Skip to content
←Back to glossaryAI

Algorithmic accountability

Its 8 cross-references, woven across the glossary's 18 categories

Principle requiring designers and users of algorithms to justify and answer for the automated decisions made.

Definition

Algorithmic accountability is the principle that designers and users of algorithmic systems must be able to justify the automated decisions they produce and answer for them, before the people concerned as well as the authorities. It encompasses the traceability of decisions, the ability to explain a given decision, the auditability of the system and the identification of an identifiable responsible party, as opposed to a dilution of responsibility in technical opacity. This principle responds to the risk that automation serves to evade responsibility, the algorithm being invoked as a neutral black box to escape attribution. It underpins several concrete obligations of the EU AI Act and the General Data Protection Regulation, such as documentation, logging and the right to explanation. For insurance, algorithmic accountability conditions the ability to establish liability in case of damage and is a risk-governance marker: an insured with audit and traceability processes presents a better-controlled and more defensible profile in litigation.

Example

Textbook case: a company able to trace and explain a contested automated credit decision is better placed to defend its liability than one invoking the opacity of its algorithm.

Related terms
Related articles
Stay informed

Follow the research

One letter a week. First, the week's underwriting note, when one is published: a dated, argued decision, signed by Paul Bernardski. Then what the observatory published on cyber risk, political risk and insurance. No account, no tracking.

No spam, one-click unsubscribe, no data ever sold.