AI for Social Impact Seminar Series

Computational tools are poised to play an increasingly large role in our society across different domains, including public health and conservation. As a consequence, computational tools need to be designed to ensure equitable benefits for everyone. 
To that end, we need to bring in a diverse set of perspectives that spans from algorithmic fairness, human-centered computing, and sustained deployment. This seminar series will explore how artificial intelligence can equitably solve social problems. For example, what role can AI play in promoting health, access to opportunity, and sustainable development? How can human-centered computing methods be deployed to ensure AI systems are ethical, inclusive, and accountable?

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Organizers

Program

Jan 25: Danielle Belgrave, Ph.D. (Microsoft Research Cambridge, UK)

Title: Machine learning for healthcare: Steps Towards a personalised approach.

Abstract: Machine learning advances are opening new routes to more precise healthcare, from the discovery of disease subtypes for stratified interventions to the development of personalised interactions supporting self-care between clinic visits. This offers an exciting opportunity for machine learning techniques to impact healthcare in a meaningful way. Within the healthcare domain, machine learning for mental healthcare is an under-investigated area and yet a potentially highly impactful area of research. In this talk, I will present recent work on machine learning to enable a more personalised approach to mental healthcare, whereby information can be aggregated from multiple sources within a unified modelling framework. I will present applications from both mental health and respiratory diseases.

Learn more about Danielle Belgrave

Talk recording: https://youtu.be/S1MfRLvoBVk

Feb 1: Omer Reingold, Ph.D. (Stanford University)

Title: Computational Insights on the Meaning of Individual Probabilities

Abstract: Prediction algorithms assign numbers to individuals that are popularly understood as individual “probabilities”—what is the probability of 5-year survival after cancer diagnosis?—and which increasingly form the basis for life-altering decisions. The philosophical and practical understanding of individual probabilities in the context of events that are non-repeatable has been the focus of intense study for decades by the statistics community. The wide-scale impact of automatic decision making calls for revisiting these questions from a computational perspective.

In this vein and building off of notions developed in complexity theory and cryptography, we introduce and study Outcome Indistinguishability. Predictors that are Outcome Indistinguishable yield a model of probabilities that cannot be efficiently refuted on the basis of the real-life observations produced by Nature.

The talk will be self-contained and will explain the relevant complexity-theoretic and algorithmic-fairness literatures in which this work is grounded. Our focus will be on the insights that can be drawn from this work such as providing scientific grounds for the political argument that, when inspecting algorithmic risk prediction instruments, auditors should be granted oracle access to the algorithm, not simply historical predictions.

Based on research joint with Cynthia Dwork, Michael Kim, Guy Rothblum and Gal Yona

Learn more about Omer Reingold

Talk recording: https://youtu.be/9RiQstGq90I

Feb 8: Heather Lynch, Ph.D. (Stony Brook University)

Title: How many penguins are there? (and other mysteries solved by satellites and AI)

Abstract: Satellite imagery and computer vision are two transformational technologies that have rapidly, and quite radically, expanded our capacity to study wildlife in the world’s most remote places. In this talk, I will describe my lab’s efforts to combine satellite imagery, drones, and other remote sensing technologies with good old fashioned field work to study the distribution and abundance of penguins and other wildlife in Antarctica. I’ll also discuss the threats facing Antarctic penguins and how scientists are bringing together new technology, artificial intelligence, and advanced predictive modelling to help guide policymakers in their work to protect one of the world’s last remaining wildernesses.

