arXiv Statistics ML
5d ago

Targeted Learning for Inference on Data Fairness

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.

By Joseph H. Rudoler, Alexander Asemota, Giles Hooker
arXiv Machine Learning
Oct 16

A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction

The paper introduces a post‑processing framework that uses model ensembling to achieve fairness‑aware predictions. It is designed to work independently of any specific model internals, making it applicable to a wide range of learning tasks, model architectures, and fairness definitions. Experiments on classification, regression, and survival analysis show that the framework improves fairness while preserving predictive accuracy.

By Zhouting Zhao, Tin Lok James Ng
arXiv Machine Learning
Jun 2

Beyond Procedure: Substantive Fairness in Conformal Prediction

arXiv:2602. 16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored.

By Pengqi Liu, Zijun Yu, Mouloud Belbahri, Arthur Charpentier, Masoud Asgharian, Jesse C. Cresswell
arXiv Machine Learning
Jun 2

FedCF: Fair Federated Conformal Prediction

arXiv:2509. 22907v2 Announce Type: replace Abstract: Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models.

By Anutam Srinivasan, Aditya T. Vadlamani, Amin Meghrazi, Srinivasan Parthasarathy