- Fairness Risk Measures, Robert Williamson and Aditya Menon
- How Do Classifiers Induce Agents To Invest Effort Strategically?, Jon Kleinberg and Manish Raghavan
- Policy Certificates: Towards Accountable Reinforcement Learning, Christoph Dann, Lihong Li, Wei Wei and Emma Brunskill
- Regulatory frameworks relating to data privacy and algorithmic decision making in the context of algorithmic bias, Adam Leon Smith, Abhik Chaudhuri, Allison Gardner, Linda Gu, Malek Ben Salem and Maroussia Levesque
- Interpretable Fairness via Target Labels in Gaussian Process Models, Thomas Kehrenberg, Zexun Chen and Novi Quadrianto
- Fairness through Causal Awareness: Learning Latent-Variable Models for Biased Data, David Madras, Elliot Creager, Toniann Pitassi and Richard Zemel
- Intersectionality: Multiple Group Fairness in Expectation Constraints, Jack Fitzsimons, Michael Osborne and Stephen Roberts
- Investigating Human + Machine Complementarity for Recidivism Predictions, Sarah Tan, Julius Adebayo, Kori Inkpen and Ece Kamar
- Envy-Free Classification, Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu and Ariel Procaccia
- Equality of Opportunity in Classification: A Causal Approach, Junzhe Zhang and Elias Bareinboim
- BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity, Alexey Gritsenko, Alex D’amour, James Atwood, Yoni Halpern and D. Sculley
- Avoiding Disparate Impact with Counterfactual Distributions, Hao Wang, Berk Ustun and Flavio Calmon
- Explaining Explanations to Society, Leilani Gilpin, Cecilia Testart, Nathaniel Fruchter and Julius Adebayo
- Fairness in the Face of Uncertainty, Michael Wang and Swati Gupta
- Actionable Recourse in Linear Classification, Berk Ustun, Alexander Spangher and Yang Liu
- Temporal Aspects of Individual Fairness, Vijay Kamble and Swati Gupta
- One-network Adversarial Fairness, Tameem Adel, Isabel Valera, Zoubin Ghahramani and Adrian Weller
- Actuarial Fairness, Dimitri Semenovich and Chris Dolman