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Workshop: Algorithmic Fairness through the Lens of Causality and Interpretability

Awa Dieng, Jessica Schrouff, Matt J Kusner, Golnoosh Farnadi, Fernando Diaz

2020-12-12T01:00:00-08:00 - 2020-12-12T12:10:00-08:00
Abstract: Black-box machine learning models have gained widespread deployment in decision-making settings across many parts of society, from sentencing decisions to medical diagnostics to loan lending. However, many models were found to be biased against certain demographic groups. Initial work on Algorithmic fairness focused on formalizing statistical measures of fairness, that could be used to train new classifiers. While these models were an important first step towards addressing fairness concerns, there were immediate challenges with them. Causality has recently emerged as a powerful tool to address these shortcomings. Causality can be seen as a model-first approach: starting with the language of structural causal models or potential outcomes, the idea is to frame, then solve questions of algorithmic fairness in this language. Such causal definitions of fairness can have far-reaching impact, especially in high risk domains. Interpretability on the other hand can be viewed as a user-first approach: can the ways in which algorithms work be made more transparent, making it easier for them to align with our societal values on fairness? In this way, Interpretability can sometimes be more actionable than Causality work.

Given these initial successes, this workshop aims to more deeply investigate how open questions in algorithmic fairness can be addressed with Causality and Interpretability. Questions such as: What improvements can causal definitions provide compared to existing statistical definitions of fairness? How can causally grounded methods help develop more robust fairness algorithms in practice? What tools for interpretability are useful for detecting bias and building fair systems? What are good formalizations of interpretability when addressing fairness questions?



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2020-12-12T01:00:00-08:00 - 2020-12-12T01:20:00-08:00
AFCI2020: Opening remarks
Awa Dieng
2020-12-12T01:20:00-08:00 - 2020-12-12T01:47:00-08:00
Tutorial: A brief tutorial on causality and fair decision making
Moritz Hardt
2020-12-12T01:47:00-08:00 - 2020-12-12T01:55:00-08:00
Tutorial: Questions
2020-12-12T01:55:00-08:00 - 2020-12-12T02:25:00-08:00
Invited Talk: On Prediction, Action and Interference
Ricardo Silva
2020-12-12T02:25:00-08:00 - 2020-12-12T02:30:00-08:00
Questions: Invited talk, R. Silva
2020-12-12T02:30:00-08:00 - 2020-12-12T02:38:00-08:00
Short break -- Join us on Gathertown
2020-12-12T02:38:00-08:00 - 2020-12-12T02:40:00-08:00
Introduction to contributed talks
2020-12-12T02:40:00-08:00 - 2020-12-12T02:50:00-08:00
Contributed Talk 1: The Importance of Modeling Data Missingness in Algorithmic Fairness
Naman Goel, Amit Deshpande
2020-12-12T02:50:00-08:00 - 2020-12-12T03:00:00-08:00
Contributed Talk 2: Foundations for Languages for Interpretability and Bias Detection
Bernardo Subercaseaux, Jorge Pérez, Pablo Barceló
2020-12-12T03:00:00-08:00 - 2020-12-12T03:10:00-08:00
Contributed Talk 3: Fairness in Risk Assessment: Post-Processing to Achieve Counterfactual Equalized Odds
Alan Mishler, Edward Kennedy, Alexandra Chouldechova
2020-12-12T03:10:00-08:00 - 2020-12-12T03:13:00-08:00
Introduction to invited talk by Hoda Heidari
2020-12-12T03:13:00-08:00 - 2020-12-12T03:38:00-08:00
Invited Talk: Fairness through the lens of equality of opportunity and its connection to causality
Hoda Heidari
2020-12-12T03:38:00-08:00 - 2020-12-12T03:45:00-08:00
Questions: Invited talk by H. Heidari + Intro to Breakout Sessions 1
2020-12-12T03:45:00-08:00 - 2020-12-12T03:55:00-08:00
Short break -- Join us on Gathertown
2020-12-12T03:55:00-08:00 - 2020-12-12T04:55:00-08:00
Virtual Breakout Session 1
2020-12-12T04:55:00-08:00 - 2020-12-12T05:00:00-08:00
Introduction to Poster session
2020-12-12T05:00:00-08:00 - 2020-12-12T06:00:00-08:00
Poster session 1 -- Join us on Gathertown
2020-12-12T06:00:00-08:00 - 2020-12-12T07:55:00-08:00
Long break -- Join us on Gathertown
2020-12-12T07:55:00-08:00 - 2020-12-12T08:00:00-08:00
Introduction to invited talk by Jon Kleinberg
2020-12-12T08:00:00-08:00 - 2020-12-12T08:30:00-08:00
Invited Talk: The Roles of Simplicity and Interpretability in Fairness Guarantees
Jon Kleinberg
2020-12-12T08:32:00-08:00 - 2020-12-12T08:40:00-08:00
Questions: Invited talk, J. Kleinberg
2020-12-12T08:40:00-08:00 - 2020-12-12T08:50:00-08:00
Contributed talks 4: Counterfactual Learning of Fair Interventions
Kristina Lerman
2020-12-12T08:50:00-08:00 - 2020-12-12T09:00:00-08:00
Contributed talks 5: Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification
Elliot Creager, David Madras, Richard Zemel
2020-12-12T09:00:00-08:00 - 2020-12-12T09:10:00-08:00
Contributed talks 6: Group Fairness by Probabilistic Modeling with Latent Fair Decisions
YooJung Choi, Guy Van den Broeck
2020-12-12T09:10:00-08:00 - 2020-12-12T09:15:00-08:00
Short Break -- Join us on Gathertown
2020-12-12T09:15:00-08:00 - 2020-12-12T09:18:00-08:00
Introduction to invited talk by Lily Hu
2020-12-12T09:18:00-08:00 - 2020-12-12T09:40:00-08:00
Invited Talk: Does Causal Thinking About Discrimination Assume a Can Opener?
Lily Hu
2020-12-12T09:40:00-08:00 - 2020-12-12T09:55:00-08:00
Questions: Invited talk by L. Hu + Intro to Breakout Sessions 2
2020-12-12T09:55:00-08:00 - 2020-12-12T10:55:00-08:00
Virtual Breakout Session 2
2020-12-12T10:55:00-08:00 - 2020-12-12T11:55:00-08:00
Poster session 2 -- Join us on Gathertown
2020-12-12T11:55:00-08:00 - 2020-12-12T12:10:00-08:00
AFCI2020: Closing remarks
Jessica Schrouff