Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Coding

• URL: http://arxiv.org/abs/2411.10877v1
• Date: Sat, 16 Nov 2024 20:06:21 GMT
• Title: Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Coding
• Authors: Trevor Stalnaker, Nathan Wintersgill, Oscar Chaparro, Laura A. Heymann, Massimiliano Di Penta, Daniel M German, Denys Poshyvanyk,
• Abstract summary: Generative AI (GenAI) tools have already started to transform software development practices. The use of these tools raises important legal questions and potential risks. This study surveyed 574 GitHub developers who use GenAI tools for development activities.
• Score: 10.531612371200625
• License:
• Abstract: Generative AI (GenAI) tools have already started to transform software development practices. Despite their utility in tasks such as writing code, the use of these tools raises important legal questions and potential risks, particularly those associated with copyright law. In the midst of this uncertainty, this paper presents a study jointly conducted by software engineering and legal researchers that surveyed 574 GitHub developers who use GenAI tools for development activities. The survey and follow-up interviews probed the developers' opinions on emerging legal issues as well as their perception of copyrightability, ownership of generated code, and related considerations. We also investigate potential developer misconceptions, the impact of GenAI on developers' work, and developers' awareness of licensing/copyright risks. Qualitative and quantitative analysis showed that developers' opinions on copyright issues vary broadly and that many developers are aware of the nuances these legal questions involve. We provide: (1) a survey of 574 developers on the licensing and copyright aspects of GenAI for coding, (2) a snapshot of practitioners' views at a time when GenAI and perceptions of it are rapidly evolving, and (3) an analysis of developers' views, yielding insights and recommendations that can inform future regulatory decisions in this evolving field.

Related papers

• Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplace [2.5280615594444567]
  Generative AI coding tools are relatively new, and their impact on developers extends beyond traditional coding metrics. This study aims to illuminate developers' preexisting beliefs about generative AI tools, their self perceptions, and how regular use of these tools may alter these beliefs. Our findings reveal that the introduction and sustained use of generative AI coding tools significantly increases developers' perceptions of these tools as both useful and enjoyable.
  arXiv  Detail & Related papers  (2024-10-24T00:07:27Z)
• Voices from the Frontier: A Comprehensive Analysis of the OpenAI Developer Forum [5.667013605202579]
  OpenAI's advanced large language models (LLMs) have revolutionized natural language processing and enabled developers to create innovative applications. This paper presents a comprehensive analysis of the OpenAI Developer Forum. We focus on (1) popularity trends and user engagement patterns, and (2) a taxonomy of challenges and concerns faced by developers.
  arXiv  Detail & Related papers  (2024-08-03T06:57:43Z)
• Legal Aspects for Software Developers Interested in Generative AI Applications [5.772982243103395]
  Generative Artificial Intelligence (GenAI) has led to new technologies capable of generating high-quality code, natural language, and images. The next step is to integrate GenAI technology into products, a task typically conducted by software developers. This article sheds light on the current state of two such risks: data protection and copyright.
  arXiv  Detail & Related papers  (2024-04-25T14:17:34Z)
• 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)
• 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)
• Generative AI and US Intellectual Property Law [0.0]
  It remains to be seen whether human content creators can retain their intellectual property rights against generative AI software. Early signs from various courts are mixed as to whether and to what degree the results generated by AI models meet the legal standards of infringement under existing law.
  arXiv  Detail & Related papers  (2023-11-27T17:36:56Z)
• Report of the 1st Workshop on Generative AI and Law [78.62063815165968]
  This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw) A cross-disciplinary group of practitioners and scholars from computer science and law convened to discuss the technical, doctrinal, and policy challenges presented by law for Generative AI.
  arXiv  Detail & Related papers  (2023-11-11T04:13:37Z)
• LLM-based Interaction for Content Generation: A Case Study on the Perception of Employees in an IT department [85.1523466539595]
  This paper presents a questionnaire survey to identify the intention to use generative tools by employees of an IT company. Our results indicate a rather average acceptability of generative tools, although the more useful the tool is perceived to be, the higher the intention seems to be. Our analyses suggest that the frequency of use of generative tools is likely to be a key factor in understanding how employees perceive these tools in the context of their work.
  arXiv  Detail & Related papers  (2023-04-18T15:35:43Z)
• Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
  We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code. We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
  arXiv  Detail & Related papers  (2023-02-14T18:43:34Z)
• AI Explainability 360: Impact and Design [120.95633114160688]
  In 2019, we created AI Explainability 360 (Arya et al. 2020), an open source software toolkit featuring ten diverse and state-of-the-art explainability methods. This paper examines the impact of the toolkit with several case studies, statistics, and community feedback. The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.
  arXiv  Detail & Related papers  (2021-09-24T19:17:09Z)
• 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.