Summary
Provides a structured approach to identifying and managing bias in AI systems, spanning data, human, and systemic sources. Addresses terminology, measurement, and evaluation practices to mitigate harmful outcomes.
Healthcare Implications
Directly applicable to clinical algorithm development and validation (e.g., subgroup performance, fairness metrics). Helps health systems design bias controls and documentation for oversight bodies and quality governance.