AI for Social Impact Seminar Series

The AI for Social Impact Seminar Series brings together researchers and thought leaders from a variety of fields to explore the diverse applications of artificial intelligence for a societal benefit. Through the series, the Center for Socially Responsible Artificial Intelligence aims to inspire new ideas and collaborations and to identify novel approaches that can advance discovery in the field at Penn State and beyond.

Note: When watching recordings of past talks, closed captions can be enabled by selecting the [CC] button in the YouTube video player.

Upcoming Events

10:45 am - 11:45 am
Online

Join Harsh Nisar, Lead Data Scientist with the Government of India's Ministry of Rural Development for an upcoming talk in the AI for Social Impact Seminar Series. This lecture is free and open to the Penn State community.

Zoom link

“Algorithmic Rural Road Planning in India: Constrained Capacities and Choices in Public Sector”

In this talk, Harsh Nisar will talk about how the peculiarities of government (within and the setting it operates in) impact algorithmic design, development, and deployment. He'll use his experience developing the resource allocation mechanism for a $10 billion road-maintenance program launched in 2018 to improve rural connectivity to schools, hospitals, and markets.

About the Speaker:

Harsh Nisar is currently the Lead Data Scientist at the Ministry of Rural Development, Government of India. The Ministry of Rural Development operates some of the largest welfare programs in the world targeting rural employment, roads, housing, and so on. At the Ministry, Nisar leads the Data & Insights Unit, which is an interdisciplinary team of HCI, Policy, and Data professionals who try to use data and algorithms to meaningfully improve service delivery for 800 million rural citizens in India. This includes using satellite imagery to find unmapped settlements, biases in resource allocation policies, and algorithmic decision support systems to reduce administrative burdens, among others. He also serves as an advisor to India's apex body for National Highways where he is working on improving emergency incident response times.

About the AI for Social Impact Seminar Series

The AI for Social Impact Seminar Series brings together researchers and thought leaders from a variety of fields to explore the diverse applications of artificial intelligence for a societal benefit. Through the series, the Center for Socially Responsible Artificial Intelligence aims to inspire new ideas and collaborations and to identify novel approaches that can advance discovery in the field at Penn State and beyond.

Past Events

2:30 pm - 3:30 pm
Online

Join Maria De-Arteaga, assistant professor in the McCombs School of Business at the University of Texas at Austin for an upcoming talk in the AI for Social Impact Seminar Series. This lecture is free and open to the Penn State community.

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“Mind the Gap: From Predictions to ML-Informed Decisions”

Machine learning (ML) is increasingly being used to support decision-making in many organizational settings. However, there is currently a gap between the design and evaluation of ML algorithms and the functional role of these algorithms as tools for decision support. The first part of the talk will highlight the role of humans-in-the-loop, and the importance of evaluating decisions instead of predictions, through a study of the adoption of a risk assessment tool in child maltreatment hotline screenings. The second part of the talk will focus on the gap between the construct of interest and the proxy that the algorithm optimizes for. Using a proposed machine learning methodology that extracts knowledge from experts’ historical decisions, De-Arteaga shows that in the context of child maltreatment hotline screenings (1) there are high-risk cases whose risk is considered by the experts but not wholly captured in the target labels used to train a deployed model, and (2) we can bridge this gap if we purposefully design with this goal in mind.

About the Speaker

Maria De-Arteaga is an assistant professor at the Information, Risk and Operation Management (IROM) Department at the University of Texas at Austin, where she is also a core faculty member in the Machine Learning Laboratory and an affiliated faculty of Good Systems. She holds a joint Ph.D. in Machine Learning and Public Policy and a M.Sc. in Machine Learning, both from Carnegie Mellon University, and a. B.Sc. in Mathematics from Universidad Nacional de Colombia. Her research focuses on the risks and opportunities of using machine learning to support experts’ decisions in high-stakes settings, with a particular interest in algorithmic fairness and human-AI collaboration. Her work has been featured by UN Women and Global Pulse and has received best paper awards at NAACL’19 and Data for Policy’16, and research awards from Google and Microsoft Research.

