WebMar 20, 2024 · Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it the same in (a) the actual world and (b) a counterfactual … WebSep 30, 2024 · A predictor Y ^ is considered counterfactually fair if A is not a cause of Y ^ in any individual instance (Kusner et al., 2024). Or equivalently, when the distribution of Y ^ remains identical while changing the value of A and holding constant all variables not causally affected by A ( Kusner et al., 2024 ).
NSF CAREER Award to Study Long-Term Fairness in Sequential …
WebIn this paper, we address this limitation by mathematically bounding the unidentifiable counterfactual quantity, and develop a theoretically sound algorithm for constructing … WebMay 20, 2024 · To this end, we introduce a framework for achieving counterfactually fair recommendations through adversary learning by generating feature-independent user embeddings for recommendation. The framework allows recommender systems to achieve personalized fairness for users while also covering non-personalized situations. … joseph stroh mylife fairfax
Causal intersectionality for fair ranking DeepAI
WebMar 21, 2024 · Counterfactually Fair Regression with Double Machine Learning. Counterfactual fairness is an approach to AI fairness that tries to make decisions based … WebDec 8, 2024 · Dec. 08, 2024. Lu Zhang, assistant professor in the Department of Computer Science and Computer Engineering, has been awarded a $484,828 grant from the National Science Foundation division of Information and Intelligent Systems (NSF IIS) to support his research, "III: Small: Counterfactually Fair Machine Learning through Causal Modeling." WebOct 1, 2024 · Counterfactually Fair Prediction Using Multiple Causal Models. In this paper we study the problem of making predictions using multiple structural casual models … how to know if you have tome of bone binding