Learn more about Heather Lynch

Feb 22: Munmun De Choudhury, Ph.D. (Georgia Institute of Technology)

Title: Bridging Machine Learning and Collaborative Action
Research: A Tale of Engaging with Three Stakeholders in Digital Mental Health

Abstract: Digital traces, such as social media data, supported with advances in the artificial intelligence (AI) and machine learning (ML) fields, are increasingly being used to understand the mental health of individuals and populations. However, such algorithms do not exist in a vacuum — there is an intertwined relationship between what an algorithm does and the world it exists in. Consequently, with algorithmic approaches offering promise to change the status quo in mental health for the first time since mid-20th century, interdisciplinary collaborations are paramount. But what are some paradigms of engagement for AL/ML researchers that augment existing algorithmic capabilities while minimizing the risk of harm? This talk will describe the experiences from working with three different stakeholders in projects relating to digital mental health – first with a federal agency, second with healthcare providers, and third with a non-profit organization. The talk hopes to present some lessons learned by way of these engagements, and to reflect on approaches that go beyond technical innovations and building technological artifacts to contributions that center humans’ roles, beliefs, needs, and expectations within those innovations and artifacts.

Learn more about Munmun De Choudhury

Talk recording: https://www.youtube.com/watch?v=swQUWL6k8k4&t=1s

Mar 8: Courtney Cogburn, Ph.D. (Columbia University)

Mar 15: Lauren Wilcox, Ph.D. (Georgia Institute of Technology, Wellbeing Lab at Google) 

AbstractAdvances in computing technology continue to offer us new insights about our health and well-being. As mutually reinforcing trends make the use of wearable and mobile devices routine, we now collect personal, health-related data at an unprecedented scale. Meanwhile, electronic health record (EHR) systems continue to evolve. In response, ML/AI-driven solutions are making use of data incorporating multitudinous dimensions of our health. How can researchers take inclusive approaches to envisioning solutions, training data, and deploying AI/ML-driven solutions? Who should be involved in decisions about how to use ML/AI in digital health and well-being solutions, and even what solutions matter in the first place? 


In this talk, I will discuss participatory approaches to designing digital health and well-being technologies with patients, family members, and clinicians.  I will provide an overview of studies focusing on how human communication, health management practices, and interactions with health-related data point to lessons for uniting computing advancements with people’s needs. Starting with field studies in clinics exploring how people navigated use of a deployed, diagnostic AI system, and then moving onto collaborative design approaches for supporting chronic condition management in families,  I will discuss participatory approaches that can be used throughout the technology design, development, and evaluation process to bring human-centered design to new solutions.

Talk recording: https://www.youtube.com/watch?v=3_5MmcJIQZM

Mar 22: Tiffany Veinot, MLS, Ph.D. (University of Michigan)

Title: Leveling Up:  Developing Upstream Health Informatics Interventions to Reduce Health Disparities

Abstract: Health disparities are differences in disease incidence, prevalence, morbidity, mortality, or survival in one group compared to the general population. Health disparities are a product of macro-level social, political and economic mechanisms and intermediary social determinants of health such as living and working conditions and social networks. This presentation makes the case for “upstream” informatics interventions that focus on the social, political, economic and physical contexts in which health is produced. The presentation outlines key findings from three community-based research projects focused on developing and evaluating upstream informatics interventions. The first leverages social media to characterize community health, and will ultimately result in a decision support tool for public health officials, urban planners, policy makers and nonprofit organizations. The second focuses on stigma reduction in social networks to enhance the uptake of HIV testing among men who have sex with men. The result will be a blended online/offline intervention leveraging social networks. The third aims to reduce hemodialysis complications disproportionately experienced by women. The cluster-randomized, pragmatic trial compares the effectiveness of technology-mediated behavioral interventions for healthcare providers (tablet-based diagnostic checklist and team training) and for patients (tablet-based education and peer mentoring). The presentation concludes with recommendations for researchers and practitioners who aspire to enhance health equity with informatics. 
Learn more about Tiffany Veinot

Talk recording: https://www.youtube.com/watch?v=ovJT_bhPjA4

Mar 29: Kush Varshney, Ph.D (Thomas J. Watson Research Center, IBM)

