Colloquium

The Colloquium Series serves to highlight exemplary work on improving access to opportunity for disadvantaged and marginalized groups through a monthly online talk series. You can find recordings for our previous talks on our Youtube channel and you can subscribe to our calendar.

Please reach out to the organizers at organizers@md4sg.com if you have any questions or suggestions for future colloquium speakers.

Elissa Redmiles, Max Planck Institute for Software Systems

Date: Friday, December 9th 2022, 12 PM ET / 5 PM WAT / 6 PM CET

Dr. Elissa M. Redmiles is a faculty member and research group leader at the Max Planck Institute for Software Systems and a Visiting Scholar at the Berkman Klein Center for Internet & Society at Harvard University. She uses computational, economic, and social science methods to understand users' security, privacy, and online safety-related decision-making processes and remedy inequities in those processes. Her work has been recognized with multiple paper awards at USENIX Security, ACM CCS and ACM CHI and has been featured in popular press publications such as the New York Times, Wall Street Journal, Scientific American, Rolling Stone, Wired, Business Insider, and CNET.

Sex, Work, and Technology: Lessons for Internet Governance & Digital Safety

Sex workers sit at the intersection of multiple marginalized identities and make up a sizable workforce: the UN estimates that at least 42 million sex workers are conducting business across the globe. Sex workers face a unique and significant set of digital, social, political, legal, and safety risks; yet their digital experiences have received little study in the CS and HCI literature. In this talk we will review findings from a 2-year long study examining how sex workers who work in countries where sex work is legal (Germany, Switzerland, the UK) use technology to conduct business and how they have developed digital strategies for staying safe online and offline. We will then describe how these findings can inform broader conversations around internet governance, digital discrimination, and safety protections for other marginalized and vulnerable users whose experiences bisect the digital and physical.

Samara Trilling and Johanna Monge, Justfix

Discussant: Daniel Waldinger, NYU

Date: Friday, May 27th 2022, 1 PM ET / 6 PM WAT / 7 PM CET

Youtube Live: Link

Samara Trilling (she/her) is a software engineer and policy technologist. As a fellow at the Aspen Tech Policy Hub, Samara wrote policy recommendations for machine learning regulation. Previously, Samara built city planning software at Sidewalk Labs and Google Fiber. Samara has a degree in computer science from Columbia University with a specialization in artificial intelligence. Samara loves hiking, collects books on the history of computing, and plans to build a tiny house and tow it around with her electric truck.

Johanna Monge (she/her/ella), a lifelong Queens gal, daughter of Peruvian immigrants and tenant organizer, integrates her passion for transformative justice into all her work. She co-founded Astoria Tenant Union at the start of the COVID-19 pandemic utilizing the skills she gained from organizing in Inwood and Washington Heights while at the Metropolitan Council on Housing. She graduated from Clark University with an honors degree in Political Science and Urban Development.

Daniel Waldinger is an empirical economist working at the intersection of market design, industrial organization, and public finance. His research has focused on the design of waiting lists for public housing and organ transplants. Daniel joined the NYU economics department as an assistant professor in Fall 2019 after a post-doc year at the NYU Furman Center. Daniel holds a PhD in Economics from MIT and a Bachelor's Degree from the University of Chicago.

Type three social change: tech's role in tenant organizing at JustFix

JustFix's work building anti-eviction web apps early in the pandemic led us to change our philosophy on how tech can best serve tenants. We experienced the mismatch between legislative and software dev cycles (not the way you'd think!), the limits of organizing within the system, and the power of open data to put external pressure on bad landlords and brokers. In this talk, we'll preview some of JustFix's new tool ideas that fit this "type three" paradigm of working outside existing systems to push for housing justice. The discussion will be moderated by Daniel Waldinger in bridging social, economic, and policy perspectives on public housing allocation.

Saiph Savage, Northeastern University

Date: Friday, April 29th 2022, 12 PM ET / 5 PM WAT / 6 PM CET

Youtube Live: Link

Saiph Savage is an Assistant Professor at Northeastern University in the Khoury College of Computer Sciences where she conducts research in the intersection of Human Computer Interaction, A.I., and Civic Technology. She is one of the 35 Innovators under 35 by the MIT Technology Review, a Google Anita Borg Scholarship recipient, and a fellow at the Center for Democracy & Technology. Her work has been covered in the BBC, Deutsche Welle, the Economist, and the New York Times, as well as published in top venues such as ACM CHI, CSCW, and the Web Conference, where she has also won honorable mentions and impact awards. Dr. Savage has been awarded grants from the National Science Foundation, the United Nations, diverse industry actors, and has also formalized new collaborations with Federal and local Governments where she is driving them to adopt Human Centered Design and A.I. to deliver better experiences and government services for citizens. Dr. Savage students have obtained fellowships and internships in industry (Facebook Research, Twitch Research, Twitter, Snap, and Microsoft Research) as well as academia (Oxford Internet Institute). Saiph holds a bachelor's degree in Computer Engineering from the National Autonomous University of Mexico (UNAM), and a master's and Ph.D. in Computer Science from the University of California, Santa Barbara (UCSB). Dr. Savage has also worked at the University of Washington, and Carnegie Mellon University (CMU). Additionally, Dr. Savage has been a tech worker at Microsoft Bing, Intel Labs, and a crowd research worker at Stanford.