About the AI for Social Impact Seminar Series

The AI for Social Impact Seminar Series brings together researchers and thought leaders from a variety of fields to explore the diverse applications of artificial intelligence for a societal benefit. Through the series, the Center for Socially Responsible Artificial Intelligence aims to inspire new ideas and collaborations and to identify novel approaches that can advance discovery in the field at Penn State and beyond.

11:00 am - 12:00 pm
Online

Join Srijan Kumar, assistant professor at the College of Computing at Georgia Institute of Technology for an upcoming talk in the AI for Social Impact Seminar Series. This lecture is free and open to the Penn State community.

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“Advances in AI for Cyber-Safety”

The safety, integrity, and well-being of users, communities, and platforms on web and social media is a critical, yet challenging task. In this talk, Kumar will describe the cybersafety-focused machine learning methods, leveraging behavior modeling, graph analytics, and deep learning, that his group has developed to efficiently detect malicious users and bad content online. While developing models that are highly accurate is important, it is also crucial to ensure that the systems are trustworthy. Thus, Kumar will describe his group's work on quantifying the reliability of cybersafety models against smart adversaries that are popularly used in practice, such as those in Facebook.

About the Speaker:

Srijan Kumar is an assistant professor at the College of Computing at Georgia Institute of Technology. He develops data science, machine learning, and AI solutions for the pressing challenges pertaining to the safety, integrity, and well-being of users, platforms, and communities in the cyber domain. He has pioneered the development of user models and network science tools to enhance the well-being and safety of users. His methods have been used in production at Flipkart (India’s largest e-commerce platform) and taught at graduate level courses worldwide. He has named to the Forbes 30 under 30 Class of 2022 and has received several awards including the Facebook Faculty Award, Adobe Faculty Award, ACM SIGKDD Doctoral Dissertation Award runner-up 2018, Larry S. Davis Doctoral Dissertation Award 2018, and 'best of' awards from WWW and ICDM. His research has been the subject of a documentary and covered in popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine. He completed his postdoctoral training at Stanford University, received a Ph.D. in Computer Science from University of Maryland, College Park, and B.Tech. from Indian Institute of Technology, Kharagpur.

About the AI for Social Impact Seminar Series

The AI for Social Impact Seminar Series brings together researchers and thought leaders from a variety of fields to explore the diverse applications of artificial intelligence for a societal benefit. Through the series, the Center for Socially Responsible Artificial Intelligence aims to inspire new ideas and collaborations and to identify novel approaches that can advance discovery in the field at Penn State and beyond.

2:30 pm - 3:30 pm
Online

Join Dashun Wang, Professor of Management and Organizations, Kellogg School of Management and McCormick School of Engineering at Northwestern University for an upcoming talk in the AI for Social Impact Seminar Series. This lecture is free and open to the Penn State community.

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"Initial Progress on the Science of Science"

The increasing availability of large-scale datasets that trace the entirety of the scientific enterprise have created an unprecedented opportunity to explore scientific production and reward. Parallel developments in data science, network science, and artificial intelligence offer us powerful tools and techniques to make sense of these millions of data points. Together, they tell a complex yet insightful story about how scientific careers unfold, how collaborations contribute to discovery, and how scientific progress emerges through a combination of multiple interconnected factors. These opportunities—and challenges that come with them—have fueled the emergence of a multidisciplinary community of scientists that are united by their goals of understanding science. These practitioners of the science of science use the scientific methods to study themselves, examine projects that work as well as those that fail, quantify the patterns that characterize discovery and invention, and offer lessons to improve science as a whole. In this talk, Wang will highlight some examples of research in this area, hoping to illustrate the promise of science of science as well as its limitations.

About the Speaker

Dashun Wang is a Professor of Management and Organizations at the Kellogg School of Management, and the McCormick School of Engineering, Northwestern University. He is the Founding Director of the Center for Science of Science and Innovation (CSSI). He is also a core faculty at the Northwestern Institute on Complex Systems (NICO). His current research focus is on Science of Science, a quest to turn the scientific methods and curiosities upon science itself, hoping to use and develop tools from complexity sciences and artificial intelligence to broadly explore the opportunities for innovation and promises of prosperity offered by the recent data explosion in science. His research has been published repeatedly in journals like Nature and Science, and has been featured in virtually all major global media outlets, including The New York Times, Wall Street Journal, The Economist, Bloomberg, Financial Times, The Today Show, Harvard Business Review, The Atlantic, World Economic Forum, Forbes, The Guardian, The Washington Post, and The Boston Globe, among others. Wang is a recipient of multiple awards for his research and teaching, including the AFOSR Young Investigator award, Poets & Quants Best 40 Under 40 Professors, Complex Systems Society’s Junior Scientific Award, Network Science Society’s Erdos-Renyi Award, Thinkers50 Radar List 2021, and more. His first book, The Science of Science, coauthored with Albert-Laszlo Barabasi, was published in March 2021.

