laws to emphasize protection of groups and to highlight the risks of DII leading to group harm, he said, as there are well-known cases that exemplify the notion of group harms (Plessy v Ferguson, Brown v Board of Education). Even in logistics data (which is the kind of data that WFP says Palantir will access) that contains no PII, it’s very simple to identify groups. “I can look at supply chain information and tell you where there are lactating mothers. If you don’t want refugees to give birth in the country they have arrived to, this information can be used for targeting.”

Many in the sector do not trust a company like Palantir

Though it is not clear who was in the room when WFP made the decision to partner with Palantir, the overall sector has concerns that the people making these decisions are not assessing partnerships from all angles: legal, privacy, programmatic, ethical, data use and management, social, protection, etc. Technologists and humanitarian practitioners are often not included in making these decisions, said one participant. “It’s the people with MBAs. They trust a tech company to say ‘this is secure’ but they don’t have the expertise to actually know that. Not to mention that yes, something might be secure, but maybe it’s not ethical. Senior people are signing off without having a full view. We need a range of skill sets reviewing these kinds of partnerships and investments.”

Another question arises: What happens when there is scope creep? Is Palantir in essence “grooming” the sector to then abuse data it accesses once it’s trusted and “allowed in”? Others pointed out that the grooming has already happened and Palantir is already on the inside. They first began partnering with the sector via the Clinton Global Initiative meetings back in 2013 and they are very active at World Economic Forum meetings. “This is not something coming out of the Trump administration, it was happening long before that,” said one person, and the company is already “in.” Another person said “Palantir lobbied their way into this, and they’ve gotten past the point of reputational challenge.” Palantir has approached many humanitarian agencies, including all the UN agencies, added a third person. Now that they have secured this contract with the WFP, the door to future work with a lot of other agencies is open and this is very concerning.

We’re in a new political economy: data brokerage.

“Humanitarians have lost their Geneva values and embraced Silicon Valley values” said one discussant. They are becoming data brokers within a colonial data paradigm. “We are making decisions in hierarchies of power, often extralegally,” he said. “We make decisions about other people’s data without their involvement, and we need to be asking: is it humanitarian to commodify for monetary or reasons of value the data of beneficiaries? When is it ethical to trade beneficiary data for something of value?” Another raised the issue of incentives. “Where are the incentives stacked? There is no incentive to treat beneficiaries better. All the incentives are on efficiency and scale and attracting donors.”

Can this example push the wider sector to do better?

One participant hoped there could be a net gain out of the WFP-Palantir case. “It’s a bad situation. But it’s a reckoning for the whole space. Most agencies don’t have these checks and balances in place. But people are waking up to it in a serious way. There’s an opportunity to step into. It’s hard inside of bureaucratic organizations, but it’s definitely an opportunity to start doing better.”

Another said that we need more transparency across the sector on these partnerships. “What is our process for evaluating something like this? Let’s just be transparent. We need to get these data partnership policies into the open. WFP could have simply said ‘here is our process’. But they didn’t. We should be working with an open and transparent model.” Overall, there is a serious lack of clarity on what data sharing agreements look like across the sector. One person attending the Salon said that their organization has been trying to understand current practice with regard to data sharing, and it’s been very difficult to get any examples, even redacted ones.

What needs to happen? 

In closing we discussed what needs to happen next. One person noted that in her research on Responsible Data, she found a total lack of capacity in terms of technology at non-profit organizations. “It’s the Economist Syndrome. Someone’s boss reads something on the bus and decides they need a blockchain,” someone quipped. In terms of responsible data approaches, research shows that organizations are completely overwhelmed. “They are keeping silent about their low capacity out of fear they will face consequences,” said one person, “and with GDPR, even more so”. At the wider level, we are still focusing on PII as the issue without considering DII and group rights, and this is a mistake, said another.

Organizations have very low capacity, and we are siloed. “Program officers do not have tech capacity. Tech people are kept in offices or ‘labs’ on their own and there is not a lot of porosity. We need protection advisors, lawyers, digital safety advisors, data protection officers, information management specialists, IT all around the table for this,” noted one discussant. Also, she said, though we do need principles and standards, it’s important that organizations adapt these so that they are their own principles and standards. “We need to adapt these boiler plate standards to our organizations. This has to happen based on our own organizational values.  Not everyone is rights-based, not everyone is humanitarian.” So organizations need to take the time to review and adapt standards, policies and procedures to their own vision and mission and to their own situations, contexts and operations and to generate awareness and buy-in. In conclusion, she said, “if you are not being responsible with data, you are already violating your existing values and codes. Responsible Data is already in your values, it’s a question of living it.”

