Reflecting the Past, Shaping the Future: Making AI Work for International Development

Curated on Posted on by Stefaan Verhulst

USAID Report: “We are in the midst of an unprecedented surge of interest in machine learning (ML) and artificial intelligence (AI) technologies. These tools, which allow computers to make data-derived predictions and automate decisions, have become part of daily life for billions of people. Ubiquitous digital services such as interactive maps, tailored advertisements, and voice-activated personal assistants are likely only the beginning. Some AI advocates even claim that AI’s impact will be as profound as “electricity or fire” that it will revolutionize nearly every field of human activity. This enthusiasm has reached international development as well. Emerging ML/AI applications promise to reshape healthcare, agriculture, and democracy in the developing world. ML and AI show tremendous potential for helping to achieve sustainable development objectives globally. They can improve efficiency by automating labor-intensive tasks, or offer new insights by finding patterns in large, complex datasets. A recent report suggests that AI advances could double economic growth rates and increase labor productivity 40% by 2035. At the same time, the very nature of these tools — their ability to codify and reproduce patterns they detect — introduces significant concerns alongside promise.

In developed countries, ML tools have sometimes been found to automate racial profiling, to foster surveillance, and to perpetuate racial stereotypes. Algorithms may be used, either intentionally or unintentionally, in ways that result in disparate or unfair outcomes between minority and majority populations. Complex models can make it difficult to establish accountability or seek redress when models make mistakes. These shortcomings are not restricted to developed countries. They can manifest in any setting, especially in places with histories of ethnic conflict or inequality. As the development community adopts tools enabled by ML and AI, we need a cleareyed understanding of how to ensure their application is effective, inclusive, and fair. This requires knowing when ML and AI offer a suitable solution to the challenge at hand. It also requires appreciating that these technologies can do harm — and committing to addressing and mitigating these harms.

ML and AI applications may sometimes seem like science fiction, and the technical intricacies of ML and AI can be off-putting for those who haven’t been formally trained in the field. However, there is a critical role for development actors to play as we begin to lean on these tools more and more in our work. Even without technical training in ML, development professionals have the ability — and the responsibility — to meaningfully influence how these technologies impact people.

You don’t need to be an ML or AI expert to shape the development and use of these tools. All of us can learn to ask the hard questions that will keep solutions working for, and not against, the development challenges we care about. Development practitioners already have deep expertise in their respective sectors or regions. They bring necessary experience in engaging local stakeholders, working with complex social systems, and identifying structural inequities that undermine inclusive progress. Unless this expert perspective informs the construction and adoption of ML/AI technologies, ML and AI will fail to reach their transformative potential in development.

This document aims to inform and empower those who may have limited technical experience as they navigate an emerging ML/AI landscape in developing countries. Donors, implementers, and other development partners should expect to come away with a basic grasp of common ML techniques and the problems ML is uniquely well-suited to solve. We will also explore some of the ways in which ML/AI may fail or be ill-suited for deployment in developing-country contexts. Awareness of these risks, and acknowledgement of our role in perpetuating or minimizing them, will help us work together to protect against harmful outcomes and ensure that AI and ML are contributing to a fair, equitable, and empowering future…(More)”.

Don’t Believe the Algorithm

Curated on Posted on by Stefaan Verhulst

Hannah Fry at the Wall Street Journal: “The Notting Hill Carnival is Europe’s largest street party. A celebration of black British culture, it attracts up to two million revelers, and thousands of police. At last year’s event, the Metropolitan Police Service of London deployed a new type of detective: a facial-recognition algorithm that searched the crowd for more than 500 people wanted for arrest or barred from attending. Driving around in a van rigged with closed-circuit TVs, the police hoped to catch potentially dangerous criminals and prevent future crimes.

It didn’t go well. Of the 96 people flagged by the algorithm, only one was a correct match. Some errors were obvious, such as the young woman identified as a bald male suspect. In those cases, the police dismissed the match and the carnival-goers never knew they had been flagged. But many were stopped and questioned before being released. And the one “correct” match? At the time of the carnival, the person had already been arrested and questioned, and was no longer wanted.

