Quantitative study about the estimated impact of the AI Act

TL;DR

This paper presents a systematic, interdisciplinary method to quantify how the AI Act would impact AI systems, using a German, self-described dataset from Plattform lernende Systeme. Applying the initial April 2021 draft, the authors preprocess and classify $514$ usable entries into risk categories, finding no prohibited systems, $160$ high-risk, $39$ with transparency obligations, and $315$ low-risk; overall, about $38.7\%$ would fall under regulation, with the remainder being low-risk. The study highlights ambiguities in definitions such as safety components and interaction, and demonstrates that expert collaboration between computer scientists and legal scholars is crucial for robust categorization. The authors advocate transferring the method to other datasets and updating it as the AI Act evolves, emphasizing its value for forecasting regulatory impact and guiding policy refinements before regulation takes effect.

Abstract

With the Proposal for a Regulation laying down harmonised rules on Artificial Intelligence (AI Act) the European Union provides the first regulatory document that applies to the entire complex of AI systems. While some fear that the regulation leaves too much room for interpretation and thus bring little benefit to society, others expect that the regulation is too restrictive and, thus, blocks progress and innovation, as well as hinders the economic success of companies within the EU. Without a systematic approach, it is difficult to assess how it will actually impact the AI landscape. In this paper, we suggest a systematic approach that we applied on the initial draft of the AI Act that has been released in April 2021. We went through several iterations of compiling the list of AI products and projects in and from Germany, which the Lernende Systeme platform lists, and then classified them according to the AI Act together with experts from the fields of computer science and law. Our study shows a need for more concrete formulation, since for some provisions it is often unclear whether they are applicable in a specific case or not. Apart from that, it turns out that only about 30\% of the AI systems considered would be regulated by the AI Act, the rest would be classified as low-risk. However, as the database is not representative, the results only provide a first assessment. The process presented can be applied to any collections, and also repeated when regulations are about to change. This allows fears of over- or under-regulation to be investigated before the regulations comes into effect.