AI Explainability

AI explainability is the degree to which an AI system's outputs can be understood by the people responsible for them and by those affected by them. In practice, this means being able to account for why a system produced a given output, which inputs influenced the result, and how confident the system was in its decision. 

Explainability is not just a technical property: it is a governance requirement. Organizations deploying AI in high-stakes contexts (such as hiring, credit, medical diagnosis, public services) need to explain those decisions to regulators, affected individuals, and their own boards. At Eticas.ai, explainability is assessed as part of every evaluation, with particular attention to whether the explanations provided are meaningful to the people who rely on them, not just technically accurate.