AI Ethics & Governance evaluates how a company oversees the design, deployment and lifecycle management of artificial-intelligence systems to ensure they are lawful, trustworthy and aligned with human rights and societal values. It covers:

• principles & policies – documented commitments (e.g., fairness, transparency, accountability, privacy, safety, sustainability) that frame all AI activities and are endorsed by the board or a designated ethics committee;
• risk-based governance structures – roles, responsibilities and review processes (AI ethics councils, model-risk committees, red-team testing) that assess use-case criticality and approve or halt models accordingly;
• responsible-AI controls – bias detection and mitigation, explainability, data-quality governance, human-in-the-loop safeguards, adversarial robustness, continuous monitoring and incident-response playbooks;
• regulatory alignment – compliance with emerging frameworks such as the EU AI Act, U.S. NIST AI RMF, OECD AI Principles and sector-specific guidelines, including mandatory conformity assessments for high-risk systems;
• stakeholder engagement & transparency – publication of model cards or system datasheets, clear user disclosures, grievance mechanisms and periodic impact assessments that involve affected groups and civil society;
• training & culture – organisation-wide capacity-building and incentives that embed ethical AI practices into product development, procurement and vendor management.