The Future of A.I. for Social Good

The A.I. Industry has powered a futuristic reality of self-driving cars and voice assistants to help us with almost any need. However, the A.I. Industry has also created systematic challenges. For instance, while it has led to platforms where workers label data to improve machine learning algorithms, my research has uncovered that these workers earn less than minimum wage. We are also seeing the surge of A.I. algorithms that privilege certain populations and racially exclude others. If we were able to fix these challenges we could create greater societal justice and enable A.I. that better addresses people's needs, especially groups we have traditionally excluded. In this talk, I will discuss some of these urgent global problems that my research has uncovered from the A.I. Industry. I will present how we can start to address these problems through my proposed "A.I. For Good" framework. My framework uses value sensitive design to understand people's values and rectify harm. I will present case-studies where I use this framework to design A.I. systems that improve the labor conditions of the workers operating behind the scenes in our A.I. industry. I conclude by presenting a research agenda for studying the impact of A.I. in society; and researching effective socio-technical solutions in favor of workers.

Ingmar Weber, Qatar Computing Research Institute

Date: Tuesday, February 22nd 2022, 12 PM ET / 5 PM WAT / 6 PM CET

Youtube Live: Link

Ingmar Weber is the Research Director for Social Computing at the Qatar Computing Research Institute (QCRI). His interdisciplinary research looks at what online user-generated data can tell us about the offline world and society at large. Working closely with sociologists and demographers he has pioneered the use of online advertising data for complementing official statistics on international migration, digital gender gaps, and poverty. Analyses performed by his team have been used to improve operations by UN agencies and NGOs ranging from Colombia to the Philippines. Prior to joining QCRI, Ingmar was a researcher at Yahoo Research Barcelona. As an undergraduate he studied mathematics at the University of Cambridge before pursuing a PhD at the Max-Planck Institute for Computer Science. He is an ACM Distinguished Member.

Collected for Profit, Repurposed for Social Good: Using Advertising Data to Monitor International Development

Most of the big internet companies, such as Facebook, Google, or Twitter, generate their revenue from targeted advertising. To offer advertisers with advanced targeting capabilities, these companies collect large amounts of user data to build elaborate profiles. Based on these profiles an advertiser can then choose to target only, say, female Facebook users living in Norte de Santander who are aged 18-24, who used to live in Venezuela, and who have access to an iOS device. To help advertisers in planning their advertising campaigns and the related budget needs, the advertising platforms provide so-called audience estimates on how many of their users match the provided targeting criteria. In the example above, Facebook estimates that there are 1,800 matching monthly active users (as of Mid-December 2021). In this talk, I'll describe how, in close collaboration with different UN agencies, we're tapping into these audience estimates to (i) monitor international migration, (ii) track digital gender gaps, and (iii) map wealth inequalities. We consistently find that, despite fake profiles, and noise in the inference algorithms, data derived from the advertising platforms can provide valuable information that is complementary to other data sources. So data collected for the explicit purpose of selling advertisements and profit maximization can be repurposed for social good. At the same time, our work shows the risk of identifying vulnerable groups, rather than individuals, which is often not adequately considered in discussions focused on individual privacy. Furthermore, it raises questions on what the relationship between internet giants and statistical offices should be.

Maria Rodriguez, University at Buffalo

Date: Friday, May 7th 2021, 1 PM ET / 6 PM WAT / 7 PM CET

Dr. Rodriguez is an Assistant Professor at the University at Buffalo's School of Social Work (SUNY). She is also a faculty associate at the BerkmanKlein Center for Internet and Society, a faculty fellow at the Center for Democracy and Technology, and a member of the Twitter Academic Research Advisory Board. Her research is at the intersection of applied demography, computational social science, and social policy. The first line of research examines the ethical implications of algorithmic decision-making in human services, child welfare in particular. The second line of research looks at the lived experience of marginalized communities as self-reported on social media. The through line between the two concerns the methods involved: she identifies as a methodologist in social work spaces, in as far as her substantive focus is how computational methods can support using large, unstructured data to scale social work interventions. For that reason Rodriguez founded the caretLAB, the first lab housed in a school of social work to her knowledge dedicated to computational research methods in the public interest. Her lab works with technologists across fields of inquiry to leverage cutting edge, innovative technologies to develop, implement, and evaluate scaled interventions.

