Training Is Everything: Artificial Intelligence, Copyright, and Fair Training

• URL: http://arxiv.org/abs/2305.03720v1
• Date: Thu, 4 May 2023 04:01:00 GMT
• Title: Training Is Everything: Artificial Intelligence, Copyright, and Fair Training
• Authors: Andrew W. Torrance and Bill Tomlinson
• Abstract summary: 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.
• Score: 9.653656920225858
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To learn how to behave, the current revolutionary generation of AIs must be trained on vast quantities of published images, written works, and sounds, many of which fall within the core subject matter of copyright law. To some, the use of copyrighted works as training sets for AI is merely a transitory and non-consumptive use that does not materially interfere with owners' content or copyrights protecting it. Companies that use such content to train their AI engine often believe such usage should be considered "fair use" under United States law (sometimes known as "fair dealing" in other countries). By contrast, many 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, and, thus, decidedly not fair use under the law. This debate is vital to the future trajectory of AI and its applications. In this article, we analyze the arguments in favor of, and against, viewing the use of copyrighted works in training sets for AI as fair use. We call this form of fair use "fair training". We identify both strong and spurious arguments on both sides of this debate. In addition, we attempt to take a broader perspective, weighing the societal costs (e.g., replacement of certain forms of human employment) and benefits (e.g., the possibility of novel AI-based approaches to global issues such as environmental disruption) of allowing AI to make easy use of copyrighted works as training sets to facilitate the development, improvement, adoption, and diffusion of AI. Finally, we suggest that the debate over AI and copyrighted works may be a tempest in a teapot when placed in the wider context of massive societal challenges such as poverty, equality, climate change, and loss of biodiversity, to which AI may be part of the solution.

Related papers

• 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)
• Don't Kill the Baby: The Case for AI in Arbitration [0.0]
  Article argues that the FAA allows parties to contractually choose AI-driven arbitration, despite traditional reservations. By advocating for the use of AI in arbitration, it underscores the importance of respecting contractual autonomy. Ultimately, it calls for a balanced, open-minded approach to AI in arbitration, recognizing its potential to enhance the efficiency, fairness, and flexibility of dispute resolution.
  arXiv  Detail & Related papers  (2024-08-21T13:34:20Z)
• 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)
• Between Copyright and Computer Science: The Law and Ethics of Generative AI [1.534667887016089]
  Copyright and computer science continue to intersect and clash, but they can coexist. This Article shows that, contrary to some scholars' views, fair use law does not bless all ways that someone can gain access to copyrighted material. The copyright industry claims, however, that almost all uses of copyrighted material must be compensated, even for non-expressive uses.
  arXiv  Detail & Related papers  (2024-02-24T16:20:44Z)
• ChatGPT and Works Scholarly: Best Practices and Legal Pitfalls in Writing with AI [9.550238260901121]
  We offer approaches to evaluating whether or not such AI-writing violates copyright or falls within the safe harbor of fair use. As AI is likely to grow more capable in the coming years, it is appropriate to begin integrating AI into scholarly writing activities.
  arXiv  Detail & Related papers  (2023-05-04T15:38:20Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• 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)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• AI and Legal Argumentation: Aligning the Autonomous Levels of AI Legal Reasoning [0.0]
  Legal argumentation is a vital cornerstone of justice, underpinning an adversarial form of law. Extensive research has attempted to augment or undertake legal argumentation via the use of computer-based automation including Artificial Intelligence (AI) An innovative meta-approach is proposed to apply the Levels of Autonomy (LoA) of AI Legal Reasoning to the maturation of AI and Legal Argumentation (AILA)
  arXiv  Detail & Related papers  (2020-09-11T22:05:40Z)
• How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence [81.04070052740596]
  Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. This paper introduces the history, the current state, and the future directions of research in LegalAI.
  arXiv  Detail & Related papers  (2020-04-25T14:45:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.