Understanding, detecting, and mitigating bias to ensure fair AI outcomes
Essential resources in this thematic area
Comprehensive systematic review synthesizing research on algorithmic bias with integrated framework and agenda for future research directions.
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International standards
Peer-reviewed research
WEF, OECD analyses
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Highlights importance of systematically identifying bias and engaging mitigation activities throughout AI model lifecycle with practical strategies.
Proposes comprehensive approaches for understanding and mitigating AI bias in healthcare to advance health equity and reduce disparities.
An independent nonprofit connecting experts across sectors to promote research and policy for safe and ethical AI systems.
A community-driven initiative promoting fairness, accountability, and transparency in machine learning systems through research and advocacy.
Comprehensive systematic review synthesizing research on algorithmic bias with integrated framework and agenda for future research directions.
A Berlin-based nonprofit evaluating algorithmic decision-making processes and maintaining a global inventory of AI ethics guidelines.
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