The Limits of Moral Philosophy in dealing with Algorithmic Bias

A great number of academic and industry initiatives seek to mitigate what are increasingly understood as ethical concerns in the development, implementation, and evaluation of algorithms in social contexts. Often, when confronted with questions of ethics within these socio-technical systems, engineers ,data scientists, and others turn to the field of Philosophy for help in trying to decide the balance between the right thing to do and what we can do. In this talk, Dr. Rodriguez presents her arguments for why most strands of moral philosophy are ill equipped to help us deal with the realities of algorithmic bias. Her arguments use evidence gleaned from years of professional experience in human services, her study of moral philosophy, as well as arguments put forth by a school of Black Philosophers. The aim of the talk is to introduce the audience to more contextual forms of philosophizing, ones grounded in the realities of marginalized existence.

Vukosi Marivate, University of Pretoria, South Africa

Date: Friday, April 9th 2021, 11:00 - 12:15 PM ET / 5-6:15 PM CET/WAT)

Youtube Live: Link

Vukosi Marivate holds a PhD in Computer Science (Rutgers University, as Fulbright Science and Technology Fellow) and MSc & BSc in Electrical Engineering (Wits University). Dr Marivate is based at the University of Pretoria as the UP ABSA Chair of Data Science. He works on developing Machine Learning/Artificial Intelligence methods to extract insights from data. A large part of his work over the last few years has been in the intersection of Machine Learning and Natural Language Processing (NLP). This has led to research outputs focused on how we can better improve low resource language tools, especially for African Languages. This has included creating new software libraries, new research approaches for robust NLP and encouraging the development of datasets for African languages. As part of his vision for the ABSA Data Science chair, Vukosi is interested in Data Science for Social Impact (https://dsfsi.github.io/), using local challenges as a springboard for research. In this area, Vukosi has worked on projects in science, energy, public safety and utilities. Vukosi is cofounder of the Deep Learning Indaba, the largest Machine Learning/Artificial Intelligence workshop on the African continent, aiming to strengthen African Machine Learning.

Coming to grips with the reality of Data Science - It's people all the way down

As practising Data Science researchers and practitioners, the COVID-19 pandemic has highlighed both the need for data driven decision making and the reality of what it really takes to get to that point. It is not only about throwing data + model at a problem. It is about understanding the environment that one is in and then strategising on what might best work for that environment. In this talk I look back at some of the work we have done within responding to different challenges within both Data Science and Natural Language Processing. I place at the center people and how they are the important piece in our practice.

Caterina Calsamiglia, Institute of Political Economy and Governance, Barcelona

Date: Friday, March 19th 2021, 12:00 - 1:15 PM ET / 6-7:15 PM CET/WAT

Youtube Live: Link

Caterina Calsamiglia is an ICREA Research Professor at IPEG. She got her PhD from the Department of Economics at Yale University. Her research focuses on market design and public policy design with a particular interest in education. Her work includes theoretical, experimental and empirical analysis. In the recent years she has been leading a research initiative to help develop Pentabilities in life-long learning processes. To assist the Pentabilities initiative she co-founded Pentabilities app, a tool to track analyse the development of your pentabilities.

The design of university entrance exams and its implications for gender gaps (joint with Andreu Arenas)

We study the effects of a reform that increased the stakes of the national exam at the end of high school for university enrolment in Spain. We find a negative effect of the reform on female test scores, driven by a worse performance in the exams for which the stakes increase the most, and driven by students expected to be top performers. The effect on test scores translates into significant changes in students' allocation to college: female enrolment in the most selective degrees declines, together with expected earnings.

Tina Eliassi-Rad, Northeastern University

Date: Friday, February 19 2021, 12:00-1:15 PM ET

Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University in Boston, MA. She is also a core faculty member at Northeastern's Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is at the intersection data mining, machine learning, and network science. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awardees); and has given over 200 invited talks and 14 tutorials. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, Tina served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (a.k.a. KDD, which is the premier conference on data mining) and as the program co-chair for the International Conference on Network Science (a.k.a. NetSci, which is the premier conference on network science). In 2020, she served as the program co-chair for the International Conference on Computational Social Science (a.k.a. IC2S2, which is the premier conference on computational social science). Tina received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010; and became a Fellow of the ISI Foundation in Turin Italy in 2019.

What can science do for democracy?

We will discuss the following questions. What is democratic backsliding? What makes a democracy stable? Which processes potentially lead to instability of a democratic system? How can complexity science help us understand and mitigate democratic backsliding? The talk is based on this paper and this paper.

Francisco Marmolejo, The Qatar Foundation

Date: January 22 2021, 11 AM - 12:15 PM ET (5-6:15 PM WAT/CET)

Youtube Live: Link

Francisco Marmolejo is since February 2020, Education Advisor of Qatar Foundation, based in Doha, Qatar, providing support and advice towards the enhancement of the overall education strategy of QF and its unique eco-system of innovative education. Previously (2012-20), he worked at the World Bank, where he served as the Global Higher Education Coordinator and Lead, based in Washington, DC., and more recently as Lead Higher Education Specialist for India and South Asia, based in Delhi. From 1995 to 2002, he served as founding Executive Director of the Consortium for North American Higher Education Collaboration, a network of more than 160 universities mainly from Canada, USA and Mexico, based at the University of Arizona, where he also worked as Assistant Vice President, Affiliated Researcher at the Center for the Study of Higher Education, and Affiliate Faculty at the Center for Latin American Studies. Previously, he has been American Council on Education Fellow at the University of Massachusetts, Academic Vice President of the University of the Americas in Mexico, and International Consultant at OECD in Paris. He has received honorary doctorate degrees from his Alma Mater, the University of San Luis Potosi, and the University of Guadalajara in Mexico.