Given the paltry success rate, you might expect the Metropolitan Police Service to be sheepish about its experiment. On the contrary, Cressida Dick, the highest-ranking police officer in Britain, said she was “completely comfortable” with deploying such technology, arguing that the public expects law enforcement to use cutting-edge systems. For Dick, the appeal of the algorithm overshadowed its lack of efficacy.

She’s not alone. A similar system tested in Wales was correct only 7% of the time: Of 2,470 soccer fans flagged by the algorithm, only 173 were actual matches. The Welsh police defended the technology in a blog post, saying, “Of course no facial recognition system is 100% accurate under all conditions.” Britain’s police force is expanding the use of the technology in the coming months, and other police departments are following suit. The NYPD is said to be seeking access to the full database of drivers’ licenses to assist with its facial-recognition program….(More).

European science funders ban grantees from publishing in paywalled journals

Curated on Posted on by Stefaan Verhulst

Martin Enserink at Science: “Frustrated with the slow transition toward open access (OA) in scientific publishing, 11 national funding organizations in Europe turned up the pressure today. As of 2020, the group, which jointly spends about €7.6 billion on research annually, will require every paper it funds to be freely available from the moment of publication. In a statement, the group said it will no longer allow the 6- or 12-month delays that many subscription journals now require before a paper is made OA, and it won’t allow publication in so-called hybrid journals, which charge subscriptions but also make individual papers OA for an extra fee.

The move means grantees from these 11 funders—which include the national funding agencies in the United Kingdom, the Netherlands, and France as well as Italy’s National Institute for Nuclear Physics—will have to forgo publishing in thousands of journals, including high-profile ones such as Nature, Science, Cell, and The Lancet, unless those journals change their business model. “We think this could create a tipping point,” says Marc Schiltz, president of Science Europe, the Brussels-based association of science organizations that helped coordinate the plan. “Really the idea was to make a big, decisive step—not to come up with another statement or an expression of intent.”

The announcement delighted many OA advocates. “This will put increased pressure on publishers and on the consciousness of individual researchers that an ecosystem change is possible,” says Ralf Schimmer, head of Scientific Information Provision at the Max Planck Digital Library in Munich, Germany. Peter Suber, director of the Harvard Library Office for Scholarly Communication, calls the plan “admirably strong.” Many other funders support OA, but only the Bill & Melinda Gates Foundation applies similarly stringent requirements for “immediate OA,” Suber says. The European Commission and the European Research Council support the plan; although they haven’t adopted similar requirements for the research they fund, a statement by EU Commissioner for Research, Science and Innovation Carlos Moedas suggests they may do so in the future and urges the European Parliament and the European Council to endorse the approach….(More)”.

The Role of Scholarly Communication in a Democratic Society

Curated on Posted on by Stefaan Verhulst

Introdution to Special Issue of the Journal of Librarianship and Scholarly Communication by Yasmeen Shorish: “The pillars of a democratic society (equity, a free press, fair elections, engaged citizens, and the equal application of laws) are directly impacted by the availability, accessibility, and accuracy of information. Additionally, engaged, critically thinking individuals require an understanding of how knowledge is produced and shared, who has the power to make that information available, and how they—as information consumers and producers—are involved in those processes. Proposed and adopted government policies and actions that limit transparency and engagement, the increasing commodification of learning, the framing of education as a measure of return on investment (ROI) in real dollars, and the rapid transition of the research landscape to an increasingly monopolized walled garden have been in motion for some time but come into sharp focus through the lens of scholarly communication.

Scholarly communication is a broad domain that covers how information and knowledge are created and shared, what levels of access to that information are available, and how economic factors influence information communication. This system affects both the production and consumption of information and knowledge.

As such, the question of democratic or equitable processes is internal (Is the scholarly communication domain democratic and equitable?) and external (How does scholarly communication affect a democratic society?). The scholarly communication and research landscapes have never been level playing fields for all interested parties. Funding constraints, prejudices, and politics have all been factors in the amplification and suppression of people’s perspectives. In this special issue, I wanted to investigate how librarians and other information professionals are interrogating those practices and situating their scholarly communication work within the frame of an equitable and democratic society. What are the challenges and the opportunities? Where are we making progress? Where is there disenfranchisement? …(More)”.

