Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance

Curated on Posted on by Stefaan Verhulst

Paper by Emily Aiken et al: “The COVID-19 pandemic has devastated many low- and middle-income countries (LMICs), causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have mobilized targeted social assistance programs. Targeting is a central challenge in the administration of these programs: given available data, how does one rapidly identify the individuals and families with the greatest need? This challenge is particularly acute in the large number of LMICs that lack recent and comprehensive data on household income and wealth.

Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our approach uses traditional survey-based measures of consumption and wealth to train machine learning algorithms that recognize patterns of poverty in non-traditional data; the trained algorithms are then used to prioritize aid to the poorest regions and mobile subscribers. We evaluate this approach by studying Novissi, Togo’s flagship emergency cash transfer program, which used these algorithms to determine eligibility for a rural assistance program that disbursed millions of dollars in COVID-19 relief aid. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo at the time, the machine learning approach reduces errors of exclusion by 4-21%. Relative to methods that require a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date….(More)”.

Guide on Geospatial Data Integration in Official Statistics

Curated on Posted on by Stefaan Verhulst

Report by PARIS21: “National geospatial integration agencies can provide detailed, timely and relevant data about people, businesses, buildings, infrastructures, agriculture, natural resources and anthropogenic impacts on the biosphere. There is a clear benefit to integrating geospatial data into traditional national statistical systems. Together they provide a very clear picture of the social, economic and environmental issues that underpin sustainable development and allow for more informed policy making. But the question is where to start?

geospatial data integration

This new PARIS21 publication provides a practical guide, based on five principles for national statistics offices to form stronger partnerships with national geospatial integration agencies….(More)”.

The Social Sector Needs a Meta Movement

Curated on Posted on by Stefaan Verhulst

Essay by Laura Deaton: “Imagine a world where the social sector exercises the full measure of its power and influence, fueled by its more than 12 million employees and 64 million volunteers. Imagine people who are fighting for living wages, women’s rights, early childhood education, racial justice, and climate action locking arms and pushing for broad social and environmental progress. Imagine a movement of movements with a bold, integrated policy agenda that drives real progress toward a more healthy, sustainable, resilient, and equitable world—not in some utopian future, but in the next decade.

If we click the heels of our ruby slippers together, we can go to that place.

OK, it’s not quite that easy. But we already have what we need to make it happen: the people, organizational models, and money. All of us—nonprofits, activists, funders, capacity builders, and knowledge providers—need to summon the vision and willingness to reach beyond our current bounds. And then we need to just do it.

Right now, we’re living in a social sector version of the tragedy of the commons, with organizations and coalitions pursuing their goals in silos and advocating only for their own narrow band of policy prescriptions. This problem is deep and wide—it’s happening both within and across movements—and it draws down the power of the sector as a whole. It’s time—actually well past time—to apply tried-and-true templates for grassroots movement building to the entire social sector and create demand for public policy changes that will move the needle toward long-term shared prosperity.

This involves a shift in mindset—from seeing our organizations as doing one thing (“We advocate for people experiencing homelessness”) to seeing them as part of a bigger thing (“We’re engaged in a movement that advocates for social and environmental justice”). Much as layers of identities make up our whole selves, this shift stands to weave all the strands of activism and service into our sector’s self-conception. From there, we can build an advocacy network that connects currently disparate movements and aligns agendas in pursuit of common goals. This requires action in the following areas: ramping up support for grassroots initiatives; coalescing behind a common goals framework; and designing a network support system that has regional, statewide, national, and potentially global scale….(More)”.

Do journalists “hide behind” sources when they use numbers in the news?

Curated on Posted on by Stefaan Verhulst

Article by Mark Coddington and Seth Lewis: “Numerical information is a central piece of journalism. Just look at how often stories rely on quantitative data — from Covid case numbers to public opinion polling to economics statistics — as their evidentiary backbone. The rise of data journalism, with its slick visualizations and interactives, has reinforced the role and influence of numbers in the news.

