Explainability (XAI, or Explainable Artificial Intelligence) is a mandatory technical requirement under the EU AI Act that obliges providers of high-risk AI systems to design their systems in a way that enables human deployers to understand the rationale behind individual outputs, decisions, or recommendations.

This requirement addresses the critical gap between a system's internal complexity and the human need for comprehensible reasons. Explainability goes beyond mere transparency (which provides general information about the system) by focusing on the interpretability of specific outcomes for a specific case. It is the technical capability that makes meaningful human oversight and the "right to explanation" practically possible. For a high-risk system, such as one denying a loan or prioritizing medical care, providing a post-hoc justification like "the algorithm decided" is legally and ethically insufficient. The system must furnish reasons that are accessible to the deployer, who bears the responsibility for the final decision and must be able to explain it to affected individuals.

Implementing explainability involves deploying a range of technical and design strategies:

Interpretable Models: Using inherently understandable models (like decision trees or linear regression) where possible for critical decision components, trading off some complexity for clarity.

Post-Hoc Explanation Techniques: Applying methods like LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) to approximate and highlight which input features most influenced a complex model's specific output.

Counterfactual Explanations: Providing actionable, user-centric explanations by indicating the minimal changes to the input that would have led to a different, more favorable outcome (e.g., "Your loan would have been approved if your income had been $5,000 higher.").

Confidence Scoring and Uncertainty Quantification: Communicating the system's confidence level in its output or highlighting areas where its prediction is uncertain, signaling to the human operator where scrutiny is most needed.

Regulatory Context: The EU AI Act explicitly mandates explainability for high-risk AI systems in Article 13, requiring that they are "accompanied by instructions for use that include concise, comprehensible information for the deployer." This includes "the characteristics, capabilities and limitations of performance of the high-risk AI system" to enable proper interpretation of the output. This operationalizes the GDPR’s concept of a "right to explanation."

Bridge to Human Agency: Explainability is not an end in itself but a vital tool for human judgment. It empowers deployers to validate decisions, spot potential errors or biases, and maintain meaningful control. Without it, human oversight is reduced to a rubber-stamping exercise, undermining the core risk-mitigation framework of the Act.

‍