Title: Lessons from Bottom of the Pyramid Innovation for AI for Social Good

Abstract: The AI for Social Good movement is nearing a full decade, but most projects in the space have had little meaningful and sustained impact beyond the purpose of demonstration. One of the main reasons for this is that, like the populations they serve, social change organizations tend to be stretched thin in terms of money, time, and investment in capacity building. Specifically, they have limited capacity to take custom solutions created for them by volunteer data scientists and deploy/maintain them in their operations due to skill and resource gaps. As an alternative, we conjecture that highly usable and reusable software toolkits delivered as a service through cloud-based platforms containing fair and robust models relevant for the missions of several social change organizations can overcome the operationalization gap for the 'bottom of the pyramid' of organizations. In this talk, we will describe the conjecture as well as the experiments we have conducted so far to validate it.

Learn more about Kush Varshney

 

Apr 5: Christopher Le Dantec, Ph.D. (Georgia Institute of Technology)

Title: Mismatches and Seams in Civic Data Infrastructure

Absract: Municipal governments are increasingly collecting and using data for decision-making and service provisioning. At the same time, community groups and civic organizations are also increasingly engaged in data collection and use as they mobilize to address local issues – including economic development, mobility, and public health. This common site of data production and use is a potential site of civic collaboration among formal and informal organizations. While there is a desire to deploy integrative computing platforms that bring data and people together in collaboration to address issues of public concern, there are gaps to enabling collaboration that are not simply impediments to integration and coordination, but meaningful seams necessary for productive civic friction. In this talk I will present a recent collaborative effort to develop data resources to address food security in a rural town in Georgia. The project leveraged community-facing features of the city's geospatial management platform and provides a perspective on the intersection of organizational boundaries and technology affordances. From this we can see the breakdowns of a unified platform and beginning of opportunities for new systems that treat these seams as a resource for design.

Apr 12: Nyalleng Moorosi (Google AI)

Title: Understanding the Socio-Technical Pipeline of AI for Public Enterprises

Abstract: By 2016, many governments were embracing the promises of AI for Social Good; we at the Council for Scientific and Industrial Research in South Africa (CSIR-SA) were no different. In this talk, I will speak about our efforts towards deploying AI tools for public enterprises. I will discuss our wins, losses and lessons learned. Specifically, I wish to highlight both the technical and social tools necessary to function in this highly dynamic environment of changing budgets, deadlines, scope and outcomes. To ground this discussion, I will use the Rhino Poaching project, which I was part of  from 2016 until 2018 as a case study.

Apr 19: Michael J. Mina, MD, PhD (Harvard T.H. Chan School of Public Health, Harvard Medical School)

Title: Advances in virology, immunology, and computation to monitor and control outbreaks

Abstract: Advances in biological and computational sciences have enabled massive leaps in basic research and medicine over the recent decades. Relative to these medical advances, advances in public health and particularly infectious diseases have been modest. The SARS-CoV2 pandemic has shown us the consequences of investing so fully in medicine without a strong foundation of public health. This talk will provide examples of the technological leaps that are taking place in the medical sciences, in part as a result of COVID-19, and will discuss how they can be coupled with advances in computational tools to usher in exciting new approaches to public health that can help us get out of the SARS-CoV-2 pandemic and prevent similar global catastrophes in the future. 

Learn more about Michael Mina

Apr 26: Kobi Gal, PhD (Ben-Gurion University)

Title: Behavior change using AI: Making an Impact in Online Collaborative Settings

Abstract: Advances in technologies and interface design are enabling group activities of varying complexities to be carried out, in whole or in part, over the internet, with benefits to science and society  (e.g., citizen science, Massive Online Open Courses (MOOC) and questions-and-answers sites). The need to support human interaction in such settings brings new and significant challenges to AI; how to provide incentives that keep participants motivated and productive; how to provide useful, information to system designers to help them decide whether and how to intervene with the group's work; how to scientifically evaluate the effects of AI interventions on the performance of individuals and the group.

I will describe  ongoing projects in my lab that address these challenges in three socially relevant settings — education, Q&A sites and citizen science--- and discuss potential ethical issues that arise from using AI for behavior change.

