Comparer des méthodes

	Apprentissage automatique conscient de l'équité	Régression logistique
Domaine	Apprentissage automatique	Statistiques de recherche
Famille	Machine learning	Process / pipeline
Année d'origine	2016	1958
Auteur d'origine	Moritz Hardt, Eric Price & Nati Srebro	David Roxbee Cox
Type	Constrained supervised learning framework	Method
Source fondatrice	Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	Algorithmic Fairness, Fair Classification, Bias-Mitigating ML, Adil Makine Öğrenmesi	logit model, binomial logistic regression, LR
Apparentées	2	3
Résumé	Fairness-Aware Machine Learning is a family of techniques that train, constrain, or post-process predictive models so that their error rates or outcomes are equitable across protected demographic groups such as race, gender, or age. The foundational framework of equalized odds and equality of opportunity was formalized by Moritz Hardt, Eric Price, and Nati Srebro in their landmark 2016 NeurIPS paper, establishing rigorous statistical criteria for non-discriminatory classifiers.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.