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Accountability in building systems—particularly

Accountability in building systems—particularly those driven by artificial intelligence and machine learning go beyond mere error-checking. They serve as a governance mechanism that guarantees every automated decision can be traced back to a responsible individual, supported by evidence, and rectified if it results in harm.

Systems are required to offer clear insights into how they generate specific outputs. This frequently entails the use of Explainable AI (XAI) methods to clarify 'black-box' models, enabling users and regulators to understand the rationale behind a decision.

We ought to keep comprehensive logs of the entire lifecycle, which includes data sources, training parameters, and model versions. Instruments like Model Cards or Data Cards assist in standardizing this documentation.

In high-stakes systems, final authority should not rest solely with the technology. Humans must retain significant oversight, possessing the ability to review, override, or contest automated decisions in critical fields such as healthcare or finance.