Explainability

Explainability refers to the degree to which humans can comprehend and articulate the reasoning behind an AI system's outputs, predictions, or decisions. This encompasses both technical interpretability (understanding the model's internal mechanisms) and practical transparency (communicating decisions to affected individuals in accessible terms).

The EU AI Act establishes explainability as a cornerstone of trustworthy AI through Article 13's transparency requirements. High-risk AI systems must be designed to enable users to interpret outputs and understand system behavior appropriately. This is not merely a technical checkbox but a fundamental enabler of human oversight, as operators cannot meaningfully supervise decisions they do not understand. For systems making consequential decisions about individuals, such as credit assessments or employment screening, explainability becomes both a regulatory requirement and an ethical imperative. The regulation recognizes that "sufficient transparency" does not demand complete interpretability of every algorithmic weight. Instead, it requires that explanations be proportionate to the risk level and comprehensible to the intended audience, whether that is a compliance officer reviewing system behavior or an affected individual seeking to understand a decision about them.

Organizations deploying high-risk AI systems must implement explainability at multiple levels. At the model level, this may involve techniques such as SHAP values, LIME, or attention visualization to understand feature importance and decision pathways. At the decision level, systems should generate human-readable explanations for individual outputs that can be reviewed during human oversight workflows or provided to affected parties upon request.

The challenge lies in balancing explainability with model performance and operational efficiency. Organizations should document their explainability approach as part of Annex IV technical documentation, specifying what explanation methods are used, what information is captured per decision, and how explanations are made accessible to relevant stakeholders.