Transparenz ist der Grad, in dem Zweck, Datennutzung, Funktionsweise, Grenzen und Ausgaben eines KI-Systems von relevanten Stakeholdern verstanden oder geprüft werden können. Sie unterstützt Verantwortlichkeit, informierte Nutzung, Audits und Governance, erfordert aber nicht immer die vollständige Offenlegung jedes internen Parameters.
‘property of a system that appropriate information about the system is made available to relevant stakeholders’ ISO/IEC 22989.
Making the operation and decision-making processes of AI systems clear and understandable to users and stakeholders. Key components of transparency are: Openness: Clearly communicating the purpose and capabilities of an AI system. This includes explaining what the system is designed to do and any limitations it may have. Explainability: Providing understandable explanations of how the AI system reaches it decisions. Accountability: Ensuring that there's a mechanism for tracking and verifying decisions made by the AI. This can include maintaining logs, version control and audit trails. Data Transparency: Disclosing what data is used to train and operate the AI system, including its sources and how its processed.