no code implementations • 24 Aug 2022 • Aline Weber, Blossom Metevier, Yuriy Brun, Philip S. Thomas, Bruno Castro da Silva
Recent research has shown that seemingly fair machine learning models, when used to inform decisions that have an impact on peoples' lives or well-being (e. g., applications involving education, employment, and lending), can inadvertently increase social inequality in the long term.
no code implementations • ICLR 2022 • Stephen Giguere, Blossom Metevier, Yuriy Brun, Philip S. Thomas, Scott Niekum, Bruno Castro da Silva
Recent studies have demonstrated that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes.
1 code implementation • NeurIPS 2019 • Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas
We present RobinHood, an ofﬂine contextual bandit algorithm designed to satisfy a broad family of fairness constraints.
1 code implementation • 5 Jun 2019 • Yash Chandak, Georgios Theocharous, Blossom Metevier, Philip S. Thomas
The Markov decision process (MDP) formulation used to model many real-world sequential decision making problems does not efficiently capture the setting where the set of available decisions (actions) at each time step is stochastic.