Invited speakers

Danielle Belgrave (seminar on January 25, 2021)

Principal Research Manager, Microsoft Research Cambridge (UK)

Danielle BelgraveDanielle Belgrave is a machine learning researcher in the Healthcare Intelligence group at Microsoft Research, in Cambridge (UK) where she works on Project Talia. Her research focuses on integrating medical domain knowledge, probabilistic graphical modelling and causal modelling frameworks to help develop personalized treatment and intervention strategies for mental health. Mental health presents one of the most challenging and under-investigated domains of machine learning research. In Project Talia, she and her team explore how a human-centric approach to machine learning can meaningfully assist in the detection, diagnosis, monitoring, and treatment of mental health problems. She obtained a BSc in Mathematics and Statistics from London School of Economics, an MSc in Statistics from University College London and a PhD in the area of machine learning in health applications from the University of Manchester. Prior to joining Microsoft, she was a tenured Research Fellow at Imperial College London.

Omer Reingold(seminar on February 1, 2021)

Professor of computer science at Stanford University and the director of the Simons Collaboration on the Theory of Algorithmic Fairness

Omer ReingoldOmer Reingold is the Rajeev Motwani professor of computer science at Stanford University and the director of the Simons Collaboration on the Theory of Algorithmic Fairness. Past positions include Samsung Research America, the Weizmann Institute of Science, Microsoft Research, the Institute for Advanced Study in Princeton, NJ and AT&T Labs. His research is in the foundations of computer science and most notably in computational complexity, cryptography and the societal impact of computation. He is an ACM Fellow and a Simons Investigator. Among his distinctions are the 2005 Grace Murray Hopper Award and the 2009 Gödel Prize.

Heather Lynch (seminar on February 8, 2021)

IACS Endowed Chair for Ecology & Evolution, Stony Brook University

Heather LynchDr. Heather Lynch is the Institute for Advanced Computational Sciences Endowed Chair for Ecology & Evolution at Stony Brook University and currently a AAAS Leshner Fellow for Science Engagement focused on AI and its applications. Following a B.A. in Physics from Princeton University and an M.A. in Physics from Harvard University, she received her Ph.D. in Organismal and Evolutionary Biology from Harvard University in 2006. Dr. Lynch’s research sits at the intersection of statistical ecology, geography, applied math, and computer science. Her research is focused on all aspects of conservation ecology in the Antarctic, with a particular focus on the integration of satellite imagery and traditional field work to map the distribution and abundance of Antarctic wildlife and to predict how populations will be impacted by climate change, fishing, and tourism.

Munmun De Choudhury, Ph.D. (seminar on February 22, 2021)

Associate professor of Interactive Computing at Georgia Tech

Munmun De ChoudhuryMunmun De Choudhury is an associate professor of Interactive Computing at Georgia Tech. Dr. De Choudhury is best known for laying the foundation of a line of research that develops computational techniques to responsibly and ethically employ social media in understanding and improving our mental health. To do this work, she adopts a highly interdisciplinary approach, combining social computing, machine learning, and natural language analysis with insights and theories from the social, behavioral, and clinical sciences. Dr. De Choudhury has been recognized with the 2021 ACM-W Rising Star Award, 2019 Complex Systems Society – Junior Scientific Award, 13 best paper and honorable mention awards from the ACM and AAAI, and extensive coverage in popular press like the New York Times, the NPR, and the BBC. In 2020, Dr. De Choudhury served as the General Chair of the 14th AAAI International Conference in Web and Social Media, the leading conference on interdisciplinary studies of social media. Earlier, Dr. De Choudhury was a faculty associate with the Berkman Klein Center for Internet and Society at Harvard, a postdoc at Microsoft Research, and obtained her PhD in Computer Science from Arizona State University.

