AiAct in Practice - Merging AI Innovation with EU Legal Frameworks
Introduction
Delve into the AiAct, the European Union's pivotal regulation shaping the trajectory of AI. This workshop is designed to foster collaboration between Natural Science PhDs and LLM students, bridging the gap between AI's technological advancements and the EU's regulatory landscape.Phase 1 - Team Formation and Introduction
Form dynamic teams, each comprising Natural Science PhDs and LLM students. Natural Science PhDs present (using a few slides) AI use-cases based on their research, with the additional
twist that a possible commercialization or an open release should be targeted.
LLM students will provide an initial legal analysis, highlighting potential regulatory
challenges.
Collaboration over the Semester Break
Teams will collaboratively assess the AI use-cases, focusing on potential legal liabilities under the AiAct. Explore solutions, considering certification processes, project and training data modifications, and other compliance strategies.
Engage in regular consultations, fostering a deeper understanding of both AI's capabilities and the intricacies of the AiAct.
Phase 2 - Presentation and Review
Conclude the workshop with a comprehensive presentation, showcasing the AI use-case and its legal evaluation. Receive feedback from peers and faculty, enriching the learning experience and refining the project's direction.
Reflect on the collaborative journey, understanding the importance of interdisciplinary approaches in the evolving world of AI and EU regulations.
Objective
By the end of this workshop, students will have a hands-on understanding of the AiAct's implications, ensuring that AI innovations align seamlessly with the EU's values, safety standards, and legal requirements.Prerequisites
Natural Science PhDs that would like to participate must hand in a suggestion for the topic they would like to bring via email to tariq.baig@ds.mpg.de.By the very nature of the workshop topics should be related to artificial intelligence and modern machine learning, half a page to a page of project description is sufficient.
Projects that are rooted in actual ongoing research are greatly preferred, please make a connection to your research clear in the description.
Preliminary Schedule
December 10th: Deadline for handing in project descriptions February 6th 2024: Session 1
Early summer semester 2024: Session 2
Notes: Unfortunately we only can accept a limited number of participants.