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Found 4 papers, 2 papers with code
Enforcing Delayed-Impact Fairness Guarantees
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
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.
Offline Contextual Bandits with High Probability Fairness Guarantees
We present RobinHood, an offline contextual bandit algorithm designed to satisfy a broad family of fairness constraints.
Reinforcement Learning When All Actions are Not Always Available
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.