Lauren Wilcox, Ph.D. (seminar on March 15, 2021)

Associate Professor, School of Interactive Computing at Georgia Tech and Staff Researcher, Wellbeing Lab at Google

Photo of Lauren Wilcox

Lauren Wilcox, PhD, is a research lead in the Google Wellbeing Lab. She brings over thirteen years of experience conducting human-centered computing research in service of human health and well-being. Previously at Google Health, Wilcox led initiatives to align AI advancements in healthcare with the needs of clinicians, patients, and their family members. Wilcox also holds an associate professor position in Georgia Tech’s School of Interactive Computing. She received a CAREER award from the NSF and a Dissertation Award from the Agency for Healthcare Research and Quality (AHRQ). She has authored several recognized papers (e.g., papers receiving editor's choice designation, best paper, and best paper honorable mention). Wilcox was named a Senior Member of the ACM in 2020. She was an inaugural member of the ACM Future of Computing Academy. She frequently serves on the organizing and technical program committees for premier conferences in the field (e.g., ACM CHI). Wilcox received her PhD in Computer Science from Columbia University in 2013.

 

Tiffany Veinot, MLS, Ph.D. (Seminar on March 22, 2021)  

Associate Dean for Faculty at the University of Michigan (UM) School of Information, Professor at School of Information and Public Health at UM

Tiffany VeinotTiffany Veinot, MLS, PhD is Associate Dean for Faculty at the University of Michigan (UM)’s School of Information. She is also a Full Professor at the Schools of Information and Public Health at UM. She is former Director of UM’s Master of Health Informatics Program, and a founding faculty member for that program. Her research focuses on “community health informatics,” or the use of information systems and services to improve the health of marginalized populations and reduce health disparities. She has over 75 published, peer-reviewed papers, and her research has garnered eight “best paper” awards in health informatics, human-computer interaction, and information science. Veinot has held over $9.8 million in research funding as Principal Investigator, with funding from agencies such as Patient-Centered Outcomes Research Institute (PCORI), the National Science Foundation (NSF), Google, Canadian Institutes for Health Research (CIHR), and the Social Sciences and Humanities Research Council of Canada (SSHRC). She is on the Editorial Boards of the Journal of the American Medical Informatics Association, Journal of the Association of Information Science and Technology, and International Journal of Medical Informatics.

Kush Varshney, Ph.D (Seminar on March 29, 2021) 

Distinguished Research Staff Member and Co-Director of IBM Science for Social Good, IBM Research - T. J. Watson Research Center

Kush R. Varshney was born in Syracuse, NY in 1982. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, NY, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), Cambridge. While at MIT, he was a National Science Foundation Graduate Research Fellow.
Dr. Varshney is a distinguished research staff member and manager with IBM Research at the Thomas J. Watson Research Center, Yorktown Heights, NY, where he leads the machine learning group in the Foundations of Trustworthy AI department. He was a visiting scientist at IBM Research - Africa, Nairobi, Kenya in 2019. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation, and Harvard Belfer Center Tech Spotlight runner-up for AI Fairness 360. He conducts academic research on the theory and methods of trustworthy machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences and the 2019 Computing Community Consortium / Schmidt Futures Computer Science for Social Good White Paper Competition. He is currently writing a book entitled 'Trustworthy Machine Learning' with Manning Publications. He is a senior member of the IEEE and a member of the Partnership on AI's Safety-Critical AI expert group.

 

Christopher Le Dantec, Ph.D (Seminar on April 5, 2021) 

Associate Professor, School of Interactive Computing and School of Literature, Media, and Communication; Georgia Institute of Technology

Dr. Christopher Le Dantec is an Associate Professor with appointments in the School of Interactive Computing and the School of Literature, Media, and Communication at the Georgia Institute of Technology. His research is focused on the area of digital civics where he works with a range of community-based partners to explore new forms of civic participation through community-centered design inquiry at the intersection of participatory design, digital democracy, and smart cities. He is the author of Designing Publics (2016, MIT Press).

