Legal accountability

Mitigation strategy

1. Establish a comprehensive AI Governance Framework that explicitly defines stakeholder roles and responsibilities across the entire AI lifecycle. This includes utilizing clear accountability matrices, such as RACI, to specify ownership for model development, deployment, continuous monitoring, and the remediation of non-compliance issues. 2. Institute mandatory and continuous technical documentation standards (e.g., Model Cards, Datasheets for Datasets) for every AI system. This documentation must ensure full traceability, recording design rationale, data provenance, validation procedures, risk assessments, and change logs to provide auditable evidence of responsible process adherence. 3. Implement regular, independent audit and compliance mechanisms to continuously verify adherence to the established governance framework and documentation requirements. This includes scheduled system reviews and the maintenance of detailed decision records to hold accountable parties responsible for system outcomes and mitigate legal exposure.