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Article aims to fill existing gaps in the theoretical and methodological elaboration of the Fundamental Rights Impact Assessment (FRIA) This article outlines the main building blocks of a model template for the FRIA. It can serve as a blueprint for other national and international regulatory initiatives to ensure that AI is fully consistent with human rights.
arXiv Detail & Related papers (2024-11-07T11:55:55Z)
Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
arXiv Detail & Related papers (2024-10-10T17:38:38Z)
Trustworthy AIGC Copyright Management with Full Lifecycle Recording and Multi-party Supervision in Blockchain [10.015046490760199]
The current legal system for copyright is built around human creators, yet in the realm of AIGC, the role of humans in content creation has diminished. It is necessary to meticulously record contributions of all entities involved in the generation of AIGC to achieve a fair distribution of copyright. This study thoroughly records the intermediate data generated throughout the full lifecycle of AIGC and deposits them into a decentralized blockchain system for secure multi-party supervision.
arXiv Detail & Related papers (2024-06-21T08:22:39Z)
AI Royalties -- an IP Framework to Compensate Artists & IP Holders for AI-Generated Content [3.4410934027154996]
This article investigates how AI-generated content can disrupt central revenue streams of the creative industries. It reviews the IP and copyright questions related to the input and output of generative AI systems.
arXiv Detail & Related papers (2024-04-05T15:35:08Z)
Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI [2.669847575321326]
The survey aims to stay abreast of the latest developments and open problems. It will first outline methods of detecting copyright infringement in mediums such as text, image, and video. Next, it will delve an exploration of existing techniques aimed at safeguarding copyrighted works from generative models.
arXiv Detail & Related papers (2024-03-31T22:10:01Z)
Generative AI and Copyright: A Dynamic Perspective [0.0]
generative AI is poised to disrupt the creative industry. The compensation to creators whose content has been used to train generative AI models (the fair use standard) and the eligibility of AI-generated content for copyright protection (AI-copyrightability) are key issues. This paper aims to better understand the economic implications of these two regulatory issues and their interactions.
arXiv Detail & Related papers (2024-02-27T07:12:48Z)
Copyright Protection in Generative AI: A Technical Perspective [58.84343394349887]
Generative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity and authenticity of contents generated by these Deep Generative Models (DGMs) have sparked significant copyright concerns. This work delves into this issue by providing a comprehensive overview of copyright protection from a technical perspective.
arXiv Detail & Related papers (2024-02-04T04:00:33Z)
Training Is Everything: Artificial Intelligence, Copyright, and Fair Training [9.653656920225858]
Authors: Companies that use such content to train their AI engine often believe such usage should be considered "fair use" Authors: Copyright owners, as well as their supporters, consider the incorporation of copyrighted works into training sets for AI to constitute misappropriation of owners' intellectual property. We identify both strong and spurious arguments on both sides of this debate.
arXiv Detail & Related papers (2023-05-04T04:01:00Z)
Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
arXiv Detail & Related papers (2022-06-08T12:32:08Z)
Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z)
An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z)

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