About the AI for Social Impact Seminar Series

The AI for Social Impact Seminar Series brings together researchers and thought leaders from a variety of fields to explore the diverse applications of artificial intelligence for a societal benefit. Through the series, the Center for Socially Responsible Artificial Intelligence aims to inspire new ideas and collaborations and to identify novel approaches that can advance discovery in the field at Penn State and beyond.

2:30 pm - 3:30 pm
Online

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“Mobility Networks for Modeling the Spread of COVID-19: Explaining Infection Rates and Informing Reopening Strategies”

In this talk, Dr. Leskovec will demonstrate how fine-grained epidemiological modeling of the spread of Coronavirus -- predicting who gets infected at which locations -- can aid the development of policy responses that account for heterogeneous risks of different locations as well as the disparities in infections among different demographic groups. He will demonstrate the use of U.S. cell phone data to capture the hourly movements of millions of people and model the spread of Coronavirus from among a population of nearly 100 million people in 10 of the largest U.S. metropolitan areas. Dr. Leskovic will show that even a relatively simple epidemiological model can accurately capture the case trajectory despite dramatic changes in population behavior due to the virus. He also estimates the impacts of fine-grained reopening plans: he predicts that a small minority of superspreader locations account for a large majority of infections, and that reopening some locations (like restaurants) pose especially large risks. He also explains why infection rates among disadvantaged racial and socioeconomic groups are higher. Overall, his model supports fine-grained analyses that can inform more effective and equitable policy responses to the Coronavirus.

About the Speaker

Jure Leskovec is an associate professor of computer science at Stanford University, the Chief Scientist at Pinterest, and an Investigator at the Chan Zuckerberg Biohub. He co-founded a machine learning startup, Kosei, which was later acquired by Pinterest. Leskovec's research area is machine learning and data science for complex, richly-labeled relational structures, graphs, and networks for systems at all scales, from interactions of proteins in a cell to interactions between humans in a society. Applications include commonsense reasoning, recommender systems, social network analysis, computational social science, and computational biology with an emphasis on drug discovery. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. It has also been featured in popular press outlets such as the New York Times and the Wall Street Journal. Leskovec received his bachelor's degree in computer science from the University of Ljubljana, Slovenia, Ph.D. in machine learning from Carnegie Mellon University, and postdoctoral training at Cornell University.

2:00 pm - 3:00 pm
Online

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“Measuring Economic Development from Space with Machine Learning”

Recent technological developments are creating new spatio-temporal data streams that contain a wealth of information relevant to climate adaptation strategies. Modern AI techniques have the potential to yield accurate, inexpensive, and highly scalable models to inform research and policy. A key challenge, however, is the lack of large quantities of labeled data that often characterize successful machine learning applications. In this talk, Dr. Ermon will present new approaches for learning useful spatio-temporal models in contexts where labeled training data is scarce or not available at all. He will show applications to predict and map poverty in developing countries, monitor agricultural productivity and food security outcomes, and map infrastructure access in Africa. These methods can reliably predict economic well-being using only high-resolution satellite imagery. Because images are passively collected in every corner of the world, these methods can provide timely and accurate measurements in a very scalable end economic way, and could significantly improve the effectiveness of climate adaptation efforts.

About the Speaker

Stefano Ermon is an assistant professor of Computer Science in the CS Department at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory and is a fellow of the Woods Institute for the Environment. His research is centered on techniques for probabilistic modeling of data and is motivated by applications in the emerging field of computational sustainability. He has won several awards, including four Best Paper Awards (AAAI, UAI and CP), an NSF Career Award, ONR and AFOSR Young Investigator Awards, a Sony Faculty Innovation Award, a Hellman Faculty Fellowship, Microsoft Research Fellowship, Sloan Fellowship, and the IJCAI Computers and Thought Award. Stefano earned his Ph.D. in Computer Science from Cornell University in 2015.

