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Found 4 papers, 1 papers with code
Interpretable Assessment of Fairness During Model Evaluation
For companies developing products or algorithms, it is important to understand the potential effects not only globally, but also on sub-populations of users.
A Framework for Fairness in Two-Sided Marketplaces
Incorporating fairness while building such systems is crucial and can have a deep social and economic impact (applications include job recommendations, recruiters searching for candidates, etc.).
Personalized Treatment Selection using Causal Heterogeneity
In this work, we develop a framework for personalization through (i) estimation of heterogeneous treatment effect at either a cohort or member-level, followed by (ii) selection of optimal treatment variants for cohorts (or members) obtained through (deterministic or stochastic) constrained optimization.
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