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Explainable AI for governance and public-sector decision support
# Overview A decision the state cannot explain is a decision the state cannot defend. Explainable AI is often pitched to the public sector as a nice-to-have — a layer of transparency bolted on to improve trust. This brief argues the opposite: in government, the ability to explain a decision is a precondition of the authority to make it at all. The state is not a recommender engine. When it allocates a benefit, flags a fraud risk, or sets a sentence, it exercises coercive power over people who did not opt in. Legitimacy there rests on an old bargain — reasons, a route to contest, and a human who can be held responsible. An unexplainable model breaks all three at once. ## What the full brief covers - **Why the public sector is different** — coercion, due process, and the slide from genuine decision *support* to *delegated authority*. - **The case files** — the Dutch childcare benefits scandal, COMPAS, and Robodebt, and why opacity (not complexity) caused the harm. - **The explanation toolkit** — interpretable-by-design vs. post-hoc methods (SHAP, LIME, counterfactuals), and the faithfulness problem. - **What an explanation is for** — different audiences, automation bias, the attribution gap, and contestability as the real test. - **The legal architecture** — GDPR, the SCHUFA ruling, and the EU AI Act's duties for high-risk public-sector systems. - **Governance scaffolding** — algorithmic impact assessments, transparency registers, audit, and public engagement. - **The emerging-economy frontier** — leapfrogging without re-importing the failures, with relevance to public-interest systems like road safety. - **A builder's & policymaker's playbook** — eight commitments for decision support that survives scrutiny. ## Read the full research The complete brief — including all case studies, the legal analysis, and 28 cited sources — is available as a standalone document. **→ [Read the full research brief](INSERT_LINK_HERE)** --- *Prepared by the ECADEL LABS Research Team. Legal provisions are summarised for orientation, not as legal advice.*
Research NoteThe Mirror Has a Margin: AI Bias and Fairness in African Training Data Contexts
Bias in African AI is less about broken algorithms than absence: African faces, languages, bodies, and genomes are barely present in the data that trains modern systems — so models return a smaller, paler world and call it neutral. This brief maps where African realities fall outside the frame, the mechanisms behind it, and documented harms across vision, language, and health — then sets out the data-sovereignty stance for building fairly from within the continent.