11:00 am - 12:00 pm
Online

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“Physics-Guided AI for Learning Spatiotemporal Dynamics”

Applications such as public health, transportation, and climate science often require learning complex dynamics from large-scale spatiotemporal data. While deep learning has shown tremendous success in these domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this talk, Dr. Yu will demonstrate how to principally integrate physics in AI models and algorithms to achieve both prediction accuracy and physical consistency. She will showcase the application of these methods to problems such as forecasting COVID-19, traffic modeling, and accelerating turbulence simulations.

About the Speaker

Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She was a Postdoctoral Fellow at the California Institute of Technology. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first-principles with data-driven models. She has won Faculty Research Awards from Google, Amazon, and Adobe, several Best Paper Awards, a Best Dissertation Award from USC, and was nominated as one of the ’MIT Rising Stars in EECS’.

11:00 am - 12:00 pm
Online

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“Data Science for Social Equality”

Our society remains profoundly unequal. This talk discusses how data science and machine learning can be used to combat inequality in health care and public health by presenting several vignettes about pain, COVID, and women's health.

About the Speaker

Emma Pierson is a senior researcher at Microsoft Research and an incoming assistant professor of computer science at Cornell Tech. She develops data science and machine learning methods to study inequality and health care. Her work has been recognized by a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, and Forbes 30 Under 30 in Science. She has written for The New York Times, FiveThirtyEight, The Atlantic, The Washington Post, Wired, and various other publications.

11:00 am - 12:00 pm
Online

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“Steps Toward Trustworthy Machine Learning”

How can we trust systems built from machine learning components? We need advances in many areas, including machine learning algorithms, software engineering, ML ops, and explanation. This talk will describe our recent work in two important directions: obtaining calibrated performance estimates and performing run-time monitoring with guarantees. I will first describe recent work with Kiri Wagstaff on region-based calibration for classifiers and work with Jesse Hostetler on performance guarantees for reinforcement learning. Then, I'll review our research on providing guarantees for open category detection and anomaly detection for run-time monitoring of deployed systems. I'll conclude with some speculations concerning meta-cognitive situational awareness for AI systems.

About the Speaker

Dr. Dietterich (A.B. Oberlin College 1977; M.S. University of Illinois 1979; Ph.D. Stanford University 1984) is Distinguished Professor Emeritus in the School of Electrical Engineering and Computer Science at Oregon State University. Dietterich is one of the pioneers of the field of machine learning and has authored more than 200 refereed publications and two books. His current research topics include robust artificial intelligence, robust human-AI systems, and applications in sustainability. Dietterich has devoted many years of service to the research community. He is a former president of the Association for the Advancement of Artificial Intelligence and the founding president of the International Machine Learning Society. Other major roles include executive editor of the journal Machine Learning, co-founder of the Journal for Machine Learning Research, and program chair of AAAI 1990 and NIPS 2000. He currently serves as one of the moderators for the cs.LG category on arXiv.

11:00 am - 12:00 pm
Online

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“Political Polarization and International Conflicts through the Lens of NLP”

In this talk, I will summarize two broad lines of NLP research focusing on (1) the current U.S. political crisis and (2) the long-standing international conflict between the two nuclear adversaries India and Pakistan.

The first part of the talk presents a new methodology that offers a fresh perspective on interpreting and understanding political polarization through machine translation. We begin with a novel proposition that two sub-communities viewing different U.S. cable news networks are speaking in two different languages. Next, we demonstrate that with this assumption, modern machine translation methods can provide a simple yet powerful and interpretable framework to understand the differences between two (or more) large-scale social media discussion data sets at the granularity of words.

The second part of the talk seeks to examine what we term as hostility-diffusing, peace seeking hope speech in the context of the 2019 India-Pakistan conflict. In doing so, we tackle several practical challenges that arise from multilingual texts and demonstrate how novel methods can effectively extend linguistic resources (e.g. content classifier, labeled examples) from a world language (e.g. English) to a low-resource language (e.g. Hindi). To this end, we show two different approaches – one relying on code switching and the other relying on unsupervised machine translation – which achieve substantial improvement in detecting Hindi hope speech under low-supervision settings.

