Friday, April 5 , 2019

9:00-9:30 Breakfast, Welcome, and Opening Remarks (Ronald Sandler, Director, Ethics Institute and Laura Green, Associate Dean of Teaching and Experiential Education)

9:35-10:35Predictive Policing: Unbiased and Unfair?

Duncan Purves (Assistant Professor, Department of Philosophy, University of Florida)

Commentator: John Basl (Assistant Professor of Philosophy, Northeastern University)

10:40-11:40 —Interpretability is a Red Herring: Grappling with “Prediction Policy Problems”-

Momin Malik (Data Science Postdoctoral Fellow at the Berkman Klein Center for Internet & Society)

Commentator: Joshua Simons (Simons is a PhD candidate in Government at Harvard University)

11:40-12:40 Catered Lunch

12:40-1:40 —What is the Organizational Counterfactual? Categorical versus Algorithmic Prioritization in U.S. Social Policy

Rebecca Johnson and Simone Zhang

(Johnson is an incoming Assistant Professor, Quantitative Social Science at Dartmouth, Zhang is a graduate student in Sociology at Princeton University)


1:45-2:45Against False Positive Rate Equality as a  Measure of Fairness in Machine Learning

Rob Long (PhD student in Philosophy at New York University)

Commentator: David Gray Grant

2:45-3:15 Coffee Break

3:15-4:15The Politics of Data: Discrimination or Justice?

Joshua Simons and Yonadav Shavit

(Simons is a PhD candidate in Government at Harvard University, Shavit is a doctoral candidate in Computer Science at Harvard University)

Commentator: Rob Long

4:20-5:50 Keynote Reuben BinnsThe Relevance and Irrelevance of Philosophy for Algorithmic Decision-making

6:30-8:30 Speaker Dinner (By invitation only)

Saturday, April 6, 2019

9:00 Breakfast

9:30-10:30The Impact of Algorithms on Judicial Discretion: Evidence from Regression Discontinuities

Bo Cowgill (Assistant Professor, Columbia Business School)

Commentator: Sneha Jha

10:30-10:55 Coffee Break

10:55-12:25 Keynote Tina Eliassi-RadJust Machine Learning

12:25-2:00 Lunch at Local Restaurants

2:00-3:00Pre-Trial Algorithmic Risk Assessments: Value Conflicts, Inherent Limitations, and Harm-Mitigation by Design

Marc Faddoul and Henriette Ruhrmann

(Faddoul is a student at University California Berkeley, School of Information, Ruhrmann is a student at University California Berkeley, School of Public Policy)

Commentator: Rebecca Johnson

3:05-4:05Fairness in Machine Learning: A Measurement Theory Perspective

Eran Tal (Assistant Professor of Philosophy, McGill University)

Commentator: Branden Fitelson (Distinguished Professor of Philosophy, Northeastern University)

4:05-5:35 Coffee Break

5:35-6:35Turning the Tables: Scoring the Algorithms that Score Us

Shea Brown (Faculty member in the Department of Physics & Astronomy, University of Iowa)

Commentator: Sainyam Galhotra

6:35-8:00 Reception (Over 21 Only)

Sunday, April 7, 2019

9:00 Breakfast

9:15-10:45 Keynote Solon BarocasIn All Fairness

10:45-11:10 Coffee Break

11:10-12:10On Becoming Human: An African Notion of Justice and Fairness in Machine Learning

Sabelo Mhlambi (Computer Scientist and Fellow at Harvard Berkman Klein Center for Internet & Society and Carr Center for Human Rights Policy)

Commentator: John Buschman

12:10-1:30 Catered Lunch

1:30-2:30Designing for Values in Machine Learning Practice – A Sociotechnical Approach

Roel Dobbe (Postdoctoral Researcher with the AI Now Institute, New York University)


2:35-3:35  Panel Session: Apophenia in Machine Learning Algorithms

Panelist: Masooda Bashir (Assistant Professor, School of Information Sciences, University of Illinois at Urbana-Champaign), Dr. Dong San Choi, Garrett O’Grady, Bhuvan Venkatesh, Yasha Mostofi