On 14 June 2023, the European Parliament (Parliament) plenary voted on its position on the Artificial Intelligence Act (AI Act), which was adopted by a large majority, with 499 votes in favor, 28 against, and 93 abstentions. The newly adopted text (Parliament position) will serve as the Parliament’s negotiating position during the forthcoming interinstitutional negotiations (trilogues) with the Council of the European Union (Council) and the European Commission (Commission).
The members of Parliament (MEPs) proposed several changes to the Commission’s proposal, published on 21 April 2021, including expanding the list of high-risk uses and prohibited AI practices. Specific transparency and safety provisions were also added on foundation models and generative AI systems. MEPs also introduced a definition of AI that is aligned with the definition provided by the Organisation for Economic Co-operation and Development. In addition, the text reinforces natural persons’ (or their groups’) right to file a complaint about AI systems and receive explanations of decisions based on high-risk AI systems that significantly impact their fundamental rights.
The Parliament position provides that AI, or an AI System, should refer to “a machine-based system that is designed to operate with varying levels of autonomy and that can, for explicit or implicit objectives, generate outputs such as predictions, recommendations, or decisions, that influence physical or virtual environments.” This amends the Commission’s proposal, where an AI System was solely limited to software acting for human-defined objectives and now encompasses the metaverses through the explicit inclusion of “virtual environments.”
Agreement on the final version of the definition of AI is expected to be found at the technical level during trilogue negotiations, as it does appear to be a noncontentious item.
Another notable inclusion relates to foundation models (Foundation Models) that were not yet in the public eye when the Commission’s proposal was published and were defined as a subset of AI System “trained on broad data at scale, is designed for generality of output, and can be adapted to a wide range of distinctive tasks.”