About the Speaker

Ashique KhudaBukhsh is currently a project scientist at the Language Technologies Institute at Carnegie Mellon University, where he is mentored by Prof. Tom Mitchell. Prior to this role, he was a postdoc mentored by Prof. Jaime Carbonell at CMU. His doctoral thesis (Computer Science Department, CMU, also advised by Prof. Jaime Carbonell) focused on distributed active learning. His current research lies at the intersection of low-resource NLP and AI for Social Impact. In this field, he is interested in analyzing globally important events in South East Asia and developing methods for noisy social media texts generated in this linguistically diverse region. His other broad research focus is U.S. politics. In this area, his research involves devising novel methods to quantify, interpret, and understand political polarization.

11:00 am - 12:00 pm

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“Behavior Change for Social Good Using AI”

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 these highly diverse interactions brings new and significant challenges to AI, including 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; and how to 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 two socially relevant settings – education and citizen science – and discuss potential ethical issues that arise from using AI for behavior change.

About the Speaker

Kobi Gal is an associate professor at the Department of Software and Information Systems Engineering at Ben-Gurion University of the Negev, and 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 combines artificial intelligence algorithms with educational technology toward supporting students in their learning and teachers in their understanding how students learn. He has published widely in highly refereed venues on topics ranging from artificial intelligence to the learning and the 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. In 2020 he was appointed a Senior Member of the Association for the Advancement of Artificial Intelligence. He has received best paper awards at ACM Conference on User Modelling Adaptation and Personalization 2019 (UMAP-19), ACM conference on Economics and Computation 2016 (EC-16) and Educational Data Mining 2014 (EDM-14). He is an associate editor of the IEEE Transactions on Learning Technologies (TLT) and a member of the Editorial Board of the Journal of Artificial Intelligence (JAIR). In 2017 he was elected as Chairperson of the Israeli Association for Artificial Intelligence.

10:00 am - 11:00 am

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

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"COPs, Bandits, and AI for Good"

In recent years, there has been an increasing interest in applying techniques from AI to tackle societal and environmental challenges, ranging from climate change and natural disasters, to food safety and disease spread. These efforts are typically known under the name AI for Good. While many research works in this area have been focusing on designing machine learning algorithms to learn new insights and predict future events from previously collected data, there is another domain where AI has been found to be useful, namely resource allocation and decision making. In particular, a key step in addressing societal and environmental challenges is to efficiently allocate a set of sparse resources to mitigate the problem(s). For example, in the case of wildfire, a decision maker has to adaptively and sequentially allocate a limited number of firefighting units to stop the spread of the fire as soon as possible. Another example comes from the problem of housing management for people in need, where a limited number of housing units have to be allocated to applicants in an online manner over time.

While sequential resource allocation can be often casted as (online) combinatorial optimisation problems (COPs), they can differ from the standard COPs when the decision maker has to perform under uncertainty (e.g., the value of the action is not known in advance, or future events are unknown at the decision-making stage). In the presence of such uncertainty, a popular tool from the decision-making literature, called multi-armed bandits, comes in handy. In this talk, I will demonstrate how to efficiently combine COPs with bandit models to tackle some AI for Good problems. In particular, I first show how to combine knapsack models with combinatorial bandits to efficiently allocate firefighting units and drones to mitigate wildfires. In the second part of the talk, I will demonstrate how interval scheduling, paired up with blocking bandits, can be a useful approach as a housing assignment method for people in need.

About the Speaker

Long is a Hungarian-Vietnamese computer scientist at the University of Warwick, U.K., where he is currently an associate professor. He obtained his Ph.D. in Computer Science from Southampton in 2012, under the supervision of Nick Jennings and Alex Rogers. Long has been doing active research in a number of key areas of AI and multi-agent systems, mainly focusing on multi-armed bandits, game theory, and incentive engineering, and their applications to crowdsourcing, human-agent learning, and AI for Good. He has published more than 60 papers at top AI conferences (AAAI, AAMAS, ECAI, IJCAI, NeurIPS, UAI) and journals (JAAMAS, AIJ), and has received a number of national and international awards, such as BCS/CPHC Best Computer Science Ph.D. Dissertation Award (2012/13 Honourable Mention); ECCAI/EurAI Best Artificial Intelligence Dissertation Award (2012/13 Honourable Mention); AAAI Outstanding Paper Award (2012 Honourable Mention); ECAI Best Student Paper Award (2012 Runner-Up); and IJCAI 2019 Early Career Spotlight Talk (invited). Long currently serves as a board member (2018-2024) of the IFAAMAS Directory Board, the main international governing body of the International Federation for Autonomous Agents and Multiagent Systems, a major sub-field of the AI community. He is also the local chair of the AAMAS 2021 conference.

