ArtBulb uses style-description-guided multimodal clustering combined with MLLMs to judge whether AI-generated artworks have a unique, consistent, prompt-accurate style eligible for copyright protection.
Taxonomy to Regulation: A (Geo)Political Taxonomy for AI Risks and Regulatory Measures in the EU AI Act
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Technological innovations have shown remarkable capabilities to benefit and harm society alike. AI constitutes a democratized sophisticated technology accessible to large parts of society, including malicious actors. This work proposes a taxonomy focusing on on (geo)political risks associated with AI. It identifies 12 risks in total divided into four categories: (1) Geopolitical Pressures, (2) Malicious Usage, (3) Environmental, Social, and Ethical Risks, and (4) Privacy and Trust Violations. Incorporating a regulatory side, this paper conducts a policy assessment of the EU AI Act. Adopted in March 2023, the landmark regulation has the potential to have a positive top-down impact concerning AI risk reduction but needs regulatory adjustments to mitigate risks more comprehensively. Regulatory exceptions for open-source models, excessively high parameters for the classification of GPAI models as a systemic risk, and the exclusion of systems designed exclusively for military purposes from the regulation's obligations leave room for future action.
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From Imitation to Innovation: The Emergence of AI Unique Artistic Styles and the Challenge of Copyright Protection
ArtBulb uses style-description-guided multimodal clustering combined with MLLMs to judge whether AI-generated artworks have a unique, consistent, prompt-accurate style eligible for copyright protection.