Technology Salons happen in several cities around the world. If you’d like to join a discussion, sign up here. If you’d like to host a Salon, suggest a topic, or support us to keep doing Salons in NYC please get in touch with me! 🙂

 

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Karen Palmer is a digital filmmaker and storyteller from London who’s doing a dual residence at ThoughtWorks in Manhattan and TED New York to further develop a project called RIOT, described as an ‘emotionally responsive, live-action film with 3D sound.’ The film uses artificial intelligence, machine learning, various biometric readings, and facial recognition to take a person through a personalized journey during dangerous riot.

Karen Palmer, the future of immersive filmmaking, Future of Storytelling (FoST) 

Karen describes RIOT as ‘bespoke film that reflects your reality.’ As you watch the film, the film is also watching you and adapting to your experience of viewing it. Using a series of biometric readings (the team is experimenting with eye tracking, facial recognition, gait analysis, infrared to capture body temperature, and an emerging technology that tracks heart rate by monitoring the capillaries under a person’s eyes) the film shifts and changes. The biometrics and AI create a “choose your own adventure” type of immersive film experience, except that the choice is made by your body’s reactions to different scenarios. A unique aspect of Karen’s work is that the viewer doesn’t need to wear any type of gear for the experience. The idea is to make RIOT as seamless and immersive as possible. Read more about Karen’s ideas and how the film is shaping up in this Fast Company article and follow along with the project on the RIOT project blog.

When we talked about her project, the first thing I thought of was “The Feelies” in Aldous Huxley’s 1932 classic ‘Brave New World.’ Yet the feelies were pure escapism, and Karen’s work aims to draw people in to a challenging experience where they face their own emotions.

On Friday, December 15, I had the opportunity to facilitate a Salon discussion with a number of people from related disciplines who are intrigued by RIOT and the various boundaries it tests and explores. We had perspectives from people working in the areas of digital storytelling and narrative, surveillance and activism, media and entertainment, emotional intelligence, digital and immersive theater, brand experience, 3D sound and immersive audio, agency and representation, conflict mediation and non-state actors, film, artificial intelligence, and interactive design.

Karen has been busy over the past month as interest in the project begins to swell. In mid-November, at Montreal’s Phi Centre’s Lucid Realities exhibit, she spoke about how digital storytelling is involving more and more of our senses, bringing an extra layer of power to the experience. This means that artists and creatives have an added layer of responsibility. (Research suggests, for example, that the brain has trouble deciphering between virtual reality [VR] and actual reality, and children under the age of 8 have had problems differentiating between a VR experience and actual memory.)

At a recent TED Talk, Karen described the essence of her work as creating experiences where the participant becomes aware of how their emotions affect the narrative of the film while they are in it, and this helps them to see how their emotions affect the narrative of their life. Can this help to create new neural pathways in the brain, she asks. Can it help a person to see how their own emotions are impacting on them but also how others are reading their emotions and reacting to those emotions in real life?

Race and sexuality are at the forefront in the US – and the Trump elections further heightened the tensions. Karen believes it’s ever more important to explore different perspectives and fears in the current context where the potential for unrest is growing. Karen hopes that RIOT can be ‘your own personal riot training tool – a way to become aware of your own reactions and of moving through your fear.’

Core themes that we discussed on Friday include:

How can we harness the power of emotion? Despite our lives being emotionally hyper-charged, (especially right now in the US), we keep using facts and data to try to change hearts and minds. This approach is ineffective. In addition, people are less trusting of third-party sources because of the onslaught of misinformation, disinformation and false information. Can we use storytelling to help us get through this period? Can immersive storytelling and creative use of 3D sound help us to trust more, to engage and to witness? Can it help us to think about how we might react during certain events, like police violence? (See Tahera Aziz’ project [re]locate about the murder of Stephen Lawrence in South London in 1993). Can it help us to better understand various perspectives? The final version of RIOT aims to bring in footage from several angles, such as CCTV from a looted store, a police body cam, and someone’s mobile phone footage shot as they ran past, in an effort to show an array of perspectives that would help viewers see things in different lights.

