Paper by Susan Ariel Aaronson: “This paper argues that if trade policymakers truly want to achieve data free flow with trust, they must address user concerns beyond privacy. Survey data reveals that users are also anxious about online harassment, malware, censorship and disinformation. The paper focuses on three such problems, specifically, internet shutdowns, censorship and ransomware (a form of malware), each of which can distort trade and make users feel less secure online. Finally, the author concludes that trade policymakers will need to rethink how they involve the broad public in digital trade policymaking if they want digital trade agreements to facilitate trust….(More)”.
Feedback Loops in Open Data Ecosystems
Paper by Daniel Rudmark and Magnus Andersson: “Public agencies are increasingly publishing open data to increase transparency and fuel data-driven innovation. For these organizations, maintaining sufficient data quality is key to continuous re-use but also heavily dependent on feedback loops being initiated between data publishers and users. This paper reports from a longitudinal engagement with Scandinavian transportation agencies, where such feedback loops have been successfully established. Based on these experiences, we propose four distinct types of data feedback loops in which both data publishers and re-users play critical roles…(More)”.
Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city
Paper by Caio Pieroni, Mariana Giannotti, Bianca B.Alves, and Renato Arbex: “Smart card data (SCD) allow analyzing mobility at a fine level of detail, despite the remaining challenges such as identifying trip purpose. The use of the SCD may improve the understanding of transit users’ travel patterns from precarious settlements areas, where the residents have historically limited access to opportunities and are usually underrepresented in surveys. In this paper, we explore smart card data mining to analyze the temporal and spatial patterns of the urban transit movements from residents of precarious settlements areas in São Paulo, Brazil, and compare the similarities and differences in travel behavior with middle/high-income-class residents. One of our concerns is to identify low-paid employment travel patterns from the low-income-class residents, that are also underrepresented in transportation planning modeling due to the lack of data. We employ the k-means clustering algorithm for the analysis, and the DBSCAN algorithm is used to infer passengers’ residence locations. The results reveal that most of the low-income residents of precarious settlements begin their first trip before, between 5 and 7 AM, while the better-off group begins from 7 to 9 AM. At least two clusters formed by commuters from precarious settlement areas suggest an association of these residents with low-paid employment, with their activities placed in medium / high-income residential areas. So, the empirical evidence revealed in this paper highlights smart card data potential to unfold low-paid employment spatial and temporal patterns….(More)”.
Statistics and Data Science for Good
Introduction to Special Issue of Chance by Caitlin Augustin, Matt Brems, and Davina P. Durgana: “One lesson that our team has taken from the past 18 months is that no individual, no team, and no organization can be successful on their own. We’ve been grateful and humbled to witness incredible collaboration—taking on forms of resource sharing, knowledge exchange, and reimagined outcomes. Some advances, like breakthrough medicine, have been widely publicized. Other advances have received less fanfare. All of these advances are in the public interest and demonstrate how collaborations can be done “for good.”
In reading this issue, we hope that you realize the power of diverse multidisciplinary collaboration; you recognize the positive social impact that statisticians, data scientists, and technologists can have; and you learn that this isn’t limited to companies with billions of dollars or teams of dozens of people. You, our reader, can get involved in similar positive social change.
This special edition of CHANCE focuses on using data and statistics for the public good and on highlighting collaborations and innovations that have been sparked by partnerships between pro bono institutions and social impact partners. We recognize that the “pro bono” or “for good” field is vast, and we welcome all actors working in the public interest into the big tent.
Through the focus of this edition, we hope to demonstrate how new or novel collaborations might spark meaningful and lasting positive change in communities, sectors, and industries. Anchored by work led through Statistics Without Borders and DataKind, this edition features reporting on projects that touch on many of the United Nations Sustainable Development Goals (SDGs).
