fairlearn and AI_fairness
Fairlearn is an established, production-ready auditing and mitigation framework, while AI_Alameer appears to be an educational resource collection, making them complements where the latter could reference or build upon patterns from the former for learning purposes.
About fairlearn
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
Provides dual assessment and mitigation tools: metrics for identifying which demographic groups experience allocation or quality-of-service harms, and algorithms for reducing unfairness across multiple fairness definitions. Implements group fairness constraints that enforce comparable model behavior across specified demographic groups, enabling data scientists to quantify fairness trade-offs against accuracy. Integrates with standard ML workflows through scikit-learn-compatible APIs and includes Jupyter notebooks demonstrating real-world applications in hiring, lending, and admissions scenarios.
About AI_fairness
Ali-Alameer/AI_fairness
This GitHub repository offers resources to create fair and unbiased AI systems, including libraries, tools and tutorials on identifying and mitigating bias in machine learning models and implementing fairness in AI.
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