Cyber & IT Supervisory Forum - Additional Resources

Measure 2.8 Risks associated with transparency and accountability – as identified in the MAP function – are examined and documented. About Transparency enables meaningful visibility into entire AI pipelines, workflows, processes or organizations and decreases information asymmetry between AI developers and operators and other AI Actors and impacted communities. Transparency is a central element of effective AI risk management that enables insight into how an AI system is working, and the ability to address risks if and when they emerge. The ability for system users, individuals, or impacted communities to seek redress for incorrect or problematic AI system outcomes is one control for transparency and accountability. Higher level recourse processes are typically enabled by lower-level implementation efforts directed at explainability and interpretability functionality. See Measure 2.9. Transparency and accountability across organizations and processes is crucial to reducing AI risks. Accountable leadership – whether individuals or groups – and transparent roles, responsibilities, and lines of communication foster and incentivize quality assurance and risk management activities within organizations. Lack of transparency complicates measurement of trustworthiness and whether AI systems or organizations are subject to effects of various individual and group biases and design blind spots and could lead to diminished user, organizational and community trust, and decreased overall system value. Enstating accountable and transparent organizational structures along with documenting system risks can enable system improvement and risk management efforts, allowing AI actors along the lifecycle to identify errors, suggest improvements, and figure out new ways to contextualize and generalize AI system features and outcomes. Suggested Actions Instrument the system for measurement and tracking, e.g., by maintaining histories, audit logs and other information that can be used by AI actors to review and evaluate possible sources of error, bias, or vulnerability.

134