The paper presents a method to estimate the Bayes optimal fairness‑accuracy tradeoff using only soft labels and group information. It derives an explicit expression for the fair Bayes error rate and constructs consistent, instance‑free estimators with provable bias bounds and sample complexity guarantees. Empirical results show that the estimators provide a robust, low‑variance estimate of the optimal fairness‑accuracy Pareto frontier even when soft labels are noisy.
arXiv:2606. 00656v1 Announce Type: cross Abstract: Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent in machine learning models.
arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.
The paper introduces a targeted learning approach to assess fairness in the data generating process used for training predictive models, rather than focusing solely on model performance. It derives estimators for demographic parity, equal opportunity, and conditional mutual information, and demonstrates through simulations that these estimators improve confidence‑interval coverage and reduce false detections of population unfairness compared to fixed‑model inference. The authors also apply the method to real data to illustrate its practical utility.
arXiv:2407. 14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds.
The paper introduces soft‑label‑based estimators for the Bayes‑optimal balanced error rate (BER) and area under the ROC curve (AUC), extending from a clean setting with known class priors to a realistic scenario with unknown priors and corrupted soft labels. It also adapts the FeeBee evaluation framework to assess these estimators without needing the true optimum, providing practical evaluation scores for any estimator of optimal BER or AUC. Experiments on synthetic and real datasets confirm the effectiveness of both the estimators and the evaluation method.
arXiv:2601. 23221v2 Announce Type: replace Abstract: As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical resort.
arXiv:2603. 21393v2 Announce Type: replace Abstract: The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness.
FairSplit decomposes the embedding space into a Fair Space for prediction and a Sensitive Space that isolates sensitive information, using a learnable mask and the Hilbert‑Schmidt Independence Criterion (HSIC) to guide the separation. The method simultaneously trains for classification accuracy and independence from sensitive attributes, providing theoretical bounds on the demographic parity gap based on the Fair Space dimensionality and HSIC. It supports multiclass tasks and non‑binary sensitive attributes, achieving competitive accuracy‑fairness trade‑offs on established benchmarks.
arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.
The paper investigates the tradeoff between fairness and accuracy when a single predictive model serves multiple demographic groups. It introduces the fairness-accuracy (FA) Pareto frontier, which identifies models that cannot be improved in either dimension without compromising the other. Focusing on finite-sample settings, the authors derive worst‑case‑optimal estimators, analyze how limited data asymmetrically affect each group’s welfare, and provide uniform bounds along the entire FA frontier.
arXiv:2606. 17810v1 Announce Type: cross Abstract: In this paper, we establish a set of theoretical impossibility results, termed the No-Free-Fairness theorems, that identify three fundamental sources of disparity in learning systems.
arXiv:2402. 01811v2 Announce Type: replace Abstract: Credit scoring has been catalogued by the European Commission and the Executive Office of the US President as a high-risk classification task, in light of the potential harms of making loan approval decisions based on models that would be biased against certain groups.