But, as B.T. Lawson reminds us in a new article in Journalism Practice, though we have plenty of research on this decade-long boom in data journalism, much of the research “overstates the significance of the data journalist within the news media. Yes, data journalists are now a mainstay of most news organizations, but they are not the only journalists using numbers. Far from it.”

Indeed, in contrast to the 1960s and 70s era of computer-assisted reporting, when a small minority of specialized reporters worked with data but most reporters did not, nowadays virtually all journalists are expected to engage with numbers as part of their work. Which brings up a potential problem: Some research suggests that journalists rarely challenge the numbers they receive, leading them to accept and reproduce the discourse around those numbers from their sources.

To get a clearer picture of how journalists draw on numbers and narratives about them, Lawson examined reporters’ use of numbers in their coverage of seven humanitarian crises in 2017. The author did this in two ways: first through a content analysis of 978 news articles from U.K. news media (to look for some direct or indirect form of challenging statistics, cross-verifying one claim relative to another, etc.), and then through interviews with 16 journalists involved in at least one of those stories, to gain additional insights into the process of receiving and reporting on numbers.

The title of the resulting article — “Hiding Behind Databases, Institutions and Actors: How Journalists Use Statistics in Reporting Humanitarian Crises” — indicates something about one of its findings: namely, that journalists covering humanitarian crises rely heavily on numbers, often provided by NGOs or the UN, but they seldom verify the numbers they use, mainly because they see it as outside their role to do such work and because they “hide behind” the perceived credibility of their sources….(More)”

AI helps scour video archives for evidence of human-rights abuses

Curated on Posted on by Stefaan Verhulst

The Economist: “Thanks especially to ubiquitous camera-phones, today’s wars have been filmed more than any in history. Consider the growing archives of Mnemonic, a Berlin charity that preserves video that purports to document war crimes and other violations of human rights. If played nonstop, Mnemonic’s collection of video from Syria’s decade-long war would run until 2061. Mnemonic also holds seemingly bottomless archives of video from conflicts in Sudan and Yemen. Even greater amounts of potentially relevant additional footage await review online.

Outfits that, like Mnemonic, scan video for evidence of rights abuses note that the task is a slog. Some trim costs by recruiting volunteer reviewers. Not everyone, however, is cut out for the tedium and, especially, periodic dreadfulness involved. That is true even for paid staff. Karim Khan, who leads a United Nations team in Baghdad investigating Islamic State (IS) atrocities, says viewing the graphic cruelty causes enough “secondary trauma” for turnover to be high. The UN project, called UNITAD, is sifting through documentation that includes more than a year’s worth of video, most of it found online or on the phones and computers of captured or killed IS members.

Now, however, reviewing such video is becoming much easier. Technologists are developing a type of artificial-intelligence (AI) software that uses “machine vision” to rapidly scour video for imagery that suggests an abuse of human rights has been recorded. It’s early days, but the software is promising. A number of organisations, including Mnemonic and UNITAD, have begun to operate such programs.

This year UNITAD began to run one dubbed Zeteo. It performs well, says David Hasman, one of its operators. Zeteo can be instructed to find—and, if the image resolution is decent, typically does find—bits of video showing things like explosions, beheadings, firing into a crowd and grave-digging. Zeteo can also spot footage of a known person’s face, as well as scenes as precise as a woman walking in uniform, a boy holding a gun in twilight, and people sitting on a rug with an IS flag in view. Searches can encompass metadata that reveals when, where and on what devices clips were filmed….(More)”.

Data-driven environmental decision-making and action in armed conflict

Curated on Posted on by Stefaan Verhulst

Essay by Wim Zwijnenburg: “Our understanding of how severely armed conflicts have impacted natural resources, eco-systems, biodiversity and long-term implications on climate has massively improved over the last decade. Without a doubt, cataclysmic events such as the 1991 Gulf War oil fires contributed to raising awareness on the conflict-environment nexus, and the images of burning wells are engraved into our collective mind. But another more recent, under-examined yet major contributor to this growing cognizance is the digital revolution, which has provided us with a wealth of data and information from conflict-affected countries quickly made available through the internet. With just a few clicks, anyone with a computer or smartphone and a Wi-Fi connection can follow, often in near-real time, events shared through social media in warzones or satellite imagery showing what is unfolding on the ground.