Keeping Democracy Alive in Cities

Curated on Posted on by Stefaan Verhulst
Myung J. Lee at the Stanford Social Innovation Review:  “It seems everywhere I go these days, people are talking and writing and podcasting about America’s lack of trust—how people don’t trust government and don’t trust each other. President Trump discourages us from trusting anything, especially the media. Even nonprofit organizations, which comprise the heart of civil society, are not exempt: A recent study found that trust in NGOsdropped by nine percent between 2017 and 2018. This fundamental lack of trust is eroding the shared public space where progress and even governance can happen, putting democracy at risk.

How did we get here? Perhaps it’s because Americans have taken our democratic way of life for granted. Perhaps it’s because people’s individual and collective beliefs are more polarized—and more out in the open—than ever before. Perhaps we’ve stopped believing we can solve problems together.

There are, however, opportunities to rebuild and fortify our sense of trust. This is especially true at the local level, where citizens can engage directly with elected leaders, nonprofit organizations, and each other.

As French political scientist Alexis de Tocqueville observed in Democracy in America, “Municipal institutions constitute the strength of free nations. Town meetings are to liberty what primary schools are to science; they bring it within the people’s reach; they teach men how to use and how to enjoy it.” Through town halls and other means, cities are where citizens, elected leaders, and nonprofit organizations can most easily connect and work together to improve their communities.

Research shows that, while trust in government is low everywhere, it is highest in local government. This is likely because people can see that their votes influence issues they care about, and they can directly interact with their mayors and city council members. Unlike with members of Congress, citizens can form real relationships with local leaders through events like “walks with the mayor” and neighborhood cleanups. Some mayors do even more to connect with their constituents. In Detroit, for example, Mayor Michael Duggan meets with residents in their homes to help them solve problems and answer questions in person. Many mayors also join in neighborhood projects. San Jose Mayor Sam Liccardo, for example, participates in a different community cleanup almost every week. Engaged citizens who participate in these activities are more likely to feel that their participation in democratic society is valuable and effective.

The role of nonprofit and community-based organizations, then, is partly to sustain democracy by being the bridge between city governments and citizens, helping them work together to solve concrete problems. It’s hard and important work. Time and again, this kind of relationship- and trust-building through action creates ripple effects that grow over time.

In my work with Cities of Service, which helps mayors and other city leaders effectively engage their citizens to solve problems, I’ve learned that local government works better when it is open to the ideas and talents of citizens. Citizen collaboration can take many forms, including defining and prioritizing problems, generating solutions, and volunteering time, creativity, and expertise to set positive change in motion. Citizens can leverage their own deep expertise about what’s best for their families and communities to deliver better services and solve public problems….(More)”.

Message and Environment: a framework for nudges and choice architecture

Curated on Posted on by Stefaan Verhulst

Paper by Luca Congiu and Ivan Moscati in Behavioural Public Policy: “We argue that the diverse components of a choice architecture can be classified into two main dimensions – Message and Environment – and that the distinction between them is useful in order to better understand how nudges work. In the first part of this paper, we define what we mean by nudge, explain what Message and Environment are, argue that the distinction between them is conceptually robust and show that it is also orthogonal to other distinctions advanced in the nudge literature. In the second part, we review some common types of nudges and show they target either Message or Environment or both dimensions of the choice architecture. We then apply the Message–Environment framework to discuss some features of Amazon’s website and, finally, we indicate how the proposed framework could help a choice architect to design a new choice architecture….(More)”.

The UK’s Gender Pay Gap Open Data Law Has Flaws, But Is A Positive Step Forward

Curated on Posted on by Stefaan Verhulst

Article by Michael McLaughlin: “Last year, the United Kingdom enacted a new regulation requiring companies to report information about their gender pay gap—a measure of the difference in average pay between men and women. The new rules are a good example of how open data can drive social change. However, the regulations have produced some misleading statistics, highlighting the importance of carefully crafting reporting requirements to ensure that they produce useful data.

In the UK, nearly 11,000 companies have filed gender pay gap reports, which include both the difference between the mean and median hourly pay rates for men and women as well the difference in bonuses. And the initial data reveals several interesting findings. Median pay for men is 11.8 percent higher than for women, on average, and nearly 87 percent of companies pay men more than women on average. In addition, over 1,000 firms had a median pay gap greater than 30 percent. The sectors with the highest pay gaps—construction, finance, and insurance—each pay men at least 20 percent more than women. A major reason for the gap is a lack of women in senior positions—UK women actually make more than men between the ages of 22-29. The total pay gap is also a result of more women holding part-time jobs.