Nyalleng Moorosi (Seminar on April 12, 2021)

Research Software Engineeer, Google

 

Nyalleng is a research software engineer at Google working on topics related to ethics and fairness in machine learning. Before Google she was a senior researcher at the South African Council for Scientific and Industrial researcNyalleng Moorosi Photoh, where she worked closely with government and academic institutions to develop products to understand phenomena such as Rhino poaching with the South African Park services and Election sentiment analysis with the South African Broadcasting Corporation and legacies of Spatial Aparthied with local faculty. 

Outside of formal work she is involved in efforts to democratize AI; she is a founding member of the Deep Learning Indaba, the largest machine learning consortium of AI/ML practitioners in Africa, a member of A+ Alliance an international coalition that seeks to not only detect, but correct, gender bias in Artificial Intelligence.

 

Michael Mina (Seminar on April 19, 2021)

Assistant Professor of Epidemiology, Harvard T.H. Chan School of Public Health; Core Faculty, Center for Communicable Disease Dynamics

Michael Mina, MD, PhD, is an assistant professor of epidemiology at the Harvard T.H. Chan School of Public Health and a core member of the School’s Center for Communicable Disease Dynamics (CCDD). He is additionally an assistant professor in immunology and infectious diseases at the Harvard Chan School and associate medical director in clinical microbiology (molecular diagnostics) in the Department of Pathology at Brigham and Women’s Hospital, Harvard Medical School.

He earned his MD and PhD degrees from Emory University, with doctoral work split between CDC, St. Jude Children’s Research Hospital, the Respiratory and Meningeal Pathogens Research Unit in Johannesburg, South Africa, and the Emory Vaccine Center. He completed his postdoctoral work at Princeton University in ecology and evolutionary biology (of infectious disease dynamics) with Prof. Bryan Grenfell and at Harvard Medical School in the Department of Genetics with Prof. Stephen Elledge. He completed his residency training in clinical pathology at Brigham and Women’s Hospital/Harvard Medical School.

Mina’s research combines mathematical and epidemiological models with high-throughput phage-display based serological laboratory investigations, including development of new technologies and statistical pipelines to better understand the population and immunological consequences and patterns underlying infectious diseases. Much of the work toward new technology development is performed in close collaboration with Steve Elledge at HMS. Major themes of his lab include (i) development of new approaches (laboratory and statistical methods) to enable extremely high-throughput serological surveillance of infectious pathogens; (ii) use of high-complexity antibody profiling and epidemiological data to understand the pathogenesis of vaccine-preventable diseases, with a specific focus on measles infections and vaccines; (iii) elucidating broad unintended/heterologous effects of vaccines to alter transmission patterns of unrelated infectious pathogens—using serology and dynamical models; and (iv) understanding the life-history of infectious pathogens across ages, genders, geographies, and times. In addition to his interests in infectious diseases, Mina’s research also explores more fundamental questions of immunity and immune repertoires: how they form, how they persist, how they are passed on, and how they become perturbed during natural life-events.

Kobi Gal (Seminar on April 26, 2021)

Associate Professor, Department of Information Systems at Ben-Gurion University

Bio: Prof. Kobi Gal is a faculty member of the Department of Software and Information Systems Engineering at the Ben-Gurion University of the Negev, and a Reader at the School of Informatics at the University of Edinburgh. His work investigates representations and algorithms for making decisions in heterogeneous groups comprising both people and computational agents. He has worked on combining artificial intelligence algorithms with educational technology towards supporting students in their learning and teachers to understand how students learn. He has published widely in highly refereed venues on topics ranging from artificial intelligence to the learning and cognitive sciences.

Gal is the recipient of the Wolf foundation's 2013 Krill prize for young Israeli scientists, a Marie Curie International fellowship, and a three-time recipient of Harvard University's outstanding teacher award. Together with his co-authors, he has won the best student paper at UMAP-19, the best paper at EC 2016, and the best student paper at EDM 2014.
 

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