AI Trust Glossary · Canonical Definition
Algorithmic Accountability
The principle that organizations deploying AI must be answerable for algorithmic decisions and their consequences - including clear attribution of responsibility and mechanisms for redress.
Explanation
Algorithmic accountability moves beyond transparency (knowing how a decision was made) to responsibility (being answerable for it). This includes identifying who owns the decision, what data informed it, and how affected parties can challenge or appeal it.
Why it matters
As AI agents make decisions affecting hiring, lending, healthcare, and criminal justice, the question of who is responsible is not merely ethical - it is increasingly a legal requirement under the EU AI Act and similar frameworks.
How Borealis uses it
The decision transparency dimension of the BM Score directly measures algorithmic accountability. Immutable Hedera-anchored audit trails mean certification records cannot be altered retroactively, creating a permanent accountability infrastructure.