手法を比較

	公平性を考慮した機械学習	ロジスティック回帰	モデルキャリブレーション
分野	機械学習	研究統計	機械学習
系統	Machine learning	Process / pipeline	Machine learning
提唱年	2016	1958	2017
提唱者	Moritz Hardt, Eric Price & Nati Srebro	David Roxbee Cox	Platt; Guo et al.
種類	Constrained supervised learning framework	Method	Post-hoc probability correction technique
原典	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.	Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330.
別名	Algorithmic Fairness, Fair Classification, Bias-Mitigating ML, Adil Makine Öğrenmesi	logit model, binomial logistic regression, LR	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu
関連	2	3	3
概要	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.	Model calibration is a post-hoc technique that adjusts the probability outputs of a trained classifier so that predicted confidence scores match empirical outcome frequencies. A classifier is said to be perfectly calibrated if, among all predictions made with confidence p, exactly a fraction p of them are correct. Systematic miscalibration of modern deep neural networks was rigorously documented by Guo et al. (2017), who showed that networks trained with standard cross-entropy loss tend to be overconfident, and proposed temperature scaling as a simple, effective remedy.