Search Results for author: Blossom Metevier

Found 4 papers, 2 papers with code

Enforcing Delayed-Impact Fairness Guarantees

no code implementations24 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.


Fairness Guarantees under Demographic Shift

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.


Reinforcement Learning When All Actions are Not Always Available

1 code implementation5 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.

Decision Making reinforcement-learning +1

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