2:30 pm - 3:30 pm

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

"Responsible AI: Thinking Beyond Data and Models"

The last decade has seen tremendous growth in artificial intelligence (AI) capabilities and its wide-spread adoption in society. Given the impact they have on our social lives, there have also been research on the fairness, accountability, and ethical values that underlie these technologies. While this line of research has gotten great attention in recent years, a majority of this work focuses primarily on mathematical interventions on the often opaque algorithms or models and/or their immediate inputs (data) and outputs (predictions). Such oversimplified mathematical interventions abstract away the underlying societal context where models are conceived, developed, and ultimately deployed. In this talk, I will discuss two strands of my recent work attempting to look beyond the data and models. First, I will discuss a complex systems theory based approach towards modeling societal context that accounts for its dynamic nature, including delayed impacts and feedback loops, and how to bring the expertise from marginalized communities into that process. Second, I discuss how the current literature on algorithmic fairness is rooted in Western concerns, histories, and values, and how this limits its portability to other geographies and cultures, especially in the Global South. In particular, I will discuss our recent work on re-imagining algorithmic fairness for the Indian context.

About the Speaker

Vinodkumar Prabhakaran is a Research Scientist at Google working on issues around ethics, fairness, and transparency in machine learning and natural language processing. Prior to Google, he was a postdoctoral researcher at Stanford University, and obtained his PhD in computer science from Columbia University. His prior research focused on building scalable ways to identify and address large-scale societal issues such as racial disparities in policing, workplace incivility, and online abuse. His work has been published in top-tier venues such as PNAS, ACL, NAACL, and EMNLP. He co-organizes the annual international workshop on online abuse and harms (WOAH), and served as a Diversity and Inclusion co-chair for ACL 2020.

2:30 pm - 3:30 pm
Online

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“Just, Equitable, and Efficient Algorithmic Allocation of Scarce Societal Resources”

Demand for resources that are collectively controlled or regulated by society, like social services or organs for transplantation, typically far outstrips supply. How should these scarce resources be allocated? Any approach to this question requires insights from computer science, economics, and beyond; we must define objectives (foregrounding equity and distributive justice in addition to efficiency), predict outcomes (taking causal considerations into account), and optimize allocations, while carefully considering agent preferences and incentives. Motivated by the real-world problem of provision of services to homeless households, I will discuss our approach to thinking through how algorithmic approaches and computational thinking can help.

About the Speaker

Sanmay Das is a Professor of Computer Science at George Mason University. His research interests are in designing effective algorithms for agents in complex, uncertain environments, and in understanding the social or collective outcomes of individual behavior. Dr. Das is chair of the ACM Special Interest Group on Artificial Intelligence, a member of the board of directors of the International Foundation for Autonomous Agents and Multiagent Systems, and serves as an associate editor of the ACM Transactions on Economics and Computation and of the Journal of Artificial Intelligence Research. Dr. Das has served as program co-chair of the AAMAS and AMMA conferences, in addition to regularly serving as an area chair or senior program committee member of major conferences including IJCAI, AAAI, EC, and AAMAS. He has been recognized with awards for research and teaching, including an NSF CAREER Award and the Department Chair Award for Outstanding Teaching at Washington University.

2:30 pm - 3:30 pm

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“AI for Population Health”

As exemplified by the COVID-19 pandemic, our health and wellbeing depend on a difficult-to-measure web of societal factors and individual behaviors. AI can help us untangle this web and optimize interventions to improve health at a population level, especially for marginalized groups. However, population health applications raise new computational challenges, requiring us to make sense of limited data and optimize decisions under the resulting uncertainty. This talk presents methodological developments in machine learning, optimization, and social networks which are motivated by on-the-ground collaborations on HIV prevention, tuberculosis treatment, and the COVID-19 response. These projects have produced deployed applications and policy impact. For example, I will present the development of an AI-augmented intervention for HIV prevention among homeless youth. This system was deployed and evaluated in a field test enrolling over 700 youth and found to significantly reduce the prevalence of key risk behaviors for HIV.