How do we catch the questions that RIOT stirs up in people’s minds? As someone experiences RIOT, they will have all sorts of emotions and thoughts, and these will depend on a their identity and lived experiences. At one showing of RIOT, a young white boy said he learned that if he’s feeling scared he should try to stay calm. He also said that when the cop yelled at him in the film, he assumed that he must have done something wrong. A black teenager might have had an entirely different reaction to the police. RIOT is bringing in scent, haze, 3D sound, and other elements which have started to affect people more profoundly. Some have been moved to tears or said that the film triggered anger and other strong emotions for them.

Does the artist have a responsibility to accompany people through the full emotional experience? In traditional VR experiences, a person waits in line, puts on a VR headset, experiences something profound (and potentially something triggering), then takes off the headset and is rushed out so that the next person can try it. Creators of these new and immersive media experiences are just now becoming fully aware of how to manage the emotional side of the experiences and they don’t yet have a good handle on what their responsibilities are toward those who are going through them. How do we debrief people afterwards? How do we give them space to process what has been triggered? How do we bring people into the co-creation process so that we better understand what it means to tell or experience these stories? The Columbia Digital Storytelling Lab is working on gaining a better understanding of all this and the impact it can have on people.

How do we create the grammar and frameworks for talking about this? The technologies and tactics for this type of digital immersive storytelling are entirely new and untested. Creators are only now becoming more aware of the consequences of the experiences that they are creating ‘What am I making? Why? How will people go through it? How will they leave? What are the structures and how do I make it safe for them?’ The artist can open someone up to an intense experience, but then they are often just ushered out, reeling, and someone else is rushed in. It’s critical to build time for debriefing into the experience and to have some capacity for managing the emotions and reactions that could be triggered.

SAFE Lab, for example, works with students and the community in Chicago, Harlem, and Brooklyn on youth-driven solutions to de-escalation of violence. The project development starts with the human experience and the tech comes in later. Youth are part of the solution space, but along the way they learn hard and soft skills related to emerging tech. The Lab is testing a debriefing process also. The challenge is that this is a new space for everyone; and creation, testing and documentation are happening simultaneously. Rather than just thinking about a ‘user journey,’ creators need to think about the emotionality of the full experience. This means that as opposed to just doing an immersive film – neuroscience, sociology, behavioral psychology, and lots of other fields and research are included in the dialogue. It’s a convergence of industries and sectors.

What about algorithmic bias? It’s not possible to create an unbiased algorithm, because humans all have bias. Even if you could create an unbiased algorithm, as soon as you started inputting human information into it, it would become biased. Also, as algorithms become more complex, it becomes more and more difficult to understand how they arrive to decisions. This results in black boxes that are putting out decisions that even the humans that build them can’t understand. The RIOT team is working with Dr. Hongying Meng of Brunel University London, an expert in the creation of facial and emotion detection algorithms, to develop an open source algorithm for RIOT. Even if the algorithm itself isn’t neutral, the process by which it computes will be transparent.

Most algorithms are not open. Because the majority of private companies have financial goals rather than social goals in using or creating algorithms, they have little incentive for being transparent about how an algorithm works or what biases are inherent. Ad agencies want to track how a customer reacts to a product. Facebook wants to generate more ad revenue so it adjusts what news you see on your feed. The justice system wants to save money and time by using sentencing algorithms. Yet the biases in their algorithms can cause serious harm in multiple ways. (See this 2016 report from ProPublica). The problem with these commercial algorithms is that they are opaque and the biases in them are not shared. This lack of transparency is considered by some to be more problematic than the bias itself.

Should there be a greater push for regulation of algorithms? People who work in surveillance are often ignored because they are perceived as paranoid. Yet fears that AI will be totally controlled by the military, the private sector and tech companies in ways that are hidden and opaque are real and it’s imperative to find ways to bring the actual dangers home to people. This could be partly accomplished through narrative and stories. (See John Oliver’s interview with Edward Snowden) Could artists create projects that drive conversations around algorithmic bias, help the public see the risks, and push for greater regulation? (Also of note: the New York City government recently announced that it will start a task force to look more deeply into algorithmic bias).

