La transparencia es el grado en que el propósito, uso de datos, funcionamiento, limitaciones y salidas de un sistema de IA pueden ser comprendidos o examinados por las partes interesadas relevantes. Apoya la rendición de cuentas, el uso informado, la auditoría y la gobernanza, pero no siempre exige revelar todos los parámetros internos.
‘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.