Program

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)

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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