How is the RIOT team developing its emotion recognition algorithm? The RIOT team is collecting data to feed into the algorithm by capturing facial emotions and labeling them. The challenge is that one person may think someone looks calm, scared, or angry and another person may read it a different way. They are also testing self-reported emotions to reduce bias. The purpose of the RIOT facial detection algorithm is to measure what the person is actually feeling and how others perceive that the person is feeling. For example, how would a police officer read your face? How would a fellow protester see you? The team is developing the algorithm with the specific bias that is needed for the narrative itself. The process will be documented in a peer-reviewed research paper that considers these issues from the angle of state control of citizens. Other angles to explore would be how algorithms and biometrics are used by societies of control and/or by non-state actors such as militia in the Middle East or by right wing and/or white supremacist groups in the US. (See this article on facial recognition tools being used to identify sexual orientation)

Stay tuned to hear more…. We’ll be meeting again in the new year to go more in-depth on topics such as responsibly guiding people through VR experiences; exploring potential unintended consequences of these technologies and experiences, especially for certain racial groups; commercial applications for sensory storytelling and elements of scale; global applications of these technologies; practical development and testing of algorithms; prototyping, ideation and foundational knowledge for algorithm development.

Garry Haywood of Kinicho from also wrote his thoughts up from the day.

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On November 14 Technology Salon NYC met to discuss issues related to the role of film and video in development and humanitarian work. Our lead discussants were Ambika Samarthya from Praekelt.org; Lina Srivastava of CIEL, and Rebekah Stutzman, from Digital Green’s DC office.

How does film support aid and development work?

Lina proposed that there are three main reasons for using video, film, and/or immersive media (such as virtual reality or augmented reality) in humanitarian and development work:

  • Raising awareness about an issue or a brand and serving as an entry point or a way to frame further actions.
  • Community-led discussion/participatory media, where people take agency and ownership and express themselves through media.
  • Catalyzing movements themselves, where film, video, and other visual arts are used to feed social movements.

Each of the above is aimed at a different audience. “Raising awareness” often only scratches the surface of an issue and can have limited impact if done on its own without additional actions. Community-led efforts tend to go deeper and focus on the learning and impact of the process (rather than the quality of the end product) but they usually reach fewer people (thus have a higher cost per person and less scale). When using video for catalyzing moments, the goal is normally bringing people into a longer-term advocacy effort.

In all three instances, there are issues with who controls access to tools/channels, platforms, and distribution channels. Though social media has changed this to an extent, there are still gatekeepers that impact who gets to be involved and whose voice/whose story is highlighted, funders who determine which work happens, and algorithms that dictate who will see the end products.

Participants suggested additional ways that video and film are used, including:

  • Social-emotional learning, where video is shown and then discussed to expand on new ideas and habits or to encourage behavior change.
  • Personal transformation through engaging with video.

Becky shared Digital Green’s approach, which is participatory and where community members to use video to help themselves and those around them. The organization supports community members to film videos about their agricultural practices, and these are then taken to nearby communities to share and discuss. (More on Digital Green here). Video doesn’t solve anyone’s development problem all by itself, Becky emphasized. If an agricultural extensionist is no good, having a video as part of their training materials won’t solve that. “If they have a top-down attitude, don’t engage, don’t answer questions, etc., or if people are not open to changing practices, video or no video, it won’t work.”

How can we improve impact measurement?

Questions arose from Salon participants around how to measure impact of film in a project or wider effort. Overall, impact measurement in the world of film for development is weak, noted one discussant, because change takes a long time and it is hard to track. We are often encouraged to focus on the wrong things like “vanity measurements” such as “likes” and “clicks,” but these don’t speak to longer-term and deeper impact of a film and they are often inappropriate in terms of who the audience is for the actual films (E.g., are we interested in impact on the local audience who is being impacted by the problem or the external audience who is being encouraged to care about it?)

Digital Green measures behavior change based on uptake of new agriculture practices. “After the agriculture extension worker shows a video to a group, they collect data on everyone that’s there. They record the questions that people ask, the feedback about why they can’t implement a particular practice, and in that way they know who is interested in trying a new practice.” The organization sets indicators for implementing the practice. “The extension worker returns to the community to see if the family has implemented a, b, c and if not, we try to find out why. So we have iterative improvement based on feedback from the video.” The organization does post their videos on YouTube but doesn’t know if the content there is having an impact. “We don’t even try to follow it up as we feel online video is much less relevant to our audience.” An organization that is working with social-emotional learning suggested that RCTs could be done to measure which videos are more effective. Others who work on a more individual or artistic level said that the immediate feedback and reactions from viewers were a way to gauge impact.

