Controltransparency

Controltransparency describes the degree to which a system's control decisions and actions are visible and understandable to people. It involves exposing control policies, state information, inputs, and the rationale behind actions, with the ability to audit and reproduce decisions.

Key components include observability of state and actions, explainability of the control rationale, and audit trails. Implementations use interpretable policy designs, logs, and dashboards that summarize decisions, sensor data, and outcomes.

Applications span industrial automation, autonomous vehicles, robotics, and smart grids. It complements Explainable AI by focusing on the control loop and decision policy rather than only model outputs.

Benefits include improved safety, accountability, and fault diagnosis, along with regulatory compliance. Trade-offs may involve reduced performance, latency, data volume, or exposure of sensitive information; design must balance transparency with security and privacy.

Measurement approaches include action traceability metrics, explanation fidelity, and time-aligned logs. Methods range from inherently interpretable control architectures to post-hoc explanations and operator dashboards.

Critics argue transparency is context-dependent and should be layered and proportionate to risk. Controlled disclosure, user-centric interfaces, and governance frameworks are typically recommended.