Pro bono volunteerism is one way of democratizing access to high-skill, high-expense services that are often unattainable for social impact organizations. Statistics Without Borders (founded in 2008), DataKind (founded in 2012), and numerous other volunteer organizations began with this model in mind: If there was an organizing or galvanizing body that could coordinate the myriad requests for statistical, data science, machine learning, or data engineering help, there would be a ready supply of talented individuals who would want to volunteer to see those projects through. Or, put another way, “If you build it, they will come.”
Doing pro bono work requires more than positive intent. Plenty of well-meaning organizations and individuals charitably donate their time, their energy, their expertise, only to have an unintended adverse impact. To do work for good, ethics is an important part of the projects. In this issue, you’ll notice the writers’ attention to independent review boards (IRBs), respecting client and data privacy, discussing ethical considerations of methods used, and so on.
While no single publication can fully capture the great work of pro bono organizations working in “data for good,” we hope readers will be inspired to contribute to open source projects, solve problems in a new way, or even volunteer themselves for a future cohort of projects. We’re thrilled that this special edition represents programs, partners, and volunteers from around the world. You will learn about work that is truly representative of the SDGs, such as international health organizations’ work in Uganda, political justice organizations in Kenya, and conservationists in Madagascar, to name a few.
Several articles describe projects that are contextualized with the SDGs. While achieving many goals is interconnected, such as the intertwining of economic attainment and reducing poverty, we hope that calling out key themes here will whet your appetite for exploration.
- • Multiple articles focused on tackling aspects of SDG 3: Ensuring healthy lives and promoting well-being for people at all ages.
- • An article tackling SDG 8: Promote sustained, inclusive, and sustainable economic growth; full and productive employment; and decent work for all.
- • Several articles touching on SDG 9: Build resilient infrastructure; promote inclusive and sustainable industrialization, and foster innovation; one is a reflection on building and sustaining free and open source software as a public good.
- • A handful of articles highlighting the needs for capacity-building and systems-strengthening aligned to SDG 16: Promote peaceful and inclusive societies for sustainable development; provide access to justice for all; and build effective, accountable, and inclusive institutions at all levels.
- • An article about migration along the southern borders of the United States addressing multiple issues related to poverty (SDG 1), opportunity (SDG 10), and peace and justice (SDG 16)….(More)”
Wiki (POCC) Authorship: The Case for An Inclusive Copyright
Paper by Sunimal Mendis: “Public open collaborative creation (POCC) constitutes an innovative form of collaborative authorship that is emerging within the digital humanities. At present, the use of the POCC (Wiki) model can be observed in many online creation projects the best known examples being Wikipedia and free-open source software (FOSS). This paper presents the POCC model as a new archetype of authorship that is founded on a creation ideology that is collective and inclusive. It posits that the POCC authorship model challenges the existing individualistic conception of authorship in exclusivity-based copyright law. Based on a comparative survey of the copyright law frameworks on collaborative authorship in France, the UK and the US, the paper demonstrates the inability of the existing framework of exclusivity-based copyright law (including copyleft licenses which are based on exclusive copyright) to give adequate legal expression to the relationships between co-authors engaged in collaborative creation within the POCC model. It proposes the introduction of an ‘inclusive’ copyright to the copyright law toolbox which would be more suited for giving legal expression to the qualities of inclusivity and dynamism that are inherent in these relationships. The paper presents an outline of the salient features of the proposed inclusive copyright, its application and effects. It concludes by outlining the potential of the ‘inclusive’ copyright to extend to other fields of application such as traditional cultural expression (TCE)….(More)”
Old Dog, New Tricks: Retraining and the Road to Government Reform
Essay by Beth Noveck: “…To be sure, one strategy for modernizing government is hiring new people with fresh skills in the fields of technology, data science, design, and marketing. Today, only 6 percent of the federal workforce is under 30 and, if age is any proxy for mastery of these in-demand new skills, then efforts by non-profits such as the Partnership for Public Service and the Tech Talent Project to attract a younger generation to work in the public sector are crucial. But we will not reinvent government fast enough through hiring alone.
