Explainability in AI
Explainability aims to make an AI system’s decisions understandable. It lies at the heart of Responsible AI and is a central requirement of the AI Act.
Why Explainability Is Essential
An AI model must allow us to understand how and why a decision was made. Without this, human oversight, regulatory compliance and user trust become impossible.
Explainability is particularly critical for high‑risk systems defined by the AI Act.
Two Forms of Explainability
1. Intrinsic Explainability
The model is understandable by design: decision trees, rules, linear models.
$$ y = w_0 + w_1 x_1 + w_2 x_2 + \dots + w_n x_n $$
Here, each coefficient $w_i$ has a direct interpretation.
2. Post‑hoc Explainability
The model is not interpretable by itself (neural networks, complex models), but explanations are generated afterwards: local importance, perturbation‑based methods, derived rules.
Limits of Classical Approaches
Unstable Explanations
Two explanations generated on similar data may differ significantly, raising reproducibility concerns.
Non‑auditable Explanations
Many post‑hoc methods lack strong mathematical justification, limiting their use in regulated environments.
Insufficient Narratives
An explanation must be understandable by a human, not only by a technical expert.
Building a Responsible AI Culture
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