AI Accountability
The obligation of AI system developers and operators to ensure their systems are designed and used responsibly, adhering to ethical standards and legal requirements.
Definition
A formal assignment of responsibility for every stage of an AI system—from data collection through deployment and decommissioning. Accountability means naming the roles (e.g., model owner, data steward, compliance officer), defining clear escalation paths for failures, and embedding review processes so that whenever an AI decision causes harm or legal breach, there is a documented trail showing who investigated, who remediated, and how the organization prevented recurrence.
Real-World Example
A major bank’s automated credit-scoring AI wrongly labels a customer as high-risk. The AI governance team traces the error to an under-representative dataset, convenes a “root-cause” task force, retrains the model with balanced data, issues an apology to the customer, and updates policy so any similar discrepancy triggers immediate human review—demonstrating clear responsibility and corrective action.
.svg)