Donors often have different understandings of useful metrics. “What is a valuable metric? How can we gather it? How much do you want us to spend gathering it?” commented one person. Larger, longer-term partners who are not one-off donors will have a better sense of how to measure impact in reasonable ways. One person who formerly worked at a large public television station noted that it was common to have long conversation about measurement, goals, and aligning to the mission. “But we didn’t go by numbers, we focused on qualitative measurement.” She highlighted the importance of having these conversations with donors and asking them “why are you partnering with us?” Being able to say no to donors is important, she said. “If you are not sharing goals and objectives you shouldn’t be working together. Is gathering these stories a benefit to the community ? If you can’t communicate your actual intent, it’s very complicated.”

The goal of participatory video is less about engaging external (international) audiences or branding and advocacy. Rather it focuses on building skills and capacities through the process of video making. Here, the impact measurement is more related to individual, and often self-reported, skills such as confidence, finding your voice, public speaking, teamwork, leadership skills, critical thinking and media literacy. The quality of video production in these cases may be low, and videos unsuitable for widespread circulation, however the process and product can be catalysts for local-level change and locally-led advocacy on themes and topics that are important to the video-makers.

Participatory video suffers from low funding levels because it doesn’t reach the kind of scale that is desired by funders, though it can often contribute to deep, personal and community-level change. Some felt that even if community-created videos were of high production quality and translated to many languages, large-scale distribution is not always feasible because they are developed in and speak to/for hyper-local contexts, thus their relevance can be limited to smaller geographic areas. Expectation management with donors can go a long way towards shifting perspectives and understanding of what constitutes “impact.”

Should we re-think compensation?

Ambika noted that there are often challenges related to incentives and compensation when filming with communities for organizational purposes (such as branding or fundraising). Organizations are usually willing to pay people for their time in places such New York City and less inclined to do so when working with a rural community that is perceived to benefit from an organization’s services and projects. Perceptions by community members that a filmmaker is financially benefiting from video work can be hard to overcome, and this means that conflict may arise during non-profit filmmaking aimed at fundraising or building a brand. Even when individuals and communities are aware that they will not be compensated directly, there is still often some type of financial expectation, noted one Salon participant, such as the purchase of local goods and products.

Working closely with gatekeepers and community leaders can help to ease these tensions. When filmmaking takes several hours or days, however, participants may be visibly stressed or concerned about household or economic chores that are falling to the side during filming, and this can be challenging to navigate, noted one media professional. Filming in virtual reality can exacerbate this problem, since VR filming is normally over-programmed and repetitive in an effort to appear realistic.

One person suggested a change in how we approach incentives. “We spent about two years in a community filming a documentary about migration. This was part of a longer research project. We were not able to compensate the community, but we were able to invest directly in some of the local businesses and to raise funds for some community projects.” It’s difficult to understand why we would not compensate people for their time and their stories, she said. “This is basically their intellectual property, and we’re stealing it. We need a sector rethink.” Another person agreed, “in the US everyone gets paid and we have rules and standards for how that happens. We should be developing these for our work elsewhere.”

Participatory video tends to have less of a challenge with compensation. “People see the videos, the videos are for their neighbors. They are sharing good agricultural or nutrition approaches with people that they already know. They sometimes love being in the videos and that is partly its own reward. Helping people around them is also an incentive,” said one person.

There were several other rabbit holes to explore in relation to film and development, so look for more Salons in 2018!

To close out the year right, join us for ICT4Drinks on December 14th at Flatiron Hall from 7-9pm. If you’re signed up for Technology Salon emails, you’ll find the invitation in your inbox!

Salons run under Chatham House Rule so no attribution has been made in this post. If you’d like to attend a future Salon discussion, join the list at Technology Salon.

 

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(Joint post from Linda Raftree, MERL Tech and Megan Colnar, Open Society Foundations)

The American Evaluation Association Conference happens once a year, and offers literally hundreds of sessions. It can take a while to sort though all of them. Because there are so many sessions, it’s easy to feel a bit lost in the crowds of people and content.

So, Megan Colnar (Open Society Foundations) and I thought we’d share some of the sessions that caught our eye.

I’m on the look-out for innovative tech applications, responsible and gender-sensitive data collection practices, and virtual or online/social media-focused evaluation techniques and methods. Megan plans to tune into sessions on policy change, complexity-aware techniques, and better MEL practices for funders. 

We both can’t wait to learn about evaluation in the post-truth and fake news era. Full disclosure, our sessions are also featured below.

Hope we see you there!

