Linganisha mbinu

	Ujuzi wa Mashine Unaofahamu Haki	Urekebishaji wa Modeli
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2016	2017
Mwanzilishi	Moritz Hardt, Eric Price & Nati Srebro	Platt; Guo et al.
Aina	Constrained supervised learning framework	Post-hoc probability correction technique
Chanzo asilia	Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29.	Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330.
Majina mbadala	Algorithmic Fairness, Fair Classification, Bias-Mitigating ML, Adil Makine Öğrenmesi	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu
Zinazohusiana	2	3
Muhtasari	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.	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.