AI Act Explainability: Building Infrastructure for Transparent AI Decisions

Article 13 requires high-risk AI systems to be "sufficiently transparent to enable deployers to interpret the system's output and use it appropriately." For infrastructure teams, this means building explainability as a service — not a one-off analysis, but real-time explanations served alongside every prediction.

Explainability Methods and Infrastructure

Model-Agnostic Methods

• SHAP (SHapley Additive exPlanations) — Feature importance for individual predictions. Compute-intensive; requires infrastructure for batch computation and caching
• LIME (Local Interpretable Model-agnostic Explanations) — Local linear approximations. Faster than SHAP but less consistent
• Counterfactual explanations — "Your application would have been approved if income was €5,000 higher." Legally powerful for GDPR Article 22 compliance
• Feature importance — Global and local feature ranking. Simplest to compute and serve

Model-Specific Methods

• Attention visualization — For transformer models, which input tokens influenced the output
• Decision tree extraction — Approximate complex models with interpretable decision trees
• Concept-based explanations — Higher-level explanations using human-understandable concepts

Explainability Infrastructure Architecture

Explanation Generation Service

A dedicated service that runs alongside model serving: receives inference requests, computes explanations using configured method, caches explanations (SHAP values are expensive to compute), serves explanations via API for downstream consumers.

Explanation Storage

Explanations must be stored for regulatory purposes: link each explanation to its prediction, input data, and model version. Retain for the required period (varies by sector). Make explanations queryable for audit and analysis.

User-Facing Explanation Interface

For end users (deployers, affected persons): translate technical explanations (SHAP values) into human-readable text. Provide different detail levels for different audiences. Support multiple languages for EU deployment.

The Explanation Quality Problem

Not all explanations are faithful to the model's actual decision process. Build infrastructure to validate explanation quality: consistency checks, perturbation testing, and human evaluation of explanation helpfulness. A bad explanation is worse than no explanation.