Wednesday, November 8th

3.15-4.15

4.30-6.00

We also think a lot of the ignite talks during this session in the Thurgood Salon South look interesting, like:

6.15-7.15

7.00-8.30

Tour of a few poster sessions before dinner. Highlights might include:

  • M&E for Journalism (51)
  • Measuring Advocacy (3)
  • Survey measures of corruption (53)
  • Theory of change in practice (186)
  • Using social networks as a decision-making tool (225)

 

Thursday, Nov 9th

8.00-9.00 – early risers are rewarded with some interesting options

9.15-10.15

10.30-11.15

12.15-1.15

1.15-2.00

2.15-3.00

3.15-4.15

4.30-5.15

 

Friday, Nov 10th

8.00-9.30early risers rewarded again!

11.00-11.45

1.45-3.15

3.30-4.15

4.30-5.15

5.30-6.15– if you can hold out for one more on a Friday evening

6.30-7.15

 

Saturday, Nov 11th–you’re on your own! Let us know what treasures you discover

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For our Tuesday, July 27th Salon, we discussed partnerships and interoperability in global health systems. The room housed a wide range of perspectives, from small to large non-governmental organizations to donors and funders to software developers to designers to healthcare professionals to students. Our lead discussants were Josh Nesbit, CEO at Medic Mobile; Jonathan McKay, Global Head of Partnerships and Director of the US Office of Praekelt.org; and Tiffany Lentz, Managing Director, Office of Social Change Initiatives at ThoughtWorks

We started by hearing from our discussants on why they had decided to tackle issues in the area of health. Reasons were primarily because health systems were excluding people from care and organizations wanted to find a way to make healthcare inclusive. As one discussant put it, “utilitarianism has infected global health. A lack of moral imagination is the top problem we’re facing.”

Other challenges include requests for small scale pilots and customization/ bespoke applications, lack of funding and extensive requirements for grant applications, and a disconnect between what is needed on the ground and what donors want to fund. “The amount of documentation to get a grant is ridiculous, and then the system that is requested to be built is not even the system that needs to be made,” commented one person. Another challenge is that everyone is under constant pressure to demonstrate that they are being innovative. [Sidenote: I’m reminded of this post from 2010….] “They want things that are not necessarily in the best interest of the project, but that are seen to be innovations. Funders are often dragged along by that,” noted another person.

The conversation most often touched on the unfulfilled potential of having a working ecosystem and a common infrastructure for health data as well as the problems and challenges that will most probably arise when trying to develop these.

“There are so many uncoordinated pilot projects in different districts, all doing different things,” said one person. “Governments are doing what they can, but they don’t have the funds,” added another, “and that’s why there are so many small pilots happening everywhere.” One company noted that it had started developing a platform for SMS but abandoned it in favor of working with an existing platform instead. “Can we create standards and protocols to tie some of this work together? There isn’t a common infrastructure that we can build on,” was the complaint. “We seem to always start from scratch. I hope donors and organizations get smart about applying pressure in the right areas. We need an infrastructure that allows us to build on it and do the work!” On the other hand, someone warned of the risks of pushing everyone to “jump on a mediocre software or platform just because we are told to by a large agency or donor.”

The benefits of collaboration and partnership are apparent: increased access to important information, more cooperation, less duplication, the ability to build on existing knowledge, and so on. However, though desirable, partnerships and interoperability is not easy to establish. “Is it too early for meaningful partnerships in mobile health? I was wondering if I could say that…” said one person. “I’m not even sure I’m actually comfortable saying it…. But if you’re providing essential basic services, collecting sensitive medical data from patients, there should be some kind of infrastructure apart from private sector services, shouldn’t there?” The question is who should own this type of a mediator platform: governments? MNOs?

Beyond this, there are several issues related to control and ownership. Who would own the data? Is there a way to get to a point where the data would be owned by the patients and demonetized? If the common system is run by the private sector, there should be protections surrounding the patients’ sensitive information. Perhaps this should be a government-run system. Should it be open source?

Open source has its own challenges. “Well… yes. We’ve practiced ‘hopensource’,” said one person (to widespread chuckles).

Another explained that the way we’ve designed information systems has held back shifts in health systems. “When we’re comparing notes and how we are designing products, we need to be out ahead of the health systems and financing shifts. We need to focus on people-centered care. We need to gather information about a person over time and place. About the teams who are caring for them. Many governments we’re working with are powerless and moneyless. But even small organizations can do something. When we show up and treat a government as a systems owner that is responsible to deliver health care to their citizens, then we start to think about them as a partner, and they begin to think about how they could support their health systems.”