Bridging research and practice in the post-pandemic world: challenges for higher education institutions

Colleges and universities are among those that have been most affected by the COVID-19 pandemic. Even with a potential end to the ongoing global restrictions in sight, the manner in which these institutions operate is likely to be permanently altered as a result of the significant impact of the virus. Most notably, a more traditional, siloed approach to research will play a key role in effectively meeting the needs of local communities. Additionally, the existing teaching-learning paradigm will need to be reimagined, keeping in mind the transformation of the educational landscape. This session will address key global trends in the education sector, as well as propose some ideas on how universities can adopt a more proactive approach to cope with these changes.

Moon Duchin, Tufts University

Date: Friday, November 13 2020, 1:30-2:45 PM ET

Moon Duchin is a mathematician at Tufts University with affiliations in Science, Technology, and Society; Race, Colonialism, and Diaspora; and the Tisch College of Civic Life. She runs the MGGG Redistricting Lab at Tisch. Moon's mathematical background is in geometric group theory and low-dimensional topology. Her lab works on data science for civil rights, incorporating mathematics, computing, geography, law, and policy in its research program. Recent projects include gerrymandering metrics, Voting Rights Act analysis, and disclosure avoidance in the Census.

Differential privacy comes to the U.S. Census

The Census is doing something new and interesting this year: implementing a differentially private algorithm called TopDown, so named because it works its way down the levels of a geographic hierarchy. The Census is already facing lots of scrutiny of its ability to provide a reliable count for marginalized communities under unprecedented political pressure from the White House and in pandemic conditions. Will this privacy protection mechanism result in erasure and undercounting for the same hard-to-count groups? What are the impacts on redistricting and the enforcement of the Voting Rights Act? I'll describe joint work with Aloni Cohen, JN Matthews, Bhushan Suwal, and Peter Wayner, incorporating reconstruction data from Mark Hansen and Denis Kazakov. No prior knowledge of U.S. politics required!

Special Talk: Bistra Dilkina, University of Southern California

Date: Friday, October 30 2020, 12:00-1:15 PM ET

Youtube Live: Link

Reflections on the talk: Medium

Bistra Dilkina is a Gabilan Associate Professor of Computer Science at the University of Southern California. She is also Co-Director of the Center for AI in Society (CAIS). Before that, Dilkina was an Assistant Professor in the College of Computing at the Georgia Institute of Technology and a co-director of the Data Science for Social Good Atlanta summer program. She received her PhD from Cornell University in 2012, and was a Post-Doctoral Associate at the Institute for Computational Sustainability until 2013. Dilkina is one of the junior faculty leaders in the young field of Computational Sustainability, and has co-organized workshops, tutorials, special tracks at AAAI and doctoral consortium on Computational Sustainability. Her work spans discrete optimization, network design, stochastic optimization, and machine learning.

Matthew Weinberg, Princeton University

Date: Friday, October 23 2020, 12:00-1:15 PM ET

Youtube Live: Link

Matthew Weinberg's primary research interest is in Algorithmic Mechanism Design: algorithm design where users have their own incentives. His research focuses on domains such as auctions, cryptocurrencies, and (more recently) "social good" domains. Before joining the faculty at Princeton, Matt spent two years as a postdoc in Princeton's CS Theory group and was a research fellow at the Simons Institute during the fall of 2015 (Economics and Computation) and fall of 2016 (Algorithms and Uncertainty). He completed his Ph.D. at MIT in 2014, where he was advised by Costis Daskalakis, and graduated from Cornell University in 2010 with a B.A. in math.

Personal Anecdote: Healthcare Research through MD4SG as a Theorist

This will be a (mostly) non-technical talk describing my experience working on this project. The paper is motivated by healthcare exchanges, where a designer can't directly set prices, but can regulate which providers enter the market. The two extremes to have in mind are typical employer exchanges in the US (which typically limit entry to few providers) or government exchanges (which typically do not). The paper provides a mathematical model to reason about tradeoffs between these two paradigms. I will describe the model and results from this paper, but mostly focus on my experience learning about healthcare through the MD4SG working group, from the perspective of someone with a CS theory background. The referenced work is joint with Meryem Essaidi (Princeton) and Kira Goldner (Columbia).

