Learning Resources
Curated courses, tutorials, books, and videos to accelerate your AI journey.
Foundation Models: Opportunities, Risks and Mitigations
Other · Ada Lovelace Institute
The Ada Lovelace Institute's 2023 report on foundation models examines the regulatory gap between existing EU and UK frameworks and the capabilities and deployment patterns of large foundation models. It argues that the EU AI Act's risk-based approach was designed before foundation models became dominant and requires adaptation. The report identifies specific governance challenges — accountability gaps when foundation models are deployed through APIs by third parties, difficulty of pre-deployment risk assessment for general-purpose systems, and the challenge of monitoring foundation model behaviour across diverse downstream applications. AICI considers it the most practically useful analysis of the governance challenges specific to foundation models.
EU AI Act — Official Legislative Text (EUR-Lex)
Other · European Union
The full text of Regulation (EU) 2024/1689 — the Artificial Intelligence Act — as published in the Official Journal of the European Union. AICI recommends reading the regulation directly rather than through summaries. Summaries flatten complexity and often omit the definitions, recitals, and conditional clauses that determine whether a specific AI system is in scope and what obligations apply. Reading Article 3 (definitions) and Annex I (high-risk AI system list) carefully before relying on any compliance guidance is non-negotiable. The regulation entered force on 1 August 2024.
NIST AI Risk Management Framework Playbook
Other · NIST
The companion document to the NIST AI RMF, providing concrete suggested actions for implementing each of the framework's four functions (Govern, Map, Measure, Manage) across the AI lifecycle. Where the AI RMF is conceptual, the Playbook is operational — it gives practitioners specific things to do. It is freely available and regularly updated. AICI uses the Playbook as a reference framework in professional development programmes. Its value is not as a compliance checklist but as a structured approach to building internal AI governance capacity that can be adapted to different organisational contexts and regulatory obligations.