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| 공정성 인식 기계 학습× | 모델 보정× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2016 | 2017 |
| 창시자≠ | Moritz Hardt, Eric Price & Nati Srebro | Platt; Guo et al. |
| 유형≠ | Constrained supervised learning framework | Post-hoc probability correction technique |
| 원전≠ | Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29. link ↗ | Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330. link ↗ |
| 별칭 | Algorithmic Fairness, Fair Classification, Bias-Mitigating ML, Adil Makine Öğrenmesi | Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu |
| 관련≠ | 2 | 3 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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