The rapid uptake of AI-assisted and AI-generated (or “vibe”) coding may be one of the biggest shifts in research methodology since the advent of the computer. While this may speed up development, it comes with concerns about code quality, plagiarism, and risks of deskilling. Advances in this technology have been so rapid they are outpacing the development of ethical and legal policies, increasing risks of falling foul of copyright and plagiarism regulations.
What are the risks in using AI-coding?
While these new AI-coding assistants have amazing potential, they should not be seen as taking over coding from human developers but rather augmenting their capabilities. As with the usage of AI tools in writing text, when using AI to write code the responsibility has become less clear. While most researchers may have sufficient English knowledge to understand, edit, and take responsibility for AI-generated text, research programmers who may understand their discipline well, may not have sufficient knowledge to understand exactly what their AI-generated code does. This "debugging cliff" is potentially even more problematic as AI-generated code has a tendency to produce more errors, an analysis of real-world open source pull requests showing AI-generated code introducing 1.7x more defects. There are also uncertainties about researchers’ responsibility and accountability for AI-generated code. Particularly in a research software context where software and computational research are submitted for publication in scientific journals, and the reviewers and editors may have similar difficulties assessing AI-generated code.
Further uncertainty has surrounded licensing and attribution as fully autonomous AI outputs have no human author behind them. This leaves questions on whether you can patent or copyright AI-generated software, and also complicates open-source licensing. With the ability to translate code from one language to another using AI it is also unclear how translation relates to licensing. As with generative AI trained on text and images, there is a lack of transparency regarding the sources that were used for training coding assistants. With lawsuits filed against GitHub claiming that their Copilot coding assistant was trained on public projects without giving permission or appropriate credit to the original creators. With risks that these coding tools could be plagiarizing the efforts of others without the users’ knowledge, and inadvertently leading to data security issues through the accidental release of encrypted keys and breaches of data privacy laws. Training and running the LLMs behind these coding tools has required massive energy consumption, also raising sustainability concerns (see the related module on green computing).
How can you use AI-coding safely and responsibly?
While specific ethical guidelines for publishing and peer-review of AI-coded research are yet to be published at the time of drafting, by following the principles and general guidelines for usage of AI in research it is possible to responsibly use these tools while minimizing the legal and ethical risks. The rise of these tools has shifted the cognitive load for developers from creation to verification, making validation crucial to check if code is doing what it says it is doing, and mitigating overfilling and bias. Developers are adopting best practices that include using additional tests, and reserving vibe coding and less reliable outputs more for personal or low stakes projects. If you decide to share AI-generated code publicly, then this should follow ethical best practice guidelines developed by COPE and WAME for AI usage in scholarly publications. Authors who use AI tools must be transparent in disclosing their use and are the ones responsible for the content of the software and manuscript. This includes any parts produced by an AI tool, and authors are liable for any legal or ethical breaches the AI may inadvertently cause. The black-box nature of most AI-technologies makes transparency and reproducibility a challenge, but explainable AI techniques and integrating the usage of AI standards will assist in the validation and checking process, and improve trust and transparency to better facilitate re-use. As with any software, you must remember to provide good documentation to help others to use and maintain it in the future. For ML research there are many standards facilitating discoverability, access, and re-use such as the Crossant-ML metadata format. There are also a number of checklists and tools (e.g. REFORMS and DOME-ML) that can help focus on the essential information AI-driven research needs to capture, to facilitate sharing and more sustainable re-use of models.
One final challenge is the risk of deskilling, with increasing dependence on these tools making it harder for young developers to understand and learn how to code and troubleshoot independently, but organisations such as The Carpentries are working on training materials tackling this issue.
Conclusions
For safe and ethical use of AI-coding the main things to take note of are scrutiny, disclosure, and accountability.
AI-generated code needs to be carefully reviewed, edited, and tested by humans before sharing. Actively monitoring progress to catch problems early, alongside critically reviewing all generated code to ensure it meets scientific standards.
Disclose any LLM use alongside your code in your README and at the top of relevant modules. Upon submitting software and resulting work to a journal you should disclose how the Generative AI tools were used in their development, and be clear about what human review was performed on these outputs. This can be outlined in the covering letter and relevant sections in the paper.
Ultimately you as the human author take responsibility for the quality, correctness, and provenance of all code and the results it produces, regardless of how it was generated. If you are concerned about reliability, unclear provenance and licensing, you may want to reserve this code for personal projects rather than make it public.
For further information see related guides and Software Carpentry modules on Coding with AI.
Further Reading
Katz D, Hosseini M, Edmunds SC. Guest Post – Code Plagiarism and AI Create New Challenges for Publishing Integrity. Scholarly Kitchen. https://scholarlykitchen.sspnet.org/2025/08/28/guest-post-code-plagiarism-and-ai-create-new-challenges-for-publishing-integrity/
COPE Council. COPE position - Authorship and AI. https://doi.org/10.24318/cCVRZBms
WAME Recommendations on Chatbots and Generative Artificial Intelligence in Relation to Scholarly Publications. WAME. https://wame.org/page3.php?id=106
Bridgeford EW, Campbell I, Chen Z, Lin Z, Ritz H, Vandekerckhove J, Poldrack RA. Ten Simple Rules for AI-Assisted Coding in Science. aRxiv 2025. https://doi.org/10.48550/arXiv.2510.22254
Edmunds SC et al., (2026) ‘Integrating Machine Learning Standards in Disseminating Machine Learning Research’, Data Science Journal, 25(1), p. 1. Available at: https://doi.org/10.5334/dsj-2026-001
Ray PP. A Review on Vibe Coding: Fundamentals, State-of-the-art, Challenges and Future Directions. TechRxiv. May 09, 2025. doi:36227/techrxiv.174681482.27435614/v1
Coding with AI https://southampton-rsg-training.github.io/coding-with-ai