Friday Dec. 7th @ Room 281
08:20 AM Welcome and organisers comments (Introduction)
08:30 AM Fairness, Simplicity, and Ranking - Jon Kleinberg, Cornell University (Invited Talk)
09:00 AM Justice May Be Blind But It Shouldn’t Be Opaque: The Risk of Using Black-Box Models in Healthcare & Criminal Justice - Rich Caruna, Microsoft Research (Invited Talk)
09:30 AM What Can Fair ML Learn from Economic Theories of Distributive Justice? - Hoda Heidari, ETH zurich (Invited Talk)
10:00 AM Poster Spotlights 1 (Spotlight talks)
- Fairness Risk Measures, Robert Williamson and Aditya Menon
- Actuarial Fairness, Dimitri Semenovich and Chris Dolman
- Policy Certificates: Towards Accountable Reinforcement Learning, Christoph Dann et al.
- Regulatory frameworks relating to data privacy and algorithmic decision making in the context of algorithmic bias, Adam Leon Smith et al.
- Fairness through Causal Awareness: Learning Latent-Variable Models for Biased Data, David Madras et al.
- Investigating Human + Machine Complementarity for Recidivism Predictions, Sarah Tan et al.
- Equality of Opportunity in Classification: A Causal Approach, Junzhe Zhang and Elias Bareinboim
- One-network Adversarial Fairness, Tameem Adel et al.
10:20 AM Posters 1 (Poster Session and Morning Tea)
11:30 AM BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity - Alexey Gritsenko et al, Google AI (Contributed Talk)
11:50 AM Temporal Aspects of Individual Fairness - Vijay Kamble & Swati Gupta, University of Illinois at Chicago & Georgia Institute of Technology (Contributed Talk)
12:10 PM Explaining Explanations to Society Leilani Gilpin et al, MIT (Contributed Talk)
12:30 PM Lunch
02:00 PM Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? - Hannah Wallach, Microsoft Research (Invited Talk)
02:30 PM Ethics & Accountability in AI and Algorithmic Decision Making Systems - There's No Such Thing As A Free Lunch - Roel Dobbe, AI Now Institute (Invited Talk) Download Slides
03:00 PM Poster Spotlights 2 (Spotlight talks)
- Interpretable Fairness via Target Labels in Gaussian Process Models, Thomas Kehrenberg et al.
- Intersectionality: Multiple Group Fairness in Expectation Constraints, Jack Fitzsimons et al
- Envy-Free Classification, Maria-Florina Balcan et al.
- Avoiding Disparate Impact with Counterfactual Distributions, Hao Wang et al
- Fairness in the Face of Uncertainty, Michael Wang and Swati Gupta
- Actionable Recourse in Linear Classification, Berk Ustun et al
- How Do Classifiers Induce Agents To Invest Effort Strategically?, Jon Kleinberg and Manish Raghavan
03:20 PM Posters 2 (Poster session and Afternoon Tea)
04:30 PM Enhancing the Accuracy and Fairness of Human Decision Making - Manuel Gomez Rodriguez, Max Planck Institute (Invited Talk)
05:00 PM Discussion Panel