AI Governance FAQ
What is algorithmic accountability?
Quick answer
Algorithmic accountability means having clear responsibility for how automated systems are used and a way to investigate and challenge consequential outcomes. It includes records, explanations suited to the context and effective review. It does not mean one named employee automatically carries all legal liability for an AI decision.
Why algorithmic accountability matters
When a decision is challenged, the organisation should be able to establish what information was used, what part AI played and who had authority to decide. Clear internal ownership helps that investigation; it does not determine civil or criminal liability by itself. Liability depends on the applicable law, contractual responsibilities and facts. Give affected people the explanation and routes to challenge required in the particular context, and ensure a reviewer can correct a mistake.
How the EU AI Act implements algorithmic accountability
The AI Act supports accountability through role-specific documentation, transparency, oversight and record requirements. GDPR and sectoral law may impose additional duties. Provider conformity assessment is not a general certificate of an organisation's accountability, and it is not required of every ordinary deployer. Identify the applicable requirements and dates, then keep evidence that the organisation's controls actually operate.
Legal references reviewed 4 October 2026. High-risk provisions have phased application and transition rules. Sources: Commission timetable, deployer duties, impact-assessment scope and literacy guidance.
Acuity AI helps Irish organisations build the governance structures that make algorithmic accountability real. How we build accountability structures.