Accelerate Aspirations: Moving Together to Achieve Systems Change

Report by “To solve our greatest global challenges, we need to accelerate how we use data for good. But to truly make data-driven tools that serve society, we must re-imagine data for social impact more broadly, more inclusively, and in a more interdisciplinary way. 

So, we face a choice. Business as usual can continue through funding and implementing under-resourced and siloed data projects that deliver incremental progress. Or we can think and act boldly to drive equitable and sustainable solutions. 

Accelerate Aspirations: Moving Together to Achieve Systems Change is a comprehensive report on the key trends and tensions in the emerging field of data for social impact…(More)”.

Industry Data for Society Partnership

Press Release: “On Wednesday, a new Industry Data for Society Partnership (IDSP) was launched by GitHub, Hewlett Packard Enterprise (HPE), LinkedIn, Microsoft, Northumbrian Water Group, R2 Factory and UK Power Networks. The IDSP is a first-of-its-kind cross-industry partnership to help advance more open and accessible private-sector data for societal good. The founding members of the IDSP agree to provide greater access to their data, where appropriate, to help tackle some of the world’s most pressing challenges in areas such as sustainability and inclusive economic growth.

In the past few years, open data has played a critical role in enabling faster research and collaboration across industries and with the public sector. As we saw during COVID-19, pandemic data that was made more open enabled researchers to make faster progress and gave citizens more information to inform their day-to-day activities. The IDSP’s goal is to continue this model into new areas and help address other complex societal challenges. The IDSP will serve as a forum for the participating companies to foster collaboration, as well as a resource for other entities working on related issues.

IDSP members commit to the following:

  • To open data or provide greater access to data, where appropriate, to help solve pressing societal problems in a usable, responsible and inclusive manner.
  • To share knowledge and information for the effective use of open data and data collaboration for social benefit.
  • To invest in skilling a broad class of professionals to use data effectively and responsibly for social impact.
  • To protect individuals’ privacy in all these activities.

The IDSP will also bring in other organizations with expertise in societal issues. At launch, The GovLab’s Data Program based at New York University and the Open Data Institute will both be partnership Affiliates to provide guidance and expertise for partnership endeavors…(More)”.

Operationalizing Digital Self Determination

Paper by Stefaan G. Verhulst: “We live in an era of datafication, one in which life is increasingly quantified and transformed into intelligence for private or public benefit. When used responsibly, this offers new opportunities for public good. However, three key forms of asymmetry currently limit this potential, especially for already vulnerable and marginalized groups: data asymmetries, information asymmetries, and agency asymmetries. These asymmetries limit human potential, both in a practical and psychological sense, leading to feelings of disempowerment and eroding public trust in technology. Existing methods to limit asymmetries (e.g., consent) as well as some alternatives under consideration (data ownership, collective ownership, personal information management systems) have limitations to adequately address the challenges at hand. A new principle and practice of digital self-determination (DSD) is therefore required.
DSD is based on existing concepts of self-determination, as articulated in sources as varied as Kantian philosophy and the 1966 International Covenant on Economic, Social and Cultural Rights. Updated for the digital age, DSD contains several key characteristics, including the fact that it has both an individual and collective dimension; is designed to especially benefit vulnerable and marginalized groups; and is context-specific (yet also enforceable). Operationalizing DSD in this (and other) contexts so as to maximize the potential of data while limiting its harms requires a number of steps. In particular, a responsible operationalization of DSD would consider four key prongs or categories of action: processes, people and organizations, policies, and products and technologies…(More)”.

What is PeaceTech?

Report by Behruz Davletov, Uma Kalkar, Marine Ragnet, and Stefaan Verhulst: “From sensors to detect explosives to geographic data for disaster relief to artificial intelligence verifying misleading online content, data and technology are essential assets for peace efforts. Indeed, the ongoing Russia-Ukraine war is a direct example of data, data science, and technology as a whole has been mobilized to assist and monitor conflict responses and support peacebuilding.

Yet understanding the ways in which technology can be applied for peace, and what kinds of peace promotion they can serve, as well as their associated risks remain muddled. Thus, a framework for the governance of these peace technologies—#PeaceTech—is needed at an international and transnational level to guide the responsible and purposeful use of technology and data to strengthen peace and justice initiatives.

Today, The GovLab is proud to announce the release of the “PeaceTech Topic Map: A Research Base for an Emerging Field,” an overview of the key themes and challenges of technologies used by and created for peace efforts…(More)”.

