What Is Algorithmic Accountability?
Algorithmic Accountability is the principle that the organisations deploying automated systems remain answerable for those systems' decisions and impacts.
Algorithmic Accountability — the principle that the organisations deploying automated systems remain answerable for those systems' decisions and impacts.
Algorithmic accountability holds that responsibility cannot be outsourced to a model: a human organisation must be able to explain, justify, and remedy the outcomes its automated systems produce. In practice it is operationalised through documentation, impact assessments, audit trails, clear internal ownership, and avenues for affected people to contest decisions. It is the connective tissue between abstract AI principles and the concrete governance controls — record-keeping, oversight, redress — that regulators expect to see.
Source: OECD AI Principles; NIST AI RMF (GOVERN function)
Plain-language explanation
Algorithmic accountability holds that responsibility cannot be outsourced to a model: a human organisation must be able to explain, justify, and remedy the outcomes its automated systems produce. In practice it is operationalised through documentation, impact assessments, audit trails, clear internal ownership, and avenues for affected people to contest decisions. It is the connective tissue between abstract AI principles and the concrete governance controls — record-keeping, oversight, redress — that regulators expect to see.
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