Karen Smilowitz, Northwestern University

Date: Friday, September 25 2020, 12:00-1:15 PM ET

Dr. Karen Smilowitz is the James N. and Margie M. Krebs Professor in Industrial Engineering and Management Science at Northwestern University, with a joint appointment in the Operations group at the Kellogg School of Business. Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and nonprofit applications. Dr. Smilowitz is the founder of the Northwestern Initiative on Humanitarian and Nonprofit Logistics. She has been instrumental in promoting the use of operations research within the humanitarian and nonproﬁt sectors through the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering, as well as various media outlets. Dr. Smilowitz is an Associate Editor for Transportation Science and Operations Research.

On the use of operations research methods for the design of school districts

Operations research methods have been used to identify and evaluate solutions to the reconfiguration of public school attendance area boundaries for over fifty years. The talk will explore connections between evolving issues in public education and advances in optimization, computing and geographic information systems, beginning with early work motivated by Supreme Court decisions to desegregate schools. We will also discuss how the limitations of early models and solution approaches hindered their applicability. The years since have seen new research directions to address additional challenges related to the design of school attendance boundaries and leverage emerging advances in technology. The talk will end with a reflection on current issues facing public school districts, including school busing and return-to-school plans amid the COVID-19 pandemic, and the ways in which operations research can be part of these discussions.

Lisa Cook, Michigan State University

Date: Friday, May 15 2020, 12:00-1:15 PM EST

Youtube Live: Link

Lisa D. Cook is Professor of Economics and International Relations at Michigan State University. She earned a Ph.D. in economics from the University of California, Berkeley with fields in macroeconomics and international economics. Previously, she was on the faculty of Harvard University's Kennedy School of Government. Her current research interests include economic growth and development, innovation, and financial crises. She has been published in the American Economic Review, the Journal of Economic Growth, among other journals, and is an associate editor of the Journal of Economic Literature and the Journal of Economic History. Dr. Cook has been funded by NSF and the National Bureau of Economic Research, among others. She is currently Director of the American Economic Association Summer Program and a Research Associate at the National Bureau of Economic Research. She served in the Obama White House at the Council of Economic Advisers from 2011 to 2012.

Mobile Money and the COVID-19 Crisis

Direct payments from the $2.2 trillion CARES Act passed by Congress should get to roughly 80% of Americans - tax filers and those receiving federal assistance - relatively quickly, about three weeks. The other 20% represent some of the most vulnerable people in the economy - the "underbanked" (19% of Americans), those who don't make enough income to file tax returns (or don't file for other reasons), and those for whom addresses have changed or are not readily available. Many are low-wage workers among the record 30 million who filed for unemployment insurance in the last four weeks. This pandemic and resulting human, economic, and financial crises are unfolding at break-neck speed, and bills were and are still due. These payments of $1,200 per adult will be a critical first lifeline for many households and the economy. To minimize the likelihood that an illiquidity crisis becomes a bankruptcy crisis in the coming weeks and months, Congress and the Treasury (with the Federal Reserve and FDIC) must act with all deliberate speed to get people paid now and not months from now. Ninety-six percent of American adults have cell phones or smartphones that could be used to speed up payments to those who are not on IRS or federal assistance rolls. A substantial share of smartphone users already makes payments using mobile platforms. Both the literature and recent experience of financial institutions suggest that the mobile money infrastructure that already can be leveraged to great effect in this crisis, and, more generally, the digital infrastructure of the federal government must be upgraded before the next crisis.

Special Talk: Araba Sey, United Nations University

Date: Friday, April 24 2020, 12:00-1:15 PM EST

Reflections on the talk: Medium

Araba Sey is a Principal Researcher with Research ICT Africa. She studies the relationship between information and communication technologies (ICTs) and society with a focus on socio-economically marginalized groups. She has led several research projects on the impacts of computers, mobile phones and internet access in a variety of international contexts. Her research seeks to understand the factors that underlie different types of digital inequality and explore ways to foster diversity and inclusion. Her most recent work focused on gender equality in digital access, digital skills and leadership in the technology industry.

What Not to Expect (from ICTD research and interventions)

Advances in information and communication technologies (ICTs) have energized governments, global agencies and non-profit organizations to seek applications of these technologies to social and economic development. Researchers and other scholars have followed suit with a slew of research and intervention projects to explore, understand or measure the potential and actual impacts of ICTs. The results so far are, at best, mixed. I will share observations from several years of organizing research on ICTs and development around the world. Research findings and fieldwork experiences provide insights on what not to expect from ICTD research and interventions.

Jim Leape, Stanford University

Date: Friday, March 6 2020, 12:00-1:15 PM EST

Youtube Live: Link

Reflections on the talk: Medium

Jim Leape is the William and Eva Price Senior Fellow in the Stanford Woods Institute for the Environment, and co-director of the Stanford Center for Ocean Solutions. Through research, writing and direct engagement with private and public sector leaders, Jim looks at how to drive large-scale systemic shifts to sustainability. Jim has more than three decades of conservation experience. From 2005 to 2014, he served as Director General of WWF International and leader of the global WWF Network, one of the world's largest conservation organizations. Previously, he worked at the David and Lucile Packard Foundation; WWF-US in Washington, D.C.; and the United Nations Environment Programme in Nairobi, Kenya. Jim serves on several boards. From 2007 to 2017, he was a member of the China Council for International Cooperation in Environment and Development, which advises the Premier of China. He received an A.B. with honors from Harvard College and a J.D. with honors from Harvard Law School.

