Introduction to AI Regulation
The Chair AI Regulation is pleased to share information about a new paper titled ‘Fitting “Systemic Risks” into a Taxonomy in the GPAI Code of Practice: Will the Resulting Ambiguity be Exploited by GPAI Model Providers?’ by its research fellow, Dr. Theodoros Karathanasis.
Abstract
The paper delves into the definition and implications of the concept “systemic risk” under Article 3(65) of the EU AI Act, exploring the ambiguities arising from the use of unclear words and redundant terms such as “high-impact” and “significant impact.” These challenges include the potential for confusion regarding the source of risk and the extent of its impact on the EU’s internal market.
Brief Overview
The working document explores the critical aspect of defining “systemic risk” within the EU AI Act, emphasizing the inherent difficulties arising from the Act’s phrasing. Article 3(65) provides a definition that Dr. Karathanasis describes as ambiguous, due to the inclusion of potentially unclear and repetitive language such as “high-impact” and “significant impact.” This fundamental lack of precision is a key concern, as it could lead to differing understandings of what qualifies as a systemic risk, thereby creating obstacles for consistent implementation and enforcement of the regulations.
Analysis of the GPAI CoP Taxonomy
Building upon this foundational ambiguity, the document analyzes the EU AI Act’s strategy of employing the General-Purpose AI Code of Practice (GPAI CoP) to create a structured categorization of these systemic risks. This taxonomy is intended to offer a more tangible framework for comprehending and managing the potential harms linked to general-purpose AI models. The document indicates that this taxonomy classifies potential threats, providing examples such as cyber offenses, discrimination, and loss of control.
Methodological Approach
The working document details the methodological approach employed to evaluate this GPAI CoP taxonomy, which involves assessing it against five specific factors: market impact, societal impact, dual impacts, propagation, and context-specificity. Market impact relates to the potential for widespread disruption and instability within the economic landscape resulting from the deployment or misuse of AI models. Societal impact examines the broader effects on communities, social structures, fundamental rights, and individual well-being.
Concerns and Implications
The central concern raised by the document is whether the initial lack of precision in defining systemic risk within the EU AI Act will be exacerbated or clarified by its integration into the GPAI CoP’s taxonomy. The author appears concerned that instead of resolving the ambiguity, the taxonomy might generate opportunities for GPAI model providers to exploit these definitional weaknesses.
Conclusion
In conclusion, the paper highlights the importance of clarifying the definition of systemic risk within the EU AI Act and its integration into the GPAI CoP taxonomy. The potential for ambiguity and exploitation by GPAI model providers underscores the need for precise language and alignment with existing legislative and practical risk management procedures.
FAQs
- Q: What is the main topic of the paper?
A: The paper discusses the concept of “systemic risk” in the EU AI Act and its integration into the GPAI Code of Practice taxonomy. - Q: What are the challenges in defining systemic risk?
A: The challenges include the use of unclear words and redundant terms, leading to potential confusion regarding the source and extent of risk. - Q: What is the purpose of the GPAI CoP taxonomy?
A: The taxonomy aims to provide a framework for comprehending and managing potential harms linked to general-purpose AI models. - Q: What factors are used to evaluate the GPAI CoP taxonomy?
A: The factors include market impact, societal impact, dual impacts, propagation, and context-specificity. - Q: What is the central concern of the document?
A: The central concern is whether the ambiguity in defining systemic risk will be exploited by GPAI model providers.
