ICSR Project Teams
Health is important, and improvements in health improve lives. However, we still don’t fundamentally understand what it means to be healthy, and the same patient may receive different treatments across different hospitals or clinicians as new evidence is discovered, or individual illness is interpreted. Unlike many problems in machine learning – games like Go, self-driving cars, object recognition – disease management does not have well-defined rewards that can be used to learn rules. Models must also work to not learn biased rules or recommendations that harm minorities or minoritized populations. These projects tackle the many novel technical opportunities for machine learning in health, and work to make important progress in health and health equity.
Lead by Marzyeh Ghassemi, PhD (Assistant Professor, MIT CSAIL and Director Healthy ML), the Healthcare vertical team members are Hammaad Adam (MIT PhD Student, Social & Engineering Systems), Kenrick Cato (Nurse Researcher, New York-Presbyterian Hospital and Assistant Professor at Columbia University School of Nursing), Charles Senteio, PhD (Assistant Professor of Library and Information Science, Rutgers), and Mingying Yang (MIT Student of Chemical Engineering).
Housing policies enacted today are not applied in a vacuum. They are implemented in an environment that has evolved through a long, complex history which includes discriminatory policies and practices put into effect by various actors, ranging from the government to banks to private citizens. These racially inequitable policies include racial covenants, redlining, predatory lending and more. Emerging research shows that AI and algorithmic systems are exacerbating rather than ameliorating these existing inequities. In our work, we take a data-driven approach to think beyond algorithmic fairness and account for historical aspects in evaluating housing equity. Our primary objective in the housing vertical is to reveal mechanisms that lead to racially disparate outcomes in housing and to identify the most impactful intervention points to disrupt these mechanisms. In particular, we are focused on three topics: evictions and housing security; home ownership and lending; and health disparities that result from residential segregation.
The Housing vertical team consists of vertical co-leads Peko Hosoi, Associate Dean of Engineering and Neil and Jane Pappalardo Professor, MechE and Catherine D’Ignazio (Assistant Professor Urban Science and Planning at MIT), Bhavani Ananthabhotla (TPP Masters Student at MIT), Wonyoung So (MIT PhD student DUSP and RA /technical lead, Data + Feminism Lab at MIT), and Aurora Zhang (MIT PhD Student, Social & Engineering Systems).
The central theme of the project is to understand the role of data in the design of racially (and otherwise) unbiased policies for emergency and police priority dispatch system, policing, justice system, correction facilities and beyond. Towards that, the project aims to develop publicly available comprehensive “data hub” to foster the role of data in policy design as well as develop analytic methods to evaluate biases (racial and otherwise) using the data.
Devavrat Shah, Professor at MIT EECS, leads the Policing vertical team that consists of Anish Agarwal (MIT PhD Student, EECS), Sarah Cen (MIT PhD Student, EECS), Fotini Christia (Ford International Professor in the Social Sciences at MIT), Roberto Fernandez (William F. Pounds Professor in Management and Professor, Work and Organization Studies at MIT), Jessy Han (MIT PhD Student, Social & Engineering Systems), Andrew Miller (Assistant Professor of PoliSci at United States Naval Academy), Renbo Tu (Graduate Student at CMU), and Chris Winship (Diker-Tishman Professor of Sociology at Harvard).
A society-wide failure of algorithmic transparency is currently perpetuating innumerable social, economic and health related risks around the world. While algorithms play a role in the proliferation of extremism online — be it white nationalism or other toxic social phenomena — we know very little about how these algorithms operate and how their operation is affecting users; researchers have no robust way to probe how or why extremism emerges and the role that algorithms play in its development. Our team is working on causal investigations of algorithmic responses to user behavior and the evolving dialectic between user behavior and algorithmic recommendations.
Lead by Sinan Aral, David Austin Professor of Management at the MIT Sloan School of Management, the Social Media vertical team consists of Dean Eckles (Associate Professor of Marketing at MIT Sloan), Chris Hayes (MIT PhD Student, Social & Engineering Systems), Manish Raghavan (postdoctoral fellow, Harvard University Center for Research on Computation and Society), Zach Schutzman (postdoctoral associate, MIT IDSS), Erin Walk (MIT PhD Student, Social & Engineering Systems), and Philipp Zimmer (MIT Technology and Policy Program).
Contributors to the edited volume on systemic racism and computation
The essays are based on the presentations given during the ICSR workshop series and explore the potential for data to both combat and perpetuate systemic racism in the U.S. Topics included healthcare inequities, policing, algorithm bias, and more.