Harnessing Global Markets for a Transition to Sustainability

Global markets have been vital engines of economic growth. They have also driven massive depletion of natural resources, such as forests and fisheries, and the escalation of greenhouse gas emissions. In recent years, we've seen NGOs multinational companies, and sometimes international organizations and governments, work together to harness global markets to spur a transition to sustainability. In this session we'll explore efforts to address deforestation and overfishing. We'll look at how these collaborations have been able to drive change and consider some of the challenges they face in translating commitments into action and taking action to scale.

Elisa Celis, Yale University

Date: Friday, December 6 2019, 12:30-2:00 PM EST

Link: Youtube Live

Elisa Celis is an Assistant Professor in the Statistics and Data Science department at Yale University. Her research focuses on problems that arise in the context of the Internet and its societal and economic implications. She approaches these problems by using both experimental and theoretical techniques. Her work spans multiple areas including social and computing crowdsourcing, data and network science, and mechanism design and algorithm with a current emphasis on fairness and diversity in artificial intelligence and machine learning. She has published articles in journals such as IEEE Transactions on Network Science and Engineering, Journal of Applied Network Science, Human Computation Journal, Management Science, SIAM Journal on Computing, among others. Before coming to Yale, she worked at the Ecole Polytechnique Federale de Lausanne as a senior research scientist since June of 2014. Celis holds a Ph.D. in Computer Science and Engineering and an M.Sc. in Mathematics, both from the University of Washington.

Mitigating Discrimination in Online Advertising

Recent events have made evident the fact that algorithms can be discriminatory, reinforce human prejudices, accelerate the spread of misinformation, and are generally not as objective as they are widely thought to be. In this talk, I will present a vignette from my recent work which tackles the problem of discrimination in housing and employment via online ad platforms. Recent studies show that the audience an ad gets shown to can be discriminatory with respect to socially salient attributes such as gender and race, crossing ethical and legal boundaries. To mitigate this, we propose a constrained optimization framework that allows the platform to control the audience that an online ad auction gives to an advertiser in a manner that avoids discriminatory allocation. Finding the parameters of this optimal auction, however, turns out to be a non-convex problem. We show how we can leverage the structure of the problem to develop a fast algorithm to solve it, resulting in a new auction mechanism that has the potential to alleviate bias in online advertising while simultaneously maintaining good empirical performance with respect to revenue. We will further discuss the hurdles in implementing such an approach, yet the importance of doing so.

Pauline Kim, Washington University in St. Louis

Date: Friday, November 15 2019, 12:00-1:30 PM EST

Link: Youtube Live

Pauline Kim is the Daniel Noyes Kirby Professor Washington University Law School in St. Louis. She is a nationally recognized expert on the law governing the workplace and has written widely on issues affecting workers, including privacy, discrimination and job security, as well as the impact of technology in the workplace. Her research focuses on the risks of unfairness and bias as automated decision-processes are incorporated into firms' personnel decision-making and the legal challenges posed by these technological developments. She is studying the role of technological intermediaries in shaping labor markets, and the possibilities for artificially intelligent systems to avoid human biases in making personnel decisions. She is a graduate of Harvard and Radcliffe Colleges and Harvard Law School, and clerked for the Honorable Cecil F. Poole on the Ninth Circuit Court of Appeals. Following her clerkship, she worked as a staff attorney at the Employment Law Center/Legal Aid Society of San Francisco.

Manipulating Opportunity: Online Market Intermediaries and Risks of Discrimination

Concerns about online manipulation have centered on fears about undermining the autonomy of consumers and citizens. Less analyzed are the risks that the same techniques of personalizing information online can also threaten equality. When predictive algorithms are used to allocate information about opportunities like employment, housing and credit, they can reproduce past patterns of discrimination and exclusion in these markets. In this talk, I will focus on the labor market and the increasingly dominant role of tech intermediaries in managing interactions between job-seekers and firms. Because these intermediaries rely on past behavioral data to distribute information about job openings and match job-seekers with hiring firms, their predictions about who will be a good match for which jobs will likely reflect existing occupational segregation and inequality. I will discuss the legal and policy implications of tech intermediaries' new role in labor markets, including the challenges in holding them responsible for discriminatory effects and the possibility of other regulatory responses that might address these concerns.

