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Tag: fair

  • Meta Optimality for Demographic Parity Constrained Regression via Post-Processing

    Meta Optimality for Demographic Parity Constrained Regression via Post-Processing arXiv:2506.13947v1 Announce Type: new Abstract: We address the regression problem under the constraint of demographic parity, a commonly used fairness definition. Recent studies have revealed fair minimax optimal regression algorithms, the most accurate algorithms that adhere to the fairness constraint. However, these analyses are tightly coupled…

  • Fair Sufficient Representation Learning

    Fair Sufficient Representation Learning arXiv:2504.01030v1 Announce Type: new Abstract: The main objective of fair statistical modeling and machine learning is to minimize or eliminate biases that may arise from the data or the model itself, ensuring that predictions and decisions are not unjustly influenced by sensitive attributes such as race, gender, age, or other protected…

  • Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing

    Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing arXiv:2501.06366v1 Announce Type: new Abstract: When applied in healthcare, reinforcement learning (RL) seeks to dynamically match the right interventions to subjects to maximize population benefit. However, the learned policy may disproportionately allocate efficacious actions to one subpopulation, creating or exacerbating disparities in other socioeconomically-disadvantaged subgroups. These biases…