The crucial and overlooked mechanism for improving government effectiveness is, therefore, to change how people work by training public servants across departments to use data and collective intelligence at each stage of the problem-solving process to foster more informed decision-making, more innovative solutions to problems, and more agile implementation of what works. All around the world we have witnessed how, when public servants work differently, government solves problems better.
Jonathan Wachtel, the lone city planner in Lakewood, Colorado, a suburb of Denver, has been able to undertake 500 sustainability projects because he knows how to collaborate and codesign with a network of 20,000 residents. When former Mayor of New Orleans Mitch Landrieu launched an initiative to start using data and resident engagement to address the city’s abysmal murder rate, that effort led to a 25 percent reduction in homicides in two years and a further decline to its lowest levels in 50 years by 2019. Because Samir Brahmachari, former Secretary, Department of Scientific and Industrial Research, of the government of India, turned to crowdsourcing and engaged the assistance of 7,900 contributors, he was able to identify six already-approved drugs that showed promised in the fight against tuberculosis….(More)”.
The Cost-Benefit Fallacy: Why Cost-Benefit Analysis Is Broken and How to Fix It
Paper by Bent Flyvbjerg and Dirk W. Bester: “Most cost-benefit analyses assume that the estimates of costs and benefits are more or less accurate and unbiased. But what if, in reality, estimates are highly inaccurate and biased? Then the assumption that cost-benefit analysis is a rational way to improve resource allocation would be a fallacy. Based on the largest dataset of its kind, we test the assumption that cost and benefit estimates of public investments are accurate and unbiased. We find this is not the case with overwhelming statistical significance. We document the extent of cost overruns, benefit shortfalls, and forecasting bias in public investments. We further assess whether such inaccuracies seriously distort effective resource allocation, which is found to be the case. We explain our findings in behavioral terms and explore their policy implications. Finally, we conclude that cost-benefit analysis of public investments stands in need of reform and we outline four steps to such reform…(More)”.
From Ethics Washing to Ethics Bashing: A View on Tech Ethics from Within Moral Philosophy
Paper by Elettra Bietti: “The word ‘ethics’ is overused in technology policy circles. Weaponized in support of deregulation, self-regulation or hands-off governance, “ethics” is increasingly identified with technology companies’ self-regulatory efforts and with shallow appearances of ethical behavior. So-called “ethics washing” by tech companies is on the rise, prompting criticism and scrutiny from scholars and the tech community at large. In parallel to the growth of ethics washing, its condemnation has led to a tendency to engage in “ethics bashing.” This consists in the trivialization of ethics and moral philosophy now understood as discrete tools or pre-formed social structures such as ethics boards, self-governance schemes or stakeholder groups.
The misunderstandings underlying ethics bashing are at least three-fold: (a) philosophy is understood in opposition and as alternative to law, political representation and social organizing; (b) philosophy and “ethics” are seen as a formalistic methodology, vulnerable to instrumentalization and abuse, and thus ontologically flawed; and (c) engagement in moral philosophy is downplayed and portrayed as mere “ivory tower” intellectualization of complex problems that need to be dealt with through alternative and more practical methodologies.
This essay argues that the rhetoric of ethics and morality should not be reductively instrumentalized, either by the industry in the form of “ethics washing,” or by scholars and policy-makers in the form of “ethics bashing.” Grappling with the role of philosophy and ethics requires moving beyond simplification and seeing ethics as a mode of inquiry that facilitates the evaluation of competing tech policy strategies. In other words, we must resist narrow reductivism of moral philosophy as instrumentalized performance and renew our faith in its intrinsic moral value as a mode of knowledge-seeking and inquiry. Far from mandating a self-regulatory scheme or a given governance structure, moral philosophy in fact facilitates the questioning and reconsideration of any given practice, situating it within a complex web of legal, political and economic institutions. Moral philosophy indeed can shed new light on human practices by adding needed perspective, explaining the relationship between technology and other worthy goals, situating technology within the human, the social, the political. It has become urgent to start considering technology ethics also from within and not only from outside of ethics….(More)”.
