# Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development

arXiv: https://arxiv.org/abs/2411.10877 · HTML: https://mlsanity.com/paper/2411.10877/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 119 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 30 of 34)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Large Language Model Unlearning](https://mlsanity.com/paper/2310.10683/) (2023)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [Foundation Models and Fair Use](https://mlsanity.com/paper/2303.15715/) (2023)
- [Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy](https://mlsanity.com/paper/2210.17546/) (2022)
- [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023)
- [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023)
- [Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain](https://mlsanity.com/paper/2309.08133/) (2023)
- [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024)
- [Unveiling Memorization in Code Models](https://mlsanity.com/paper/2308.09932/) (2023)
- [Assessing the Security of GitHub Copilot Generated Code -- A Targeted Replication Study](https://mlsanity.com/paper/2311.11177/) (2023)
- [Gotcha! This Model Uses My Code! Evaluating Membership Leakage Risks in Code Models](https://mlsanity.com/paper/2310.01166/) (2023)
- [Whisper: Fast Flooding for Low-Power Wireless Networks](https://mlsanity.com/paper/1809.03699/) (2018)
- [TMI! Finetuned Models Leak Private Information from their Pretraining Data](https://mlsanity.com/paper/2306.01181/) (2023)
- [Automating Dependency Updates in Practice: An Exploratory Study on GitHub Dependabot](https://mlsanity.com/paper/2206.07230/) (2022)
- [Towards Traceability in Data Ecosystems using a Bill of Materials Model](https://mlsanity.com/paper/1904.04253/) (2019)
- [Understanding Skills for OSS Communities on GitHub](https://mlsanity.com/paper/2209.02222/) (2022)
- [Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code](https://mlsanity.com/paper/2402.09299/) (2024)
- [An Empirical Study on Software Bill of Materials: Where We Stand and the Road Ahead](https://mlsanity.com/paper/2301.05362/) (2023)
- [BOMs Away! Inside the Minds of Stakeholders: A Comprehensive Study of Bills of Materials for Software Systems](https://mlsanity.com/paper/2309.12206/) (2023)
- [Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines](https://mlsanity.com/paper/2402.19421/) (2024)
- [Developers' Perception of GitHub Actions: A Survey Analysis](https://mlsanity.com/paper/2303.04084/) (2023)

## Built on by (most cited first, 2 of 2)

- [Towards Privacy-Preserving Code Generation: Differentially Private Code Language Models](https://mlsanity.com/paper/2512.11482/) (2025)
- ["Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work](https://mlsanity.com/paper/2603.07459/) (2026)

## Abstract (arXiv)

Despite the utility that Generative AI (GenAI) tools provide for tasks such as writing code, the use of these tools raises important legal questions and potential risks, particularly those associated with copyright law. As lawmakers and regulators engage with those questions, the views of users can provide relevant perspectives. In this paper, we provide: (1) a survey of 574 developers on the licensing and copyright aspects of GenAI for coding, as well as follow-up interviews; (2) a snapshot of developers' 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. Our results show the benefits developers derive from GenAI, how they view the use of AI-generated code as similar to using other existing code, the varied opinions they have on who should own or be compensated for such code, that they are concerned about data leakage via GenAI, and much more, providing organizations and policymakers with valuable insights into how the technology is being used and what concerns stakeholders would like to see addressed.