Matteo Pistillo

Matteo Pistillo

This memorandum analyzes and stress-tests arguments in favor and against the inclusion of internal deployment within the scope of the European Union Artificial Intelligence Act (AI Act). In doing so, it aims to offer several possible interpretative pathways to the European Commission, AI providers and deployers, courts, and the legal and policy community at large based on Articles 2(1), 2(6), 2(8) of the AI Act. Specifically, this memorandum first analyzes interpretative pathways based on Article 2(1)(a)-(c) supporting the application of the AI Act to internally deployed AI models and systems. Then, it examines possible objections and exceptions based on Articles 2(6) and 2(8), with particular attention to the complexity of the scientific R&D exception under Article 2(6). Finally, it illustrates how Articles 2(1), 2(6), and 2(8) can be viewed as complementary to each other, once broken down to their most plausible meaning and interpreted in conjunction with Articles 3(1), 3(3), 3(4), 3(9), 3(10), 3(11), 3(12), 3(63), and Recitals 12, 13, 21, 25, 97, 109, and 110.

Matteo Pistillo, Charlotte Stix

High-impact and potentially dangerous capabilities can and should be broken down into early warning shots long before reaching red lines. Each of these early warning shots should correspond to a precursory capability. Each precursory capability sits on a spectrum indicating its proximity to a final high-impact capability, corresponding to a red line. To meaningfully detect and track capability progress, we propose a taxonomy of dangerous capability zones (a zoning taxonomy) tied to a staggered information exchange framework that enables relevant bodies to take action accordingly. In the Frontier AI Safety Commitments, signatories commit to sharing more detailed information with trusted actors, including an appointed body, as appropriate (Commitment VII). Building on our zoning taxonomy, this paper makes four recommendations for specifying information sharing as detailed in Commitment VII. (1) Precursory capabilities should be shared as soon as they become known through internal evaluations before deployment. (2) AI Safety Institutes (AISIs) should be the trusted actors appointed to receive and coordinate information on precursory components. (3) AISIs should establish adequate information protection infrastructure and guarantee increased information security as precursory capabilities move through the zones and towards red lines, including, if necessary, by classifying the information on precursory capabilities or marking it as controlled. (4) High-impact capability progress in one geographical region may translate to risk in other regions and necessitates more comprehensive risk assessment internationally. As such, AISIs should exchange information on precursory capabilities with other AISIs, relying on the existing frameworks on international classified exchanges and applying lessons learned from other regulated high-risk sectors.