Greece used AI to curb COVID: what other nations can learn
Editorial at Nature: “A few months into the COVID-19 pandemic, operations researcher Kimon Drakopoulos e-mailed both the Greek prime minister and the head of the country’s COVID-19 scientific task force to ask if they needed any extra advice.
Drakopoulos works in data science at the University of Southern California in Los Angeles, and is originally from Greece. To his surprise, he received a reply from Prime Minister Kyriakos Mitsotakis within hours. The European Union was asking member states, many of which had implemented widespread lockdowns in March, to allow non-essential travel to recommence from July 2020, and the Greek government needed help in deciding when and how to reopen borders.
Greece, like many other countries, lacked the capacity to test all travellers, particularly those not displaying symptoms. One option was to test a sample of visitors, but Greece opted to trial an approach rooted in artificial intelligence (AI).
Between August and November 2020 — with input from Drakopoulos and his colleagues — the authorities launched a system that uses a machine-learning algorithm to determine which travellers entering the country should be tested for COVID-19. The authors found machine learning to be more effective at identifying asymptomatic people than was random testing or testing based on a traveller’s country of origin. According to the researchers’ analysis, during the peak tourist season, the system detected two to four times more infected travellers than did random testing.
The machine-learning system, which is among the first of its kind, is called Eva and is described in Nature this week (H. Bastani et al. Nature https://doi.org/10.1038/s41586-021-04014-z; 2021). It’s an example of how data analysis can contribute to effective COVID-19 policies. But it also presents challenges, from ensuring that individuals’ privacy is protected to the need to independently verify its accuracy. Moreover, Eva is a reminder of why proposals for a pandemic treaty (see Nature 594, 8; 2021) must consider rules and protocols on the proper use of AI and big data. These need to be drawn up in advance so that such analyses can be used quickly and safely in an emergency.
In many countries, travellers are chosen for COVID-19 testing at random or according to risk categories. For example, a person coming from a region with a high rate of infections might be prioritized for testing over someone travelling from a region with a lower rate.
By contrast, Eva collected not only travel history, but also demographic data such as age and sex from the passenger information forms required for entry to Greece. It then matched those characteristics with data from previously tested passengers and used the results to estimate an individual’s risk of infection. COVID-19 tests were targeted to travellers calculated to be at highest risk. The algorithm also issued tests to allow it to fill data gaps, ensuring that it remained up to date as the situation unfolded.
During the pandemic, there has been no shortage of ideas on how to deploy big data and AI to improve public health or assess the pandemic’s economic impact. However, relatively few of these ideas have made it into practice. This is partly because companies and governments that hold relevant data — such as mobile-phone records or details of financial transactions — need agreed systems to be in place before they can share the data with researchers. It’s also not clear how consent can be obtained to use such personal data, or how to ensure that these data are stored safely and securely…(More)”.
Less complex language, more participation: how consultation documents shape participatory patterns
Paper by Simon Fink, Eva Ruffing, Tobias Burst & Sara Katharina Chinnow: “Consultations are thought to increase the legitimacy of policies. However, this reasoning only holds if stakeholders really participate in the consultations. Current scholarship offers three explanations for participation patterns: Institutional rules, policy characteristics, and interest group resources determine participation. This article argues that additionally the linguistic complexity of consultation documents influences participation. Complex language deters potential participants, because it raises the costs of participation. A quantitative analysis of the German consultation of electricity grids lends credibility to the argument: If the description of a power line is simplified between two consultation rounds, the number of contributions mentioning that power line increases. This result contributes to our understanding of unequal participation patterns, and the institutional design of participatory procedures. If we think that legitimacy is enhanced by broad participation, then language of the documents matters….(More)”.