2019 Justice and Fairness in Data Use and Machine Learning

April 5-7, 2019 | 909 Renaissance Park, Northeastern University


17th Annual Information Ethics Roundtable

Justice and Fairness in Data Use and Machine Learning

April 5-7
Northeastern University

The 17th annual Information Ethics Roundtable will explore the relationship between the normative notions of justice and fairness and current practices of data use and machine learning.

Artificial intelligence is now a part of our everyday lives. It allows us to easily get to a place we have never been before, while avoiding traffic and road work, to communicate with our Chinese friend when we don’t share a common language, and to carry out complex but mind numbing repetitive jobs in factories. But such artificial intelligences can also exhibit what we might call “artificial bias;” that is, machine behavior that, if produced by a person, we would say is biased against particular groups, such as racial minorities. Machine learning using large data sets is one means of achieving AI that is particularly vulnerable to producing biased systems, because it uses data from human behavior that is itself biased. A number of tech companies, such as Google and IBM, and computer science researchers are currently seeking ways to correct for such biases and to produce “fair” algorithms. But a number of fundamental questions about bias, fairness, and even justice still need to be answered if we are to solve this problem.




Friday, April 5 

9:00-9:30 Breakfast and Welcome

9:35-10:35 Duncan Purves—Predictive Policing: Unbiased and Unfair

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

11:40-12:40 Catered Lunch

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

1:45-2:45 Eran Tal—Fairness in Machine Learning: A Measurement Theory Perspective

2:45-3:15 Coffee Break

3:15-4:15 Joshua Simons and Yonadav Shavit—The Politics of Data: Discrimination or Justice?

4:20-5:50 Keynote Rueben Binns—Title TBA

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


Saturday, April 6

9:00 Breakfast

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

10:30-10:55 Coffee Break

10:55-12:25 Keynote Tina Eliassi-Rad—Title TBA

12:25-2:00 Lunch at Local Restaurants

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

3:05-4:05 Rob Long—Title TBA

4:05-5:35 Coffee Break

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

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


Sunday, April 7

9:00 Breakfast

9:15-10:45 Keynote Solon Barocas—Title TBA

10:45-11:10 Coffee Break

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

12:10-1:30 Catered Lunch

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

2:35-3:35 Panel Session (Masooda Bashir, et al)—Title TBA