Being responsible for what an AI system does and being prepared to answer for it — you can't blame the algorithm and walk away.
Being responsible for what an AI system does and being prepared to answer for it — you can't blame the algorithm and walk away.
Accountability in AI is a multi-party obligation: providers are accountable for design and conformity; deployers are accountable for operation and oversight; governance boards are accountable for risk appetite and oversight adequacy. The EU AI Act distributes accountability across the supply chain through its provider, deployer, importer, and distributor obligations. The OECD AI Principles and ISO/IEC 42001 treat accountability as a core governance principle. Accountability requires traceability (to know who made decisions), transparency (to disclose information), and auditability (to allow verification).
Defining clear accountability assignments in AI governance frameworks prevents diffusion of responsibility — the single most common governance failure that leads to undetected AI harms and regulatory exposure.
Like the clear chain of authority in a regulated financial institution — every decision has an identified accountable person whose name is on record, enabling regulators to ask 'who authorised this?' and receive a clear answer.
Being responsible for what an AI system does and being prepared to answer for it — you can't blame the algorithm and walk away.
Accountability in AI is a multi-party obligation: providers are accountable for design and conformity; deployers are accountable for operation and oversight; governance boards are accountable for risk appetite and oversight adequacy. The EU AI Act distributes accountability across the supply chain through its provider, deployer, importer, and distributor obligations. The OECD AI Principles and ISO/IEC 42001 treat accountability as a core governance principle. Accountability requires traceability (to know who made decisions), transparency (to disclose information), and auditability (to allow verification).
Defining clear accountability assignments in AI governance frameworks prevents diffusion of responsibility — the single most common governance failure that leads to undetected AI harms and regulatory exposure.