Data for Social Good: Non-Profit Sector Data Projects

Open Access Book by Jane Farmer, Anthony McCosker, Kath Albury & Amir Aryani: “In February 2020, just pre-COVID, a group of managers from community organisations met with us researchers about data for social good. “We want to collaborate with data,” said one CEO. “We want to find the big community challenges, work together to fix them and monitor the change we make over ten years.” The managers created a small, pooled fund and, through the 2020–2021 COVID lockdowns, used Zoom to workshop. Together we identified organisations’ datasets, probed their strengths and weaknesses, and found ways to share and visualise data. There were early frustrations about what data was available, its ‘granularity’ and whether new insights about the community could be found, but about half-way through the project, there was a tipping point, and something changed. While still focused on discovery from visualisations comparing their data by suburb, the group started to talk about other benefits. Through drawing in staff from across their organisations, they saw how the work of departments could be integrated by using data, and they developed new confidence in using analytics techniques. Together, the organisations developed an understanding of each other’s missions and services, while developing new relationships, trust and awareness of the possibilities of collaborating to address community needs. Managers completed the pilot having codesigned an interactive Community Resilience Dashboard, which enabled them to visualise their own organisations’ data and open public data to reveal new landscapes about community financial wellbeing and social determinants of health. They agreed they also had so much more: a collective data-capable partnership, internally and across organisations, with new potential to achieve community social justice driven by data.

We use this story to signify how right now is a special—indeed critical—time for non-profit organisations and communities to build their capability to work with data. Certainly, in high-income countries, there is pressure on non-profits to operate like commercial businesses—prioritising efficiency and using data about their outputs and impacts to compete for funding. However, beyond the immediate operational horizon, non-profits can use data analytics techniques to drive community social justice and potentially impact on the institutional capability of the whole social welfare sector. Non-profits generate a lot of data but innovating with technology is not a traditional competence, and it demands infrastructure investment and specialist workforce. Given their meagre access to funding, this book examines how non-profits of different types and sizes can use data for social good and find a path to data capability. The aim is to inspire and give practical examples of how non-profits can make data useful. While there is an emerging range of novel data for social good cases around the world, the case studies featured in this book exemplify our research and developing thinking in experimental data projects with diverse non-profits that harnessed various types of data. We outline a way to gain data capability through collaborating internally across departments and with other external non-profits and skilled data analytics partners. We term this way of working collaborative data action…(More)”.

Data and displacement: Ethical and practical issues in data-driven humanitarian assistance for IDPs

Blog by Vicki Squire: “Ten years since the so-called “data revolution” (Pearn et al, 2022), the rise of “innovation” and the proliferation of “data solutions” has rendered the assessment of changing data practices within the humanitarian sector ever more urgent. New data acquisition modalities have provoked a range of controversies across multiple contexts and sites (e.g. Human Rights Watch, 20212022a2022b). Moreover, a range of concerns have been raised about data sharing (e.g. Fast, 2022) and the inequities embedded within humanitarian data (e.g. Data Values, 2022).

With this in mind, the Data and Displacement project set out to explore the practical and ethical implications of data-driven humanitarian assistance in two contexts characterised by high levels of internal displacement: north-eastern Nigeria and South Sudan. Our interdisciplinary research team includes academics from each of the regions under analysis, as well as practitioners from the International Organization for Migration. From the start, the research was designed to centre the lived experiences of Internally Displaced Persons (IDPs), while also shedding light on the production and use of humanitarian data from multiple perspectives.

We conducted primary research during 2021-2022. Our research combines dataset analysis and visualisation techniques with a thematic analysis of 174 semi-structured qualitative interviews. In total we interviewed 182 people: 42 international data experts, donors, and humanitarian practitioners from a range of governmental and non-governmental organisations; 40 stakeholders and practitioners working with IDPs across north-eastern Nigeria and South Sudan (20 in each region); and 100 IDPs in camp-like settings (50 in each region). Our findings point to a disconnect between international humanitarian standards and practices on the ground, the need to revisit existing ethical guidelines such informed consent, and the importance of investing in data literacies…(More)”.

Hurricane Ian Destroyed Their Homes. Algorithms Sent Them Money

Article by Chris Stokel-Walker: “The algorithms that power Skai’s damage assessments are trained by manually labeling satellite images of a couple of hundred buildings in a disaster-struck area that are known to have been damaged. The software can then, at speed, detect damaged buildings across the whole affected area. A research paper on the underlying technology presented at a 2020 academic workshop on AI for disaster response claimed the auto-generated damage assessments match those of human experts with between 85 and 98 percent accuracy.