One potential model is to design a platform or system such that it can eventually be handed off to a government. This, of course, isn’t a simple idea in execution. Governments can be limited by their internal expertise. The personnel that a government has at the time of the handoff won’t necessarily be there years or months later. So while the handoff itself may be successful in the short term, there’s no firm guarantee that the system will be continually operational in the future. Additionally, governments may not be equipped with the knowledge to make the best decisions about software systems they purchase. Governments’ negotiating capacity must be expanded if they are to successfully run an interoperable system. “But if we can bring in a snazzy system that’s already interoperable, it may be more successful,” said one person.

Having a common data infrastructure is crucial. However, we must also spend some time thinking about what the data itself should look like. Can it be standardized? How can we ensure that it is legible to anyone with access to it?

These are only some of the relevant political issues, and at a more material level, one cannot ignore the technical challenges of maintaining a national scale system. For example, “just getting a successful outbound dialing rate is hard!” said one person. “If you are running servers in Nigeria it just won’t always be up! I think human centered design is important. But there is also a huge problem simply with making these things work at scale. The hardcore technical challenges are real. We can help governments to filter through some of the potential options. Like, can a system demonstrate that it can really operate at massive scale?” Another person highlighted that “it’s often non-profits who are helping to strengthen the capacity of governments to make better decisions. They don’t have money for large-scale systems and often don’t know how to judge what’s good or to be a strong negotiator. They are really in a bind.”

This is not to mention that “the computers have plastic over them half the time. Electricity, computers, literacy, there are all these issues. And the TelCo infrastructure! We have layers of capacity gaps to address,” said one person.

There are also donors to consider. They may come into a project with unrealistic expectations of what is normal and what can be accomplished. There is a delicate balance to be struck between inspiring the donors to take up the project and managing expectations so that they are not disappointed.” One strategy is to “start hopeful and steadily temper expectations.” This is true also with other kinds of partnerships. “Building trust with organizations so that when things do go bad, you can try to manage it is crucial. Often it seems like you don’t want to be too real in the first conversation. I think, ‘if I lay this on them at the start it can be too real and feel overwhelming.…'” Others recommended setting expectations about how everyone together is performing. “It’s more like, ‘together we are going to be looking at this, and we’ll be seeing together how we are going to work and perform together.”

Creating an interoperable data system is costly and time-consuming, oftentimes more so than donors and other stakeholders imagine, but there are real benefits. Any step in the direction of interoperability must deal with challenges like those considered in this discussion. Problems abound. Solutions will be harder to come by, but not impossible.

So, what would practitioners like to see? “I would like to see one country that provides an incredible case study showing what good partnership and collaboration looks like with different partners working at different levels and having a massive impact and improved outcomes. Maybe in Uganda,” said one person. “I hope we see more of us rally around supporting and helping governments to be the system owners. We could focus on a metric or shared cause – I hope in the near future we have a view into the equity measure and not just the vast numbers. I’d love to see us use health equity as the rallying point,” added another. From a different angle, one person felt that “from a for-profit, we could see it differently. We could take on a country, a clinic or something as our own project. What if we could sponsor a government’s health care system?”

A participant summed the Salon up nicely: “I’d like to make a flip-side comment. I want to express gratitude to all the folks here as discussants. This is one of the most unforgiving and difficult environments to work in. It’ SO difficult. You have to be an organization super hero. We’re among peers and feel it as normal to talk about challenges, but you’re really all contributing so much!”

Salons are run under Chatham House Rule so not attribution has been made in this post. If you’d like to attend a future Salon discussion, join the list at Technology Salon.

 

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(I’ve been blogging a little bit over at MERLTech.org. Here’s a repost.)

It can be overwhelming to get your head around all the different kinds of data and the various approaches to collecting or finding data for development and humanitarian monitoring, evaluation, research and learning (MERL).

