Survey on Fair Reinforcement Learning: Theory and Practice

20 May 2022

Papers citing "Survey on Fair Reinforcement Learning: Theory and Practice"

6 / 6 papers shown

Title
DECAF: Learning to be Fair in Multi-agent Resource Allocation Ashwin Kumar William Yeoh 76 1 0 06 Feb 2025
Fair Resource Allocation in Weakly Coupled Markov Decision Processes Xiaohui Tu Yossiri Adulyasak Nima Akbarzadeh Erick Delage 29 0 0 14 Nov 2024
Achieving Counterfactual Fairness for Causal Bandit Wen Huang Lu Zhang Xintao Wu CML 101 22 0 21 Sep 2021
Achieving User-Side Fairness in Contextual Bandits Wen Huang Kevin Labille Xintao Wu Dongwon Lee Neil T. Heffernan FaML 64 18 0 22 Oct 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 286 4,143 0 23 Aug 2019
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,079 0 24 Oct 2016