Papers

Julius Adebayo and Lalana Kagal: Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models

Emrah Akyol, Cedric Langbort, and Tamer Basar: Price of Transparency in Strategic Machine Learning

Aws Albarghouthi, Loris D'Antoni, Samuel Drews, and Aditya Nori: Fairness as a Program Property

Sarah Bird, Solon Barocas, Fernando Diaz, Hanna Wallach, and Kate Crawford: Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI

L. Elisa Celis, Amit Deshpande, Tarun Kathuria, and Nisheeth Vishnoi: How to Be Fair and Diverse?

Alexandra Chouldechova: Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments

Miguel Ferreira, Muhammad Bilal Zafar, and Krishna Gummadi: The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems

Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth: Fair Learning in Markovian Environments

Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth: Rawlsian Fairness for Machine Learning

Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan: Inherent Trade-Offs in the Fair Determination of Risk Scores

Kristian Lum and James Johndrow: A Statistical Framework for Fair Predictive Algorithms

Ke Yang and Julia Stoyanovich: Measuring Fairness in Ranked Outputs

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna Gummadi: Fairness Beyond Disparate Treatment and Disparate Impact: Learning Classification without Disparate Mistreatment