However, as detractors note, the UK’s data can be misleading. For example, the data overstates the pay gap on bonuses because it does not adjust these figures for hours worked. More women work part-time than men, so it makes sense that women would receive less in bonus pay when they work less. The data also understates the pay gap because it excludes the high compensation of partners in organizations such as law firms, a group that includes few women. And it is important to note that—by definition—the pay gap data does not compare the wages of men and women working the same jobs, so the data says nothing about whether women receive equal pay for equal work.

Still, publication of the data has sparked an important national conversation. Google searches in the UK for the phrase “gender pay gap” experienced a 12-month high the week the regulations began enforcement, and major news sites like Financial Times have provided significant coverage of the issue by analyzing the reported data. While it is too soon to tell if the law will change employer behavior, such as businesses hiring more female executives, or employee behavior, such as women leaving companies or fields that pay less, countries with similar reporting requirements, such as Belgium, have seen the pay gap narrow following implementation of their rules.

Requiring companies to report this data to the government may be the only way to obtain gender pay gap data, because evidence suggests that the private sector will not produce this data on its own. Only 300 UK organizations joined a voluntary government program to report their gender pay gap in 2011, and as few as 11 actually published the data. Crowdsourced efforts, where women voluntary report their pay, have also suffered from incomplete data. And even complete data does not illuminate variables such as why women may work in a field that pays less….(More)”.

This co-op lets patients monetize their own health data

Curated on Posted on by Stefaan Verhulst

Eillie Anzilotti at FastCompany: “Diagnosed with juvenile arthritis as a kid, Jen Horonjeff knew she wanted to enter the medical field to help others navigate the healthcare system in America. She went on to get her Ph.D. in environmental medicine, hoping to better understand the social and contextual factors that surround the strict biology of a disease. Throughout her studies, though, something began to irk her. In both the practice of and research around medicine, she found that the perspective of the patient was all but nonexistent.

So in 2016, Horonjeff, along with her co-founder Ronnie Sharpe, who grew up with cystic fibrosis and founded a social network for others with the diseases, started Savvy, a platform to bridge the gap between patients and practitioners. The platform officially launched in the fall of 2017, and recently became a public benefit corporation….

But Savvy also tackles another imbalance in the patient-practitioner relationship. Whenever a patient is seen by a doctor, or enters their information into a medical app or platform, they’re providing the health community an invaluable resource: their data. But they’re not getting compensated for it. To ensure that patients participating in Savvy get something in return, Horonjeff and Sharpe set their platform up as a cooperative, owned collectively by the patients that contribute to it. Any patient who wants to become a Savvy member pays a buy-in fee of $34, which establishes them as a member of the co-op (the fee is waived for patients who cannot afford it, and some other members give more than the base membership fee to subsidize others). “When people become members, they have a voice in what we do, and they also share in our profits,” Horonjeff says….(More)”.

Following Fenno: Learning from Senate Candidates in the Age of Social Media and Party Polarization

Curated on Posted on by Stefaan Verhulst

David C.W. Parker  at The Forum: “Nearly 40 years ago, Richard Fenno published Home Style, a seminal volume explaining how members of Congress think about and engage in the process of representation. To accomplish his task, he observed members of Congress as they crafted and communicated their representational styles to the folks back home in their districts. The book, and Fenno’s ensuing research agenda, served as a clarion call to move beyond sophisticated quantitative analyses of roll call voting and elite interviews in Washington, D.C. to comprehend congressional representation. Instead, Fenno argued, political scientists are better served by going home with members of Congress where “their perceptions of their constituencies are shaped, sharpened, or altered” (Fenno 1978, p. xiii). These perceptions of constituencies fundamentally shape what members of Congress do at home and in Washington. If members of Congress are single-minded seekers of reelection, as we often assume, then political scientists must begin with the constituent relationship essential to winning reelection. Go home, Fenno says, to understand Congress.

There are many ways constituency relationships can be understood and uncovered; the preferred method for Fenno is participant observation, which he variously terms as “soaking and poking” or “just hanging around.” Although it sounds easy enough to sit and watch, good participant observation requires many considerations (as Fenno details in a thorough appendix to Home Style). In this appendix, and in another series of essays, Fenno grapples forthrightly with the tough choices researchers must consider when watching and learning from politicians.

