AI bias regulation is evolving, with the US FTC and EEOC using existing antidiscrimination laws, while the EU advances its AI Act with risk-based oversight, transparency, and impact assessments. Bias stems from systemic, computational, and human factors, and fairness remains hard to define across jurisdictions. UNESCO and OECD principles stress inclusivity, explainability, and accountability. Mitigation tools include audits (internal/external), explainable AI, ethical checklists, and matrices. Organizations should embed bias detection and fairness practices throughout the AI lifecycle to prepare for stricter global standards.

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