Also known as: AI Accountability Β· Artificial Intelligence Accountability
AI Accountability is the allocation and enforcement of responsibility for the development, deployment, operation, and effects of AI systems. It relies on governance practices such as transparency, assurance, documentation, evaluation, and mechanisms that make responsible parties answerable for risks or harms.
AI accountability is the process, heavily reliant on transparency and assurance practices, of holding entities answerable for the risks and/or harms of the AI systems they develop or deploy. This is closest to the definition adopted by the Trade and Technology Council (TTC) joint U.S.-EU set of AI terms, which defines accountability as an 'allocated responsibility' for system performance or for governance functions. Whereas OECD interpretive guidance distinguishes 'accountability' from 'responsibility' and 'liability,' the TTC definition embraces responsibility as part of accountability and includes a broader scope of governance activities. Accountability may require enforceable consequences. Such consequences, usually determined by regulators, courts, and the market, are accountability outputs. This Report focuses on developing and shaping 'accountability inputs,' which feed into systems of accountability.