About the Speaker

Bryan Wilder is a final-year Ph.D. student in Computer Science at Harvard University, where he is advised by Milind Tambe. His research focuses on the intersection of optimization, machine learning, and social networks, with the goal of improving population health. His work has received or been nominated for best paper awards at ICML and AAMAS, and was a finalist for the INFORMS Doing Good with Good OR competition. He is supported by the Siebel Scholars program and previously received an NSF Graduate Research Fellowship.

2:30 pm - 3:30 pm
Online

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“Doing Good with Data: Fairly and Equitably”

Can AI, ML and Data Science help help prevent children from getting lead poisoning? Can it help reduce police violence and misconduct? Can it improve vaccination rates? Can it help cities better prioritize limited resources to improve lives of citizens and achieve equity? We’re all aware of the potential of ML and AI but turning this potential into tangible social impact, and more importantly equitable social impact, takes cross-disciplinary training, new methods, and collaborations with governments and non profits. I’ll discuss lessons learned from working on 50+ projects over the past few years with non-profits and governments on high-impact public policy and social challenges in criminal justice, public health, education, economic development, public safety, workforce training, and urban infrastructure. I’ll highlight opportunities as well as challenges around explainability and bias/fairness that need to tackled in order to have social and policy impact in a fair and equitable manner.

About the Speaker

Rayid Ghani is a Distinguished Career Professor in the Machine Learning Department and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. Rayid is a reformed computer scientist and wanna-be social scientist, focused on the use of large-scale AI/Machine Learning/Data Science in solving critical public policy and social challenges in a fair and equitable manner. Among other areas, Rayid works with governments and non-profits in policy areas such as health, criminal justice, education, public safety, economic development, and urban infrastructure. Rayid is also passionate about teaching practical data science and started the Data Science for Social Good Fellowship that trains computer scientists, statisticians, and social scientists from around the world to work on data science problems with social impact. Before joining Carnegie Mellon University, Rayid was the Founding Director of the Center for Data Science & Public Policy, Research Associate Professor in Computer Science, and a Senior Fellow at the Harris School of Public Policy at the University of Chicago. Previously, Rayid was the Chief Scientist of the Obama 2012 Election Campaign where he focused on data, analytics, and technology to target and influence voters, donors, and volunteers. In his ample free time, Rayid obsesses over everything related to coffee and works with non-profits to help them with their data, analytics and digital efforts and strategy.

11:00 am - 12:00 pm
Online

This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.

Watch This Talk

“AI for Public Health and Conservation: Learning and Planning in the Data-to-Deployment Pipeline”

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. We focus on the problems of public health and wildlife conservation, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present our deployments from around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. Achieving social impact in these domains often requires methodological advances; we will highlight key research advances in topics such as computational game theory, multi-armed bandits and influence maximization in social networks for addressing challenges in public health and conservation. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society.

About the Speaker

Dr. Milind Tambe is Gordon McKay Professor of Computer Science and Director of Center for Research in Computation and Society at Harvard University; concurrently, he is also Director “AI for Social Good” at Google Research India. He is the recipient of the IJCAI (International Joint Conference on AI) John McCarthy Award, ACM/SIGAI Autonomous Agents Research Award from AAMAS (Autonomous Agents and Multiagent Systems Conference), AAAI (Association for Advancement of Artificial Intelligence) Robert S Engelmore Memorial Lecture award, INFORMS (Institute for Operations Research and the Management Sciences) Wagner prize, the Rist Prize of the Military Operations Research Society, the Christopher Columbus Fellowship Foundation Homeland security award, International Foundation for Agents and Multiagent Systems influential paper award, best paper awards at conferences including AAMAS, IJCAI, IVA. He has also received meritorious Team Commendation from the US Coast Guard and Los Angeles Airport, and Certificate of Appreciation from US Federal Air Marshals Service for pioneering real-world use of security games. He is a fellow of AAAI and ACM.