These developments have significantly deepened our understanding of how military activities, both historically and in current conflicts, contribute to environmental damage and can impact the lives and livelihoods of civilians. Geospatial analysis through earth observation (EO) is now widely used to document international humanitarian law (IHL) violations, improve humanitarian response and inform post-conflict assessments.

These new insights on conflict-environment dynamics have driven humanitarian, military and political responses. The latter are essential for the protection of the environment in armed conflict: with knowledge and understanding also comes a responsibility to prevent, mitigate and minimize environmental damage, in line with existing international obligations. Of particular relevance, under international humanitarian law, militaries must take into account incidental environmental damage that is reasonably foreseeable based on an assessment of information from all sources available to them at the relevant time (ICRC Guidelines on the Protection of the Environment, Rule 7; Customary IHL Rule 43). Excessive harm is prohibited, and all feasible precautions must be taken to reduce incidental damage (Guidelines Rule 8, Customary IHL Rule 44).

How do we ensure that the data-driven strides forward in understanding conflict-driven environmental damage translate into proper military training and decision-making, humanitarian response and reconstruction efforts? How can this influence behaviour change and improve accountability for military actions and targeting decisions?…(More)”.

Investing in Data Saves Lives

Curated on Posted on by Stefaan Verhulst

Mark Lowcock and Raj Shah at Project Syndicate: “…Our experience of building a predictive model, and its use by public-health officials in these countries, showed that this approach could lead to better humanitarian outcomes. But it was also a reminder that significant data challenges, regarding both gaps and quality, limit the viability and accuracy of such models for the world’s most vulnerable countries. For example, data on the prevalence of cardiovascular diseases was 4-7 years old in several poorer countries, and not available at all for Sudan and South Sudan.

Globally, we are still missing about 50% of the data needed to respond effectively in countries experiencing humanitarian emergencies. OCHA and The Rockefeller Foundation are cooperating to provide early insight into crises, during and beyond the COVID-19 pandemic. But realizing the full potential of our approach depends on the contributions of others.

So, as governments, development banks, and major humanitarian and development agencies reflect on the first year of the pandemic response, as well as on discussions at the recent World Bank Spring Meetings, they must recognize the crucial role data will play in recovering from this crisis and preventing future ones. Filling gaps in critical data should be a top priority for all humanitarian and development actors.

Governments, humanitarian organizations, and regional development banks thus need to invest in data collection, data-sharing infrastructure, and the people who manage these processes. Likewise, these stakeholders must become more adept at responsibly sharing their data through open data platforms and that maintain rigorous interoperability standards.

Where data are not available, the private sector should develop new sources of information through innovative methods such as using anonymized social-media data or call records to understand population movement patterns….(More)”.

We’re Beating Systems Change to Death

Curated on Posted on by Stefaan Verhulst

Essay by Kevin Starr: “Systems change! Just saying the words aloud makes me feel like one of the cognoscenti, one of the elite who has transcended the ways of old-school philanthropy. Those two words capture our aspirations of lasting impact at scale: systems are big, and if you manage to change them, they’ll keep spinning out impact forever. Why would you want to do anything else?

There’s a problem, though. “Systems analysis” is an elegant and useful way to think about problems and get ideas for solutions, but “systems change” is accelerating toward buzzword purgatory. It’s so sexy that everyone wants to use it for everything. …

But when you rummage through the growing literature on systems change thinking, there are in fact a few recurring themes. One is the need to tackle the root causes of any problem you take on. Another is that a broad coalition must be assembled ASAP. Finally, the most salient theme is the notion that the systems involved are transformed as a result of the work (although in many of the examples I read about, it’s not articulated clearly just what system is being changed).