In Florida this month, GiveDirectly sent its push notification offering $700 to any user of the Providers app with a registered address in neighborhoods of Collier, Charlotte, and Lee Counties where Google’s AI system deemed more than 50 percent of buildings had been damaged. So far, 900 people have taken up the offer, and half of those have been paid. If every recipient takes up GiveDirectly’s offer, the organization will pay out $2.4 million in direct financial aid.

Some may be skeptical of automated disaster response. But in the chaos after an event like a hurricane making landfall, the conventional, human response can be far from perfect. Diaz points to an analysis GiveDirectly conducted looking at their work after Hurricane Harvey, which hit Texas and Louisiana in 2017, before the project with Google. Two out of the three areas that were most damaged and economically depressed were initially overlooked. A data-driven approach is “much better than what we’ll have from boots on the ground and word of mouth,” Diaz says.

GiveDirectly and Google’s hands-off, algorithm-led approach to aid distribution has been welcomed by some disaster assistance experts—with caveats. Reem Talhouk, a research fellow at Northumbria University’s School of Design and Centre for International Development in the UK, says that the system appears to offer a more efficient way of delivering aid. And it protects the dignity of recipients, who don’t have to queue up for handouts in public…(More)”.

Nowcasting daily population displacement in Ukraine through social media advertising data

Pre-Publication Paper by Douglas R. Leasure et al: “In times of crisis, real-time data mapping population displacements are invaluable for targeted humanitarian response. The Russian invasion of Ukraine on February 24, 2022 forcibly displaced millions of people from their homes including nearly 6m refugees flowing across the border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged near real-time social media marketing data to estimate sub-national population sizes every day disaggregated by age and sex. Our metric of internal displacement estimated that 5.3m people had been internally displaced away from their baseline administrative region by March 14. Results revealed four distinct displacement patterns: large scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics. While this innovative approach provided one of the only quantitative estimates of internal displacement in virtual real-time, we conclude by acknowledging risks and challenges for the future…(More)”.

Mapping community resources for disaster preparedness: humanitarian data capability and automated futures

Report by Anthony McCosker et al: “This report details the rationale, background research and design for a platform to help local communities map resources for disaster preparedness. It sets out a first step in improving community data capability through resource mapping to enhance humanitarian action before disaster events occur.The project seeks to enable local community disaster preparedness and thus build community resilience by improving the quality of data about community strengths, resources and assets.

In this report, the authors define a gap in existing humanitarian mapping approaches and the uses of open, public and social media data in humanitarian contexts. The report surveys current knowledge and present a selection of case studies delivering data and humanitarian mapping in local communities.

Drawing on this knowledge and practice review and stakeholder workshops throughout 2021, the authors also define a method and toolkit for the effective use of community assets data…(More)”

Localising AI for crisis response

Report by Aleks Berditchevskaia and Kathy Peach, Isabel Stewart: “Putting power back in the hands of frontline humanitarians and local communities.

This report documents the results of a year-long project to design and evaluate new proof-of-concept Collective Crisis Intelligence tools. These are tools that combine data from crisis-affected communities with the processing power of AI to improve humanitarian action.

The two collective crisis intelligence tool prototypes developed were:

  • NFRI-Predict: a tool that predicts which non-food aid items (NFRI) are most needed by different types of households in different regions of Nepal after a crisis.
  • Report and Respond: a French language SMS-based tool that allows Red Cross volunteers in Cameroon to check the accuracy of COVID-19 rumours or misinformation they hear from the community while they’re in the field, and receive real-time guidance on appropriate responses.

Both tools were developed using Nesta’s Participatory AI methods, which aimed to address some of the risks associated with humanitarian AI by involving local communities in the design, development and evaluation of the new tools.

The project was a partnership between Nesta’s Centre for Collective Intelligence Design (CCID) and Data Analytics Practice (DAP), the Nepal Red Cross and Cameroon Red Cross, IFRC Solferino Academy, and Open Lab Newcastle University, and it was funded by the UK Humanitarian Innovation Hub.

We found that collective crisis intelligence:

  • has the potential to make local humanitarian action more timely and appropriate to local needs.
  • can transform locally-generated data to drive new forms of (anticipatory) action.

We found that participatory AI:

  • can overcome several critiques and limitations of AI – as well as helping to improve model performance.
  • helps to surface tensions between the assumptions and standards set by AI gatekeepers versus the pragmatic reality of implementation.
  • creates opportunities for building and sharing new capabilities among frontline staff and data scientists.

We also validated that collective crisis intelligence and participatory AI can help increase trust in AI tools, but more research is needed to untangle the factors that were responsible…(More)”.