Though there are many ways of categorizing data, lately I find myself conceptually organizing data streams into four general buckets when thinking about MERL in the aid and development space:

  1. ‘Traditional’ data. How we’ve been doing things for(pretty much)ever. Researchers, evaluators and/or enumerators are in relative control of the process. They design a specific questionnaire or a data gathering process and go out and collect qualitative or quantitative data; they send out a survey and request feedback; they do focus group discussions or interviews; or they collect data on paper and eventually digitize the data for analysis and decision-making. Increasingly, we’re using digital tools for all of these processes, but they are still quite traditional approaches (and there is nothing wrong with traditional!).
  2. ‘Found’ data.  The Internet, digital data and open data have made it lots easier to find, share, and re-use datasets collected by others, whether this is internally in our own organizations, with partners or just in general. These tend to be datasets collected in traditional ways, such as government or agency data sets. In cases where the datasets are digitized and have proper descriptions, clear provenance, consent has been obtained for use/re-use, and care has been taken to de-identify them, they can eliminate the need to collect the same data over again. Data hubs are springing up that aim to collect and organize these data sets to make them easier to find and use.
  3. ‘Seamless’ data. Development and humanitarian agencies are increasingly using digital applications and platforms in their work — whether bespoke or commercially available ones. Data generated by users of these platforms can provide insights that help answer specific questions about their behaviors, and the data is not limited to quantitative data. This data is normally used to improve applications and platform experiences, interfaces, content, etc. but it can also provide clues into a host of other online and offline behaviors, including knowledge, attitudes, and practices. One cautionary note is that because this data is collected seamlessly, users of these tools and platforms may not realize that they are generating data or understand the degree to which their behaviors are being tracked and used for MERL purposes (even if they’ve checked “I agree” to the terms and conditions). This has big implications for privacy that organizations should think about, especially as new regulations are being developed such a the EU’s General Data Protection Regulations (GDPR). The commercial sector is great at this type of data analysis, but the development set are only just starting to get more sophisticated at it.
  4. ‘Big’ data. In addition to data generated ‘seamlessly’ by platforms and applications, there are also ‘big data’ and data that exists on the Internet that can be ‘harvested’ if one only knows how. The term ‘Big data’ describes the application of analytical techniques to search, aggregate, and cross-reference large data sets in order to develop intelligence and insights. (See this post for a good overview of big data and some of the associated challenges and concerns). Data harvesting is a term used for the process of finding and turning ‘unstructured’ content (message boards, a webpage, a PDF file, Tweets, videos, comments), into ‘semi-structured’ data so that it can then be analyzed. (Estimates are that 90 percent of the data on the Internet exists as unstructured content). Currently, big data seems to be more apt for predictive modeling than for looking backward at how well a program performed or what impact it had. Development and humanitarian organizations (self included) are only just starting to better understand concepts around big data how it might be used for MERL. (This is a useful primer).

Thinking about these four buckets of data can help MERL practitioners to identify data sources and how they might complement one another in a MERL plan. Categorizing them as such can also help to map out how the different kinds of data will be responsibly collected/found/harvested, stored, shared, used, and maintained/ retained/ destroyed. Each type of data also has certain implications in terms of privacy, consent and use/re-use and how it is stored and protected. Planning for the use of different data sources and types can also help organizations choose the data management systems needed and identify the resources, capacities and skill sets required (or needing to be acquired) for modern MERL.

Organizations and evaluators are increasingly comfortable using mobile and/or tablets to do traditional data gathering, but they often are not using ‘found’ datasets. This may be because these datasets are not very ‘find-able,’ because organizations are not creating them, re-using data is not a common practice for them, the data are of questionable quality/integrity, there are no descriptors, or a variety of other reasons.

The use of ‘seamless’ data is something that development and humanitarian agencies might want to get better at. Even though large swaths of the populations that we work with are not yet online, this is changing. And if we are using digital tools and applications in our work, we shouldn’t let that data go to waste if it can help us improve our services or better understand the impact and value of the programs we are implementing. (At the very least, we had better understand what seamless data the tools, applications and platforms we’re using are collecting so that we can manage data privacy and security of our users and ensure they are not being violated by third parties!)

Big data is also new to the development sector, and there may be good reason it is not yet widely used. Many of the populations we are working with are not producing much data — though this is also changing as digital financial services and mobile phone use has become almost universal and the use of smart phones is on the rise. Normally organizations require new knowledge, skills, partnerships and tools to access and use existing big data sets or to do any data harvesting. Some say that big data along with ‘seamless’ data will one day replace our current form of MERL. As artificial intelligence and machine learning advance, who knows… (and it’s not only MERL practitioners who will be out of a job –but that’s a conversation for another time!)

Not every organization needs to be using all four of these kinds of data, but we should at least be aware that they are out there and consider whether they are of use to our MERL efforts, depending on what our programs look like, who we are working with, and what kind of MERL we are tasked with.

I’m curious how other people conceptualize their buckets of data, and where I’ve missed something or defined these buckets erroneously…. Thoughts?

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