In this essay, I respond to Fenno’s thought-provoking methodological treatise in Home Style and the ensuing collection of musings he published as Watching Politicians: Essays on Participant Observation. I do so for three reasons: First, I wish to reinforce Fenno’s call to action. As the study of political science has matured, it has moved away from engaging with politicians in the field across the various sub-fields, favoring statistical analyses. “Everyone cites Fenno, but no one does Fenno,” I recently opined, echoing another scholar commenting on Fenno’s work (Fenno 2013, p. 2; Parker 2015, p. 246). Unfortunately, that sentiment is supported by data (Grimmer 2013, pp. 13–19; Curry 2017). Although quantitative and formal analyses have led to important insights into the study of political behavior and institutions, politics is as important to our discipline as science. And in politics, the motives and concerns of people are important to witness, not just because they add complexity and richness to our stories, but because they aid in theory generation.1 Fenno’s study was exploratory, but is full of key theoretical insights relevant to explaining how members of Congress understand their constituencies and the ensuing political choices they make.

Second, to “do” participant observation requires understanding the choices the methodology imposes. This necessitates that those who practice this method of discovery document and share their experiences (Lin 2000). The more the prospective participant observer can understand the size of the choice set she faces and the potential consequences at each decision point in advance, the better her odds of avoiding unanticipated consequences with both immediate and long-term research ramifications. I hope that adding my cumulative experiences to this ongoing methodological conversation will assist in minimizing both unexpected and undesirable consequences for those who follow into the field. Fenno is open about his own choices, and the difficult decisions he faced as a participant observer. Encouraging scholars to engage in participant observation is only half the battle. The other half is to encourage interested scholars to think about those same choices and methodological considerations, while acknowledging that context precludes a one-size fits all approach. Fenno’s choices may not be your choices – and that might be just fine depending upon your circumstances. Fenno would wholeheartedly agree.

Finally, Congress and American politics have changed considerably from when Fenno embarked on his research in Home Style. At the end of his introduction, Fenno writes that “this book is about the early to mid-1970s only. These years were characterized by the steady decline of strong national party attachments and strong local party organizations. … Had these conditions been different, House members might have behaved differently in their constituencies” (xv). Developments since Fenno put down his pen include political parties polarizing to an almost unprecedented degree, partisan attachments strengthening among voters, and technology emerging to change fundamentally how politicians engage with constituents. In light of this evolution of political culture in Washington and at home, it is worth considering the consequences for the participant-observation research approach. Many have asked me if it is still possible to do such work in the current political environment, and if so, what are the challenges facing political scientists going into the field? This essay provides some answers.

I proceed as follows: First, I briefly discuss my own foray into the world of participant observation, which occurred during the 2012 Senate race in Montana. Second, I consider two important methodological considerations raised by Fenno: access and participation as an observer. Third, I relate these two issues to a final consideration: the development of social media and the consequences of this for the participant observation enterprise. Finally, I show the perils of social science divorced from context, as demonstrated by the recent Stanford-Dartmouth mailer scandal. I conclude with not just a plea for us to pick up where Fenno has left off, but by suggesting that more thinking like a participant observer would benefit the discipline as whole by reminding us of our ethical obligations as researchers to each other, and to the political community that we study…(More)”.

Understanding Data Use: Building M&E Systems that Empower Users

Curated on Posted on by Stefaan Verhulst

Paper by Susan Stout, Vinisha Bhatia, and Paige Kirby: “We know that Monitoring and Evaluation (M&E) aims to support accountability and learning, in order to drive better outcomes…The paper, Understanding Data Use: Building M&E Systems that Empower Users, emphasizes how critical it is for decision makers to consider users’ decision space – from the institutional all the way to technical levels – in achieving data uptake.

Specifically, we call on smart mapping of this decision space – what do intended M&E users need, and what institutional factors shape those needs? With this understanding, we can better anticipate what types of data are most useful, and invest in systems to support data-driven decision making and better outcomes.

Mapping decision space is essential to understanding M&E data use. And as we’ve explored before, the development community has the opportunity to unlock existing resources to access more and better data that fits the needs of development actors to meet the SDGs….(More)”.