Labs

AI Bias Study

Identifying and mitigating bias in artificial intelligence through research, datasets, and public collaboration.

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About the Bias Study

Our AI Bias Study examines algorithmic fairness across 60+ datasets to ensure ethical AI development. We focus on detecting, understanding, and eliminating bias in machine learning systems across domains.

Key Goals

  • • Measure bias across multiple AI domains
  • • Develop mitigation techniques

Public Impact

  • • Open-source bias mitigation tools
  • • Community datasets for transparency

Current Research

Algorithm Analysis

Auditing 500+ AI models for racial, gender, and cultural bias patterns.

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Mitigation Frameworks

Creating fairness-aware training tools for AI developers.

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Public Datasets

Publishing benchmark bias-aware datasets for ethical AI training.

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Key Research Challenges

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Measurement Complexity

Developing metrics that capture all types of bias across cultural and demographic dimensions.

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Algorithm Fairness

Balancing performance and fairness in critical domain like credit scoring and hiring.

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Transparency Challenges

Creating explainable AI to show how bias mitigation decisions are made.

Address AI Bias Together

Join our global community of researchers, developers, and advocates working to build fair AI systems.

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