Kentaro Toyama, University of Michigan

Date: Friday, October 25 2019, 12:00-1:30 PM EST

Link: Youtube Live

Kentaro Toyama is W. K. Kellogg Professor of Community Information at the University of Michigan School of Information and a fellow of the Dalai Lama Center for Ethics and Transformative Values at MIT. He is the author of Geek Heresy: Rescuing Social Change from the Cult of Technology. From 2005-2009, Toyama was co-founder and assistant managing director of Microsoft Research India. There, he started the Technology for Emerging Markets research group, which conducts interdisciplinary research to understand how the world's poorest communities interact with electronic technology and to invent new ways for technology to support their socio-economic development. Prior to his time in India, Toyama did research in artificial intelligence, computer vision, and human-computer interaction at Microsoft and taught mathematics at Ashesi University in Ghana.

Lessons from ICTD -- Information & Communication Technologies and Development

Since the turn of the millennium, the interdisciplinary field of information & communication technologies and development (ICTD) has explored how digital technologies could contribute to international socio-economic development. The associated research community includes both techno-utopians who imagine that just about any problem can be solved with the right application of technology, as well as extreme skeptics wary of any attempts at intervention. Debates continue, but in this talk, I will attempt to summarize some of the expressed consensus in ICTD -- things that not everyone necessarily believes, but will at least pay lip service to. I will also discuss what I call technology's "Law of Amplification," which reconciles some of the differing opinions in ICTD and also offers guidance for how mechanism design can have real-world impact.

Justine Hastings, Brown University

Date: Friday, May 10 2019, 1:00-2:15 PM EST

Justine Hastings is a Professor of Economics and International and Public Affairs at Brown University and a Faculty Research Associate with the National Bureau of Economic Research. Her areas of expertise include research in Industrial Organization and Public Economics which address important economic and public policy questions. She has conducted academic research on topics such as market structure and competition, environment and energy regulation, advertising and consumer protection, consumer financial markets, health care, social safety-net programs, and markets for higher education. Her research employs diverse empirical techniques including field experiments, survey analysis, machine learning, predictive analytics, analysis of large administrative datasets, and structural demand and supply estimation. Her research was cited in the 2017 Nobel Prize scientific background materials, and has been used to shape public policy improvements around the world. Professor Hastings is the founding director of Research Improving People's Lives (RIPL), a nonprofit research institute using data and science to impact policy and improve lives.

Fact-Based Policy: How Do States and Local Governments Accomplish It?

There is growing demand for a genuinely accountable government which, even with limited resources, delivers programs and policies with meaningful, measurable impact. Rapid advances in technology support the use of data and science in the private sector to develop insights about what people need, innovate products and policies to meet those needs, and then measure their success. Government has the potential to be similarly impactful, prompting recent federal and state calls for government to use a data-driven approach to produce efficient and effective policy solutions. But how can state and local governments use data and science to deliver improved results to their constituents? This talk will highlight the key challenges to creating and supporting fact-based policy at the state and local level, and will outline solutions and lessons learned from an innovative and scalable partnership model developed with the state of Rhode Island.

Rajiv Sethi, Barnard College, Columbia University

Date: Friday, April 5 2019, 1:00-2:30 PM EST

Link: Youtube Live

Rajiv Sethi is a Professor of Economics at Barnard College, Columbia University and an External Professor at the Santa Fe Institute. He has previously held visiting positions at Microsoft Research in New York City, and at the Institute for Advanced Study in Princeton. He is on the editorial boards of the American Economic Review and Economics and Philosophy. His current research deals with information and beliefs, including examining how stereotypes affect interactions among strangers, especially in relation to crime and the criminal justice system. He is also part of a large interdisciplinary team working on the forecasting of geopolitical events using methods that combine machine models with human judgment. Rajiv is a founding member of CORE (Curriculum Open-Access Resources for Economics), a group of scholars engaged in the production of high-quality freely-available resources for the teaching of economics.

The Geography of Lethal Force

Police officers in the United States currently kill about eleven hundred civilians annually. In contrast, police in Germany kill fewer than ten a year, and those in England and Wales kill about two. This talk will examine recent data on police homicides in the US, with particular attention to the geographic distribution of incidents and racial disparities in victimization. I consider and evaluate two competing hypotheses that seek to account for the data, and discuss the possibility that Simpson's paradox may be relevant for understanding the patterns that we see. Some historical context is provided with reference to the 1968 Kerner Commission Report and the Carnegie-Myrdal study of the 1930s. The talk will draw on material from Shadows of Doubt: Stereotypes, Crime and the Pursuit of Justice, written jointly with Brendan O'Flaherty (Harvard University Press, forthcoming in April 2019) as well as ongoing work with Jose Luis Monteil Olea and Brendan O'Flaherty.

Matthew Jackson, Stanford University

Date: Friday, March 1 2019, 1:00-2:30 PM EST

Link: Youtube Live

Matthew O. Jackson is the William D. Eberle Professor of Economics at Stanford University and an external faculty member of the Santa Fe Institute and a senior fellow of CIFAR. He was at Northwestern University and Caltech before joining Stanford, and received his BA from Princeton University in 1984 and PhD from Stanford in 1988. Jackson's research interests include game theory, microeconomic theory, and the study of social and economic networks, on which he has published many articles and the books `The Human Network' and `Social and Economic Networks'. He also teaches an online course on networks and co-teaches two others on game theory. Jackson is a Member of the National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, a Fellow of the Econometric Society, a Game Theory Society Fellow, and an Economic Theory Fellow, and his other honors include the von Neumann Award, a Guggenheim Fellowship, the Social Choice and Welfare Prize, the B.E.Press Arrow Prize for Senior Economists, and teaching awards. He has served as co-editor of Games and Economic Behavior, the Review of Economic Design, and Econometrica.

Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials

Can we identify highly central individuals in a network without collecting network data, simply by asking community members? Can seeding information via such nominated individuals lead to significantly wider diffusion than {choosing} randomly chosen people, or even respected ones? In two separate large field experiments in India, we answer both questions in the affirmative. In particular, in 521 villages in Haryana, we provided information on monthly immunization camps to either randomly selected individuals (in some villages) or to individuals nominated by villagers as people who would be good at transmitting information (in other villages). We find that the number of children vaccinated every month is 22% higher in villages in which nominees received the information. We show that people's knowledge of who are highly central individuals and good seeds can be explained by a model in which community members simply track how often they hear gossip about others. Indeed, we find in a third dataset that nominated seeds are central in a network sense, {and are} not just those with many friends or in {powerful} positions.

Tawanna Dillahunt, University of Michigan

Date: Friday, January 25th 2019, 1:00-2:30 PM EST

Tawanna Dillahunt is an Assistant Professor at the University of Michigan's School of Information and holds a courtesy appointment with the Electrical Engineering and Computer Science Department. Tawanna earned her Ph.D. in Human-Computer Interaction (HCI) from Carnegie Mellon University. She now leads the Social Innovations research group, an interdisciplinary group of individuals whose vision is to design, build, and enhance technologies to solve real-world problems affecting marginalized groups and individuals primarily in the U.S. Our current projects aim to address unemployment, environmental sustainability, and technical literacy by fostering social and sociotechnical capital within these communities.

Designing and Envisioning Digital Tools for Low-resource Job Seekers

Today's Information and Communication Technologies (ICTs) are designed to address one of society's most pressing problems---unemployment. These technologies support job seekers' ability to search for jobs, create resumes, highlight skills, share employment opportunities, and even transport to work and job counseling. However, the benefits of employment tools and technologies are unequally distributed and provide limited advantages for certain populations in our society. Like other valuable resources, ICTs have done little to support individuals with limited knowledge, skills, or experience to leverage them and who often face geographic and social isolation. Without an understanding of how people from low-resource settings use ICTs for job seeking, the same employment inequalities that occur offline will be repeated in online contexts. In this presentation, I will discuss the results of several studies that investigate how ICTs could improve employability, particularly among job seekers with limited digital skills, education, and income, and those who are geographically and socially isolated. I will also discuss new principles for fostering innovations among these populations and identify barriers for designers and technologists to address in the future.

Alvin Roth, Stanford University

Date: Thursday, December 13th 2018, 12:00-1:30 PM EST
Link: Youtube Live

Al Roth is a professor of Economics at Stanford. He shared the 2012 Nobel prize in Economics for "the theory of stable allocations and the practice of market design".

Market design is more complicated than mechanism design. And so is achieving good social outcomes.

Marketplaces are often small parts of large markets, and so potential marketplace participants may have large strategy sets, that include actions taken outside of the marketplace. And markets require social support, so the behavior of people who do not intend to participate in the market may nevertheless be important for market design. This talk will illustrate these points with some examples, drawing on experience from the design of school choice systems and kidney exchange clearinghouses.

Canice Prendergast, University of Chicago

Date: Monday, November 12th 2018, 1:00-2:30 PM EST
Link: Youtube Live

Canice Prendergast is the author of "The Limits of Bureaucratic Efficiency" published in the Journal of Political Economy in 2003 and "The Tenuous Trade-Off Between Risk and Incentives" that appeared in the Journal of Political Economy in 2002. Prendergast is widely published, with work appearing in the Economic Journal, the Journal of Labor Economics, the American Economic Review, the Journal of the Japanese and International Economics, and the European Economic Review. Articles on his recent research have appeared in Fortune Magazine, the Financial Times, the Economist, and Der Spiegel.

The Allocation of Food to Food Banks

Feeding America distributes food to food banks across the United States. In 2005, it transitioned from a centralized allocation process to one where local affiliates would bid for food items through an online auction mechanism. To do so, it constructed a specialized currency called “shares”. The change, its necessary idiosyncrasies, and outcomes are described here. We both show that the new system exhibits desirable theoretical properties, and document considerable welfare implications. The choices of the food banks vary enormously from the allocations they received under the old system, and much of this gain is from sorting of food banks along the quality-quantity dimension. Furthermore, supply of food rose by roughly 100 million pounds around the time of its introduction. A structural exercise estimates that the value of reallocated demand effectively meant that each pound of food allocated through this system increased efficiency by almost another additional pound.