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Counterfactual Fairness
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Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it is the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school.
Forward citations
Cited by 12 Pith papers
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Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
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A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning
A quantile-based data preprocessing method, CFSMDM, makes offline reinforcement learning approximately counterfactually fair under non-additive noise, with bounded suboptimality and unfairness.
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FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents
FairDiffuseVQVAE reaches state-of-the-art fairness on the standard tabular benchmark (DPR 0.702, EOR 0.686) by uniform protected-attribute sampling at inference, paying ~15 AUC points of utility.
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Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
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AgentFairBench: Do LLM Agents Discriminate When They Act?
AgentFairBench is a multi-domain benchmark for demographic disparity in LLM agent actions, with a pilot showing no significant effect for Claude Haiku 4.5 after arity-matched noise correction.
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Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
Models that appear demographically neutral in AI resume screening can actually be incompetent evaluators, a pattern the paper calls the Illusion of Neutrality.
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Local Statistical Parity for the Estimation of Fair Decision Trees
A decision tree satisfies statistical parity if every node split is independent of the protected attribute, a condition C-LRT approximates with constrained logistic splits.
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Large language models perpetuate bias in palliative care: development and analysis of the Palliative Care Adversarial Dataset (PCAD)
GPT-4o generated biased palliative care responses in a substantial fraction of adversarial and counterfactual test questions, according to expert grading.
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MACAW: A Causal Generative Model for Medical Imaging
A single causal normalizing flow with masked autoencoders generates counterfactual 2D brain MRIs and performs Bayesian age classification.
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Online Learning with Multiple Fairness Regularizers via Graph-Structured Feedback
Develops a bandit algorithm with graph feedback that learns weights for multiple fairness constraints adaptively over sequential interactions.
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Fairness Aware Reinforcement Learning via Proximal Policy Optimization
Adding retrospective and prospective reward-disparity penalties to PPO lowers demographic parity and conditional statistical parity disparities in two multi-agent simulations, at a measurable efficiency cost.
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Critical Appraisal of Fairness Metrics in Clinical Predictive AI
A scoping review of 62 fairness metrics for clinical predictive AI finds a fragmented, threshold-dependent landscape with only one clinical utility metric.
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