Taken individually or as a whole, these themes point to some of the ways in which systems change is a less-than-ideal paradigm for the work we need to get done:

1. It’s too hard to know to what degree systems change is or isn’t happening. It may be the case that “not everything that matters can be counted,” but most of the stuff that matters can, and it’s hard to get better at something if you’re unable to measure it. But these words of a so-called expert on systems change measurement are typical of what I’ve seen in in the literature: “Measuring systems change is about detecting patterns in the connections between the parts. It is about qualitative changes in the structure of the system, about its adaptiveness and resilience, about synergies emerging from collective efforts—and more…”

Like I said, it’s too hard to know to what is or isn’t happening.

2. “Root cause” thinking can—paradoxically—bog down progress. “Root cause” analysis is a common feature of most systems change discussions, and it’s a wonderful tool to generate ideas and avoid unintended consequences. However, broad efforts to tackle all of a problem’s root causes can turn anything into a complicated, hard-to-replicate project. It can also make things look so overwhelming as to result in a kind of paralysis. And however successful a systems change effort might be, that complication makes it hard to replicate, and you’re often stuck with a one-off project….(More)”.

Averting Catastrophe

Curated on Posted on by Stefaan Verhulst

Book by Cass Sunstein on “Decision Theory for COVID-19, Climate Change, and Potential Disasters of All Kinds…The world is increasingly confronted with new challenges related to climate change, globalization, disease, and technology. Governments are faced with having to decide how much risk is worth taking, how much destruction and death can be tolerated, and how much money should be invested in the hopes of avoiding catastrophe. Lacking full information, should decision-makers focus on avoiding the most catastrophic outcomes? When should extreme measures be taken to prevent as much destruction as possible?

Averting Catastrophe explores how governments ought to make decisions in times of imminent disaster. Cass R. Sunstein argues that using the “maximin rule,” which calls for choosing the approach that eliminates the worst of the worst-case scenarios, may be necessary when public officials lack important information, and when the worst-case scenario is too disastrous to contemplate. He underscores this argument by emphasizing the reality of “Knightian uncertainty,” found in circumstances in which it is not possible to assign probabilities to various outcomes. Sunstein brings foundational issues in decision theory in close contact with real problems in regulation, law, and daily life, and considers other potential future risks. At once an approachable introduction to decision-theory and a provocative argument for how governments ought to handle risk, Averting Catastrophe offers a definitive path forward in a world rife with uncertainty….(More)”.

Resilience in the Digital Age

Curated on Posted on by Stefaan Verhulst

Book edited by Fred S. Roberts and Igor A. Sheremet: “The growth of a global digital economy has enabled rapid communication, instantaneous movement of funds, and availability of vast amounts of information. With this come challenges such as the vulnerability of digitalized sociotechnological systems (STSs) to destructive events (earthquakes, disease events, terrorist attacks). Similar issues arise for disruptions to complex linked natural and social systems (from changing climates, evolving urban environments, etc.). This book explores new approaches to the resilience of sociotechnological and natural-social systems in a digital world of big data, extraordinary computing capacity, and rapidly developing methods of Artificial Intelligence….

The world-wide COVID-19 pandemic illustrates the vulnerability of our healthcare systems, supply chains, and social infrastructure, and confronts our notions of what makes a system resilient. We have found that use of AI tools can lead to problems when unexpected events occur. On the other hand, the vast amounts of data available from sensors, satellite images, social media, etc. can also be used to make modern systems more resilient.

Papers in the book explore disruptions of complex networks and algorithms that minimize departure from a previous state after a disruption; introduce a multigrammatical framework for the technological and resource bases of today’s large-scale industrial systems and the transformations resulting from disruptive events; and explain how robotics can enhance pre-emptive measures or post-disaster responses to increase resiliency. Other papers explore current directions in data processing and handling and principles of FAIRness in data; how the availability of large amounts of data can aid in the development of resilient STSs and challenges to overcome in doing so. The book also addresses interactions between humans and built environments, focusing on how AI can inform today’s smart and connected buildings and make them resilient, and how AI tools can increase resilience to misinformation and its dissemination….(More)”.