> ML_LITERATURE // HARDT-2016-EQUALITY-OPPORTUNITY-IN-SUPERVISED-LEARNING_v1.0
Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, Nathan Srebro · Advances in Neural Information Processing Systems (NeurIPS) (2016)
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Principal Contribution
Formulated Equalized Odds and Equality of Opportunity, requiring equal true positive rates across protected demographic subgroups.
Operational Relevance
Directly guides deployment choices and architecture selection for task-fairness-audit, task-binary-classification.
Assumptions
- Standard empirical regularity and statistical stability hold across evaluation domains
Limitations
- Performance characteristics depend on domain distribution and compute allocation parameters
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: