From e1be1e442bff907984419a40665cfe1a72c78d06 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 16 Sep 2025 13:18:28 -0600 Subject: [PATCH 01/26] enh(blog): Add blog post on generative AI peer review policy This blog post outlines pyOpenSci's new peer review policy regarding the use of generative AI tools in scientific software, emphasizing transparency, ethical considerations, and the importance of human oversight in the review process. --- .../2025-09-16-generative-ai-peer-review.md | 139 ++++++++++++++++++ 1 file changed, 139 insertions(+) create mode 100644 _posts/2025-09-16-generative-ai-peer-review.md diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md new file mode 100644 index 00000000..381d0001 --- /dev/null +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -0,0 +1,139 @@ +--- +layout: single +title: "Navigating LLMs in Open Source: pyOpenSci's New Peer Review Policy" +excerpt: "Generative AI tools are making is easier to generate large amounts of code which in some cases is causing a strain on volunteer peer review programs like ours. Learn about pyOpenSci's policy on generative AI in peer review in this blog post." +author: "pyopensci" +permalink: /blog/generative-ai-peer-review-policy.html +header: + overlay_image: images/headers/pyopensci-floral.png +categories: + - blog-post + - community +classes: wide +toc: true +comments: true +last_modified: 2025-09-16 +--- + +authors: Leah Wasser, Mandy Moore, + +## Generative AI meets scientific open source + +It has been suggested that for some developers, using AI tools for tasks can increase efficiency by as much as 55%. But in open source scientific software, speed isn't everything—transparency, quality, and community trust matter just as much. So do the ethical questions these tools raise. + +**Edit this.** Whatever breakout content we want here.... needs to be all on a single line. +{: .notice--success} + + +## Why we need guidelines + +At [pyOpenSci](https://www.pyopensci.org/), we’ve drafted a new policy for our peer review process to set clear expectations around disclosing use of LLMs in scientific software packages. + +This is not about banning AI tools. We recognize their value to some. Instead, our goal is transparency. We want maintainers to **disclose when and how they’ve used LLMs** so editors and reviewers can fairly and efficiently evaluate submissions. + +## Our Approach: Transparency and Disclosure + +We know that people will continue to use LLMs. We also know they can meaningfully increase productivity and lower barriers to contribution for some. We also know that there are significant ethical, societal and other challenges that come with the development and use of LLM’s. + +Our community’s expectation is simple: **be open about it**. + +* Disclose LLM use in your README and at the top of relevant modules. +* Describe how the tools were used +* Be clear about what human review you performed. + +Transparency helps reviewers understand context, trace decisions, and focus their time where it matters most. + +### Human oversight + +LLM-assisted code must be **reviewed, edited, and tested by humans** before submission. + +* Run tests and confirm correctness. +* Check for security and quality issues. +* Ensure style, readability, and clear docstrings. +* Explain your review process in your software submission to pyOpenSci. + +Please don’t offload vetting to volunteer reviewers. Arrive with human-reviewed code that you understand, have tested, and can maintain. + +### Licensing awareness + +LLMs may be trained on mixed-license corpora. Outputs can create **license compatibility questions**, especially when your package uses a permissive license (MIT/BSD-3). + +* Acknowledge potential license ambiguity in your disclosure. +* Avoid pasting verbatim outputs that resemble known copyrighted code. +* Prefer human-edited, transformative outputs you fully understand. + +We can’t control upstream model training data, but we can be cautious, explicit and critical about our usage. + +### Ethics and inclusion + +LLM outputs can reflect and amplify bias in training data. In documentation and tutorials, that bias can harm the very communities we want to support. + +* Review AI-generated text for stereotypes or exclusionary language. +* Prefer plain, inclusive language. +* Invite feedback and review from diverse contributors. + +Inclusion is part of quality. Treat AI-generated text with the same care as code. + +## Supporting volunteer peer review + +Peer review runs on **volunteer time**. Rapid, AI-assisted submissions can overwhelm reviewers—especially when code hasn’t been vetted. + +* Submit smaller PRs with clear scopes. +* Summarize changes and provide test evidence. +* Flag AI-assisted sections so reviewers know where to look closely. +* Be responsive to feedback, especially on AI-generated code. + +These safeguards protect human capacity so high-quality packages can move through review efficiently. + +## Benefits and opportunities + +LLMs are already helping developers: + +* Explaining complex codebases +* Generating unit tests and docstrings +* In some cases, simplifying language barriers for participants in open source around the world +* Speeding up everyday workflows + +For some contributors, these tools make open source more accessible. + +## Challenges we must address + +### Overloaded peer review + +Peer review relies on volunteers. LLMs can produce large volumes of code quickly, increasing submissions with content that may not have been carefully reviewed by a human before reaching our review system. + +### Ethical and legal complexities + +LLMs are often trained on copyrighted or licensed material. Outputs may create conflicts when used in projects under different licenses. They can also reflect extractive practices, like data colonialism, and disproportionately harm underserved communities. + +### Bias and equity concerns + +AI-generated text can perpetuate bias. When it appears in documentation or tutorials, it can alienate the very groups open source most needs to welcome. + +### Environmental impacts + +Training and running LLMs [requires massive energy consumption](https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/), raising sustainability concerns that sit uncomfortably alongside much of the scientific research our community supports. + +### Impact on learning + +Heavy reliance on LLMs risks producing developers who can prompt, but not debug or maintain, code—undermining long-term project sustainability and growth. + +## What you can do now + +* **Be transparent.** Disclose LLM use in your README and modules. +* **Be accountable.** Thoroughly review, test, and edit AI-assisted code. +* **Be license-aware.** Note uncertainties and avoid verbatim look-alikes. +* **Be inclusive.** Check AI-generated docs for bias and clarity. +* **Be considerate.** Respect volunteer reviewers’ time. + + +
+## Join the conversation + +This policy is just the beginning. As AI continues to evolve, so will our practices. We invite you to: + +👉 Read the full draft policy +👉 Share your feedback and help us shape how the scientific Python community approaches AI in open source. + +The conversation is only starting, and your voice matters. +
From 869132c44dc7717c4d39ca3ad608beac337ef96a Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 23 Sep 2025 15:08:45 -0600 Subject: [PATCH 02/26] Apply suggestion from @jedbrown Co-authored-by: Jed Brown --- _posts/2025-09-16-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index 381d0001..8f2a48c8 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -1,7 +1,7 @@ --- layout: single title: "Navigating LLMs in Open Source: pyOpenSci's New Peer Review Policy" -excerpt: "Generative AI tools are making is easier to generate large amounts of code which in some cases is causing a strain on volunteer peer review programs like ours. Learn about pyOpenSci's policy on generative AI in peer review in this blog post." +excerpt: "Generative AI products are reducing the effort and skill necessary to generate large amounts of code, which in some cases is causing a strain on volunteer peer review programs like ours. Learn about pyOpenSci's policy on generative AI in peer review in this blog post." author: "pyopensci" permalink: /blog/generative-ai-peer-review-policy.html header: From 6ae999ed2af598ae2541182f559bf029f36c0eb8 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 23 Sep 2025 16:46:47 -0600 Subject: [PATCH 03/26] Apply suggestion from @lwasser --- _posts/2025-09-16-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index 8f2a48c8..e6c873fa 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -35,7 +35,7 @@ This is not about banning AI tools. We recognize their value to some. Instead, o We know that people will continue to use LLMs. We also know they can meaningfully increase productivity and lower barriers to contribution for some. We also know that there are significant ethical, societal and other challenges that come with the development and use of LLM’s. -Our community’s expectation is simple: **be open about it**. +Our community’s expectation is simple: **be open about and disclose any generative AI use in your package**. * Disclose LLM use in your README and at the top of relevant modules. * Describe how the tools were used From 5b0d7f13fb9d222348217e96eddd2d7152a3c217 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 23 Sep 2025 16:55:24 -0600 Subject: [PATCH 04/26] Apply suggestion from @jedbrown Co-authored-by: Jed Brown --- _posts/2025-09-16-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index e6c873fa..3fd18300 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -87,7 +87,7 @@ These safeguards protect human capacity so high-quality packages can move throug ## Benefits and opportunities -LLMs are already helping developers: +LLMs are already perceived as helping developers: * Explaining complex codebases * Generating unit tests and docstrings From 598bfa62c86e0554c26bf96f60d6adb7c4b34107 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 23 Sep 2025 16:55:49 -0600 Subject: [PATCH 05/26] Apply suggestion from @jedbrown Co-authored-by: Jed Brown --- _posts/2025-09-16-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index 3fd18300..3758881e 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -94,7 +94,7 @@ LLMs are already perceived as helping developers: * In some cases, simplifying language barriers for participants in open source around the world * Speeding up everyday workflows -For some contributors, these tools make open source more accessible. +Some contributors perceive these products as making open source more accessible. ## Challenges we must address From ce8e4991e90f866dc5ea16b8ed68d49bd4f92c6d Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 18 Nov 2025 10:40:55 -0700 Subject: [PATCH 06/26] Update _posts/2025-09-16-generative-ai-peer-review.md Co-authored-by: Jed Brown --- _posts/2025-09-16-generative-ai-peer-review.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index 3758881e..04c90a8c 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -56,7 +56,9 @@ Please don’t offload vetting to volunteer reviewers. Arrive with human-reviewe ### Licensing awareness -LLMs may be trained on mixed-license corpora. Outputs can create **license compatibility questions**, especially when your package uses a permissive license (MIT/BSD-3). +LLMs are trained on source code and documents with many licenses, most of which require attribution/preservation of a copyright notice (possibly in addition to other terms). LLM outputs sometimes produce verbatim or near-verbatim copies of [code](https://githubcopilotlitigation.com/case-updates.html) or [prose](https://arxiv.org/abs/2505.12546) from the training data, but with attribution stripped. Without attribution, such instances constitute a derivative work that violates the license, thus are likely to be copyright infringement and are certainly plagiarism. Copyright infringement and plagiarism are issues of process, not merely of the final artifact, so it is difficult to prescribe a reliable procedure for due diligence when working with LLM output, short of assuming that such output is always tainted and thus the generated code or derivative works can never come into the code base. We recognize that many users of LLM products for software development would consider such diligence impractical. + +If similarities with existing software is detected **and** the licenses are compatible, one can come into compliance with the license by complying with its terms, such as by adding attribution. When the source package has an [incompatible license](https://dwheeler.com/essays/floss-license-slide.html), there is no simple fix. For example, if LGPL-2.1 code is emitted by an LLM into an Apache-2.0 project, no amount of attribution or license changes can bring the project into compliance. The Apache-2.0 project cannot even relicense to LGPL-2.1 without consent from every contributor (or their copyright holder). In such cases, the project would be responsible for deleting all implicated code and derivative works, and rewriting it all using [clean-room techniques](https://en.wikipedia.org/wiki/Clean-room_design). * Acknowledge potential license ambiguity in your disclosure. * Avoid pasting verbatim outputs that resemble known copyrighted code. From 761c530e09cc47d4e0f71141c96aa8ac924d6374 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 18 Nov 2025 10:41:57 -0700 Subject: [PATCH 07/26] Apply suggestions from code review --- _posts/2025-09-16-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index 04c90a8c..9047fbfa 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -19,7 +19,7 @@ authors: Leah Wasser, Mandy Moore, ## Generative AI meets scientific open source -It has been suggested that for some developers, using AI tools for tasks can increase efficiency by as much as 55%. But in open source scientific software, speed isn't everything—transparency, quality, and community trust matter just as much. So do the ethical questions these tools raise. +Some developers believe that using AI products increases efficiency. However, in scientific open-source, speed isn't everything—transparency, quality, and community trust are just as important. Similarly, the ethical questions that these tools raise are also a concern. **Edit this.** Whatever breakout content we want here.... needs to be all on a single line. {: .notice--success} From 5d22fda9fea35d855304ce841400fb43fad9e079 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 18 Nov 2025 10:43:38 -0700 Subject: [PATCH 08/26] Apply suggestion from @jedbrown Co-authored-by: Jed Brown --- _posts/2025-09-16-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index 9047fbfa..58f5e73c 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -33,7 +33,7 @@ This is not about banning AI tools. We recognize their value to some. Instead, o ## Our Approach: Transparency and Disclosure -We know that people will continue to use LLMs. We also know they can meaningfully increase productivity and lower barriers to contribution for some. We also know that there are significant ethical, societal and other challenges that come with the development and use of LLM’s. +We acknowledge that social and ethical norms and concern for environmental and societal externalities varies greatly across the community, and yet few members of the community will look to pyOpenSci for guidance on whether to use LLMs in their own work. Our focus thus centers on assisting with informed decision-making and consent with respect to LLM use in the submission, reviewing, and editorial process. Our community’s expectation is simple: **be open about and disclose any generative AI use in your package**. From c386fedc495116e2dff259856d479691ff8db1aa Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 18 Nov 2025 10:44:14 -0700 Subject: [PATCH 09/26] Apply suggestion from @lwasser --- _posts/2025-09-16-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index 58f5e73c..f98757b9 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -49,7 +49,7 @@ LLM-assisted code must be **reviewed, edited, and tested by humans** before subm * Run tests and confirm correctness. * Check for security and quality issues. -* Ensure style, readability, and clear docstrings. +* Ensure style, readability, and concise docstrings. * Explain your review process in your software submission to pyOpenSci. Please don’t offload vetting to volunteer reviewers. Arrive with human-reviewed code that you understand, have tested, and can maintain. From 6e4b299675da0c1fbcda313e2f0b83123eb87c73 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 18 Nov 2025 10:48:54 -0700 Subject: [PATCH 10/26] Apply suggestion from @lwasser --- _posts/2025-09-16-generative-ai-peer-review.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index f98757b9..08954f2c 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -60,8 +60,8 @@ LLMs are trained on source code and documents with many licenses, most of which If similarities with existing software is detected **and** the licenses are compatible, one can come into compliance with the license by complying with its terms, such as by adding attribution. When the source package has an [incompatible license](https://dwheeler.com/essays/floss-license-slide.html), there is no simple fix. For example, if LGPL-2.1 code is emitted by an LLM into an Apache-2.0 project, no amount of attribution or license changes can bring the project into compliance. The Apache-2.0 project cannot even relicense to LGPL-2.1 without consent from every contributor (or their copyright holder). In such cases, the project would be responsible for deleting all implicated code and derivative works, and rewriting it all using [clean-room techniques](https://en.wikipedia.org/wiki/Clean-room_design). -* Acknowledge potential license ambiguity in your disclosure. -* Avoid pasting verbatim outputs that resemble known copyrighted code. +* Be aware that when you directly use content developed by an LLM, there will be inherent license conflicts. +* Be aware that LLM products can potentially return copyrighted code verbatim in some cases. Avoid pasting verbatim outputs from an LLM into your package. Rather, if you use LLMs in your work, carefully review, edit, and modify the content, and * Prefer human-edited, transformative outputs you fully understand. We can’t control upstream model training data, but we can be cautious, explicit and critical about our usage. From 12dcc3842e54c9206f652a4ee92cc9741a4a530d Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 18 Nov 2025 10:49:38 -0700 Subject: [PATCH 11/26] Apply suggestion from @lwasser --- _posts/2025-09-16-generative-ai-peer-review.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index 08954f2c..803ffcec 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -98,6 +98,10 @@ LLMs are already perceived as helping developers: Some contributors perceive these products as making open source more accessible. +### Incorrectness of LLMs and misleading time benefits + +Although it is commonly stated that LLMs help improve the productivity of high-level developers, recent scientific explorations of this hypothesis indicate the contrary (see https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ for an excellent discussion on this). What's more is that the responses of LLM's for complex coding tasks tend to be incorrect (e.g., https://arxiv.org/html/2407.06153v1). Therefore, it is crucial that, if an LLM is used to help produce code, that the correctness of the code is evaluated separately from the LLM. + ## Challenges we must address ### Overloaded peer review From 8927a1c669b4ddc57f075f4e40dc60d398e6a318 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 18 Nov 2025 10:49:59 -0700 Subject: [PATCH 12/26] Apply suggestion from @jedbrown Co-authored-by: Jed Brown --- _posts/2025-09-16-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index 803ffcec..fee72ba8 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -110,7 +110,7 @@ Peer review relies on volunteers. LLMs can produce large volumes of code quickly ### Ethical and legal complexities -LLMs are often trained on copyrighted or licensed material. Outputs may create conflicts when used in projects under different licenses. They can also reflect extractive practices, like data colonialism, and disproportionately harm underserved communities. +LLMs are often trained on copyrighted material with varying (or no) licenses. Outputs may constitute copyright infringement and/or ethical violations such as plagiarism. They can also reflect extractive practices, like data colonialism, and disproportionately harm underserved communities. ### Bias and equity concerns From a9cce3eced78d1da0380bbb1c0f9dd63724e5cdb Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 18 Nov 2025 10:50:50 -0700 Subject: [PATCH 13/26] Apply suggestion from @lwasser --- _posts/2025-09-16-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md index fee72ba8..75bb22b9 100644 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ b/_posts/2025-09-16-generative-ai-peer-review.md @@ -15,7 +15,7 @@ comments: true last_modified: 2025-09-16 --- -authors: Leah Wasser, Mandy Moore, +authors: Leah Wasser, Jed Brown, Carter Rhea, Ellie Abrahams ## Generative AI meets scientific open source From b66e70cc0e74a88c4ec324e07d6505e97f02d540 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Tue, 18 Nov 2025 12:09:37 -0700 Subject: [PATCH 14/26] enh: more edits and updates --- .../2025-09-16-generative-ai-peer-review.md | 145 ----------------- .../2025-11-18-generative-ai-peer-review.md | 146 ++++++++++++++++++ 2 files changed, 146 insertions(+), 145 deletions(-) delete mode 100644 _posts/2025-09-16-generative-ai-peer-review.md create mode 100644 _posts/2025-11-18-generative-ai-peer-review.md diff --git a/_posts/2025-09-16-generative-ai-peer-review.md b/_posts/2025-09-16-generative-ai-peer-review.md deleted file mode 100644 index 75bb22b9..00000000 --- a/_posts/2025-09-16-generative-ai-peer-review.md +++ /dev/null @@ -1,145 +0,0 @@ ---- -layout: single -title: "Navigating LLMs in Open Source: pyOpenSci's New Peer Review Policy" -excerpt: "Generative AI products are reducing the effort and skill necessary to generate large amounts of code, which in some cases is causing a strain on volunteer peer review programs like ours. Learn about pyOpenSci's policy on generative AI in peer review in this blog post." -author: "pyopensci" -permalink: /blog/generative-ai-peer-review-policy.html -header: - overlay_image: images/headers/pyopensci-floral.png -categories: - - blog-post - - community -classes: wide -toc: true -comments: true -last_modified: 2025-09-16 ---- - -authors: Leah Wasser, Jed Brown, Carter Rhea, Ellie Abrahams - -## Generative AI meets scientific open source - -Some developers believe that using AI products increases efficiency. However, in scientific open-source, speed isn't everything—transparency, quality, and community trust are just as important. Similarly, the ethical questions that these tools raise are also a concern. - -**Edit this.** Whatever breakout content we want here.... needs to be all on a single line. -{: .notice--success} - - -## Why we need guidelines - -At [pyOpenSci](https://www.pyopensci.org/), we’ve drafted a new policy for our peer review process to set clear expectations around disclosing use of LLMs in scientific software packages. - -This is not about banning AI tools. We recognize their value to some. Instead, our goal is transparency. We want maintainers to **disclose when and how they’ve used LLMs** so editors and reviewers can fairly and efficiently evaluate submissions. - -## Our Approach: Transparency and Disclosure - -We acknowledge that social and ethical norms and concern for environmental and societal externalities varies greatly across the community, and yet few members of the community will look to pyOpenSci for guidance on whether to use LLMs in their own work. Our focus thus centers on assisting with informed decision-making and consent with respect to LLM use in the submission, reviewing, and editorial process. - -Our community’s expectation is simple: **be open about and disclose any generative AI use in your package**. - -* Disclose LLM use in your README and at the top of relevant modules. -* Describe how the tools were used -* Be clear about what human review you performed. - -Transparency helps reviewers understand context, trace decisions, and focus their time where it matters most. - -### Human oversight - -LLM-assisted code must be **reviewed, edited, and tested by humans** before submission. - -* Run tests and confirm correctness. -* Check for security and quality issues. -* Ensure style, readability, and concise docstrings. -* Explain your review process in your software submission to pyOpenSci. - -Please don’t offload vetting to volunteer reviewers. Arrive with human-reviewed code that you understand, have tested, and can maintain. - -### Licensing awareness - -LLMs are trained on source code and documents with many licenses, most of which require attribution/preservation of a copyright notice (possibly in addition to other terms). LLM outputs sometimes produce verbatim or near-verbatim copies of [code](https://githubcopilotlitigation.com/case-updates.html) or [prose](https://arxiv.org/abs/2505.12546) from the training data, but with attribution stripped. Without attribution, such instances constitute a derivative work that violates the license, thus are likely to be copyright infringement and are certainly plagiarism. Copyright infringement and plagiarism are issues of process, not merely of the final artifact, so it is difficult to prescribe a reliable procedure for due diligence when working with LLM output, short of assuming that such output is always tainted and thus the generated code or derivative works can never come into the code base. We recognize that many users of LLM products for software development would consider such diligence impractical. - -If similarities with existing software is detected **and** the licenses are compatible, one can come into compliance with the license by complying with its terms, such as by adding attribution. When the source package has an [incompatible license](https://dwheeler.com/essays/floss-license-slide.html), there is no simple fix. For example, if LGPL-2.1 code is emitted by an LLM into an Apache-2.0 project, no amount of attribution or license changes can bring the project into compliance. The Apache-2.0 project cannot even relicense to LGPL-2.1 without consent from every contributor (or their copyright holder). In such cases, the project would be responsible for deleting all implicated code and derivative works, and rewriting it all using [clean-room techniques](https://en.wikipedia.org/wiki/Clean-room_design). - -* Be aware that when you directly use content developed by an LLM, there will be inherent license conflicts. -* Be aware that LLM products can potentially return copyrighted code verbatim in some cases. Avoid pasting verbatim outputs from an LLM into your package. Rather, if you use LLMs in your work, carefully review, edit, and modify the content, and -* Prefer human-edited, transformative outputs you fully understand. - -We can’t control upstream model training data, but we can be cautious, explicit and critical about our usage. - -### Ethics and inclusion - -LLM outputs can reflect and amplify bias in training data. In documentation and tutorials, that bias can harm the very communities we want to support. - -* Review AI-generated text for stereotypes or exclusionary language. -* Prefer plain, inclusive language. -* Invite feedback and review from diverse contributors. - -Inclusion is part of quality. Treat AI-generated text with the same care as code. - -## Supporting volunteer peer review - -Peer review runs on **volunteer time**. Rapid, AI-assisted submissions can overwhelm reviewers—especially when code hasn’t been vetted. - -* Submit smaller PRs with clear scopes. -* Summarize changes and provide test evidence. -* Flag AI-assisted sections so reviewers know where to look closely. -* Be responsive to feedback, especially on AI-generated code. - -These safeguards protect human capacity so high-quality packages can move through review efficiently. - -## Benefits and opportunities - -LLMs are already perceived as helping developers: - -* Explaining complex codebases -* Generating unit tests and docstrings -* In some cases, simplifying language barriers for participants in open source around the world -* Speeding up everyday workflows - -Some contributors perceive these products as making open source more accessible. - -### Incorrectness of LLMs and misleading time benefits - -Although it is commonly stated that LLMs help improve the productivity of high-level developers, recent scientific explorations of this hypothesis indicate the contrary (see https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ for an excellent discussion on this). What's more is that the responses of LLM's for complex coding tasks tend to be incorrect (e.g., https://arxiv.org/html/2407.06153v1). Therefore, it is crucial that, if an LLM is used to help produce code, that the correctness of the code is evaluated separately from the LLM. - -## Challenges we must address - -### Overloaded peer review - -Peer review relies on volunteers. LLMs can produce large volumes of code quickly, increasing submissions with content that may not have been carefully reviewed by a human before reaching our review system. - -### Ethical and legal complexities - -LLMs are often trained on copyrighted material with varying (or no) licenses. Outputs may constitute copyright infringement and/or ethical violations such as plagiarism. They can also reflect extractive practices, like data colonialism, and disproportionately harm underserved communities. - -### Bias and equity concerns - -AI-generated text can perpetuate bias. When it appears in documentation or tutorials, it can alienate the very groups open source most needs to welcome. - -### Environmental impacts - -Training and running LLMs [requires massive energy consumption](https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/), raising sustainability concerns that sit uncomfortably alongside much of the scientific research our community supports. - -### Impact on learning - -Heavy reliance on LLMs risks producing developers who can prompt, but not debug or maintain, code—undermining long-term project sustainability and growth. - -## What you can do now - -* **Be transparent.** Disclose LLM use in your README and modules. -* **Be accountable.** Thoroughly review, test, and edit AI-assisted code. -* **Be license-aware.** Note uncertainties and avoid verbatim look-alikes. -* **Be inclusive.** Check AI-generated docs for bias and clarity. -* **Be considerate.** Respect volunteer reviewers’ time. - - -
-## Join the conversation - -This policy is just the beginning. As AI continues to evolve, so will our practices. We invite you to: - -👉 Read the full draft policy -👉 Share your feedback and help us shape how the scientific Python community approaches AI in open source. - -The conversation is only starting, and your voice matters. -
diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md new file mode 100644 index 00000000..97e2a091 --- /dev/null +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -0,0 +1,146 @@ +--- +layout: single +title: "Navigating LLMs in Open Source: pyOpenSci's New Peer Review Policy" +excerpt: "Generative AI products are reducing the effort and skill necessary to generate large amounts of code. In some cases, this strains volunteer peer review programs like ours. Learn about pyOpenSci's approach to developing a Generative AI policy for our software peer review program." +author: "pyopensci" +permalink: /blog/generative-ai-peer-review-policy.html +header: + overlay_image: images/headers/pyopensci-floral.png +categories: + - blog-post + - community +classes: wide +toc: true +comments: true +last_modified: 2025-09-16 +--- + +authors: Leah Wasser, Jed Brown, Carter Rhea, Ellie Abrahams + +## Generative AI meets scientific open source + +Some developers believe that using AI products increases efficiency. However, in scientific open source, speed isn't everything—transparency, quality, and community trust are just as important as understanding the environmental impacts of using large language models in our everyday work. Similarly, the ethical questions that these tools raise are also a concern as some communities may benefit from the same tools that hurt others. + +## Why we need guidelines + +At pyOpenSci, we’ve drafted a new policy for our peer review process to set clear expectations for disclosing the use of LLMs in scientific open-source software. + +This is not about banning AI tools. We recognize their value to some people. Instead, our goal is transparency. We want maintainers to **disclose when and how they’ve used LLMs** so editors and reviewers can fairly and efficiently evaluate submissions. Further, we want to avoid burdening our volunteer editorial and reviewer team with being the first to review generated code. + +## A complex topic: Benefits and concerns + +LLMs are perceived as helping developers: + +* Explain complex codebases +* Generate unit tests and docstrings +* In some cases, simplifying language barriers for participants in open source around the world +* Speeding up everyday workflows + +Some contributors also perceive these products as making open source more accessible. However, LLM's also present +unprecedented social and environmental challenges. + +### Incorrectness of LLMs and misleading time benefits + +Although it is commonly stated that LLMs help improve the productivity of high-level developers, recent scientific explorations of this hypothesis [indicate the contrary](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/). What's more is that the responses of LLMs for complex coding tasks [tend to be incorrect](https://arxiv.org/html/2407.06153v1) and/or overly verbose/inefficient. It is crucial that, if you use an LLM to help produce code, you should independently evaluate code correctness and efficiency. + +### Environmental impacts + +Training and running LLMs [requires massive energy consumption](https://www.unep.org/news-and-stories/story/ai-has-environmental-problem-heres-what-world-can-do-about), raising sustainability concerns that sit uncomfortably alongside much of the global scale scientific research that our community supports. + +### Impact on learning + +Heavy reliance on LLMs risks producing developers who can prompt, but not debug or maintain, code—undermining long-term project sustainability and growth. This also in the long run will make it [harder for young developers to learn how to code, and troubleshoot independently](https://knowledge.wharton.upenn.edu/article/without-guardrails-generative-ai-can-harm-education/). + +> We’re really worried that if humans don’t learn, if they start using these tools as a crutch and rely on it, then they won’t actually build those fundamental skills to be able to use these tools effectively in the future. *Hamsa Bastani* + +### Ethics and inclusion + +LLM outputs can reflect and amplify bias in training data. In documentation and tutorials, that bias can harm the very communities we want to support. + +## Our Approach: Transparency and Disclosure + +We acknowledge that social and ethical norms, as well as concerns about environmental and societal impacts, vary widely across the community. We are not here to judge anyone who uses or doesn't use LLMs. Our focus centers on supporting informed decision-making and consent regarding LLM use in the pyOpenSci software submission, review, and editorial process. + +Our community’s expectation is simple: **be open about and disclose any Generative AI use in your package** when you submit it to our open software review process. + +* Disclose LLM use in your README and at the top of relevant modules. +* Describe how the Generative AI tools were used in your package's development. +* Be clear about what human review you performed on Generative AI outputs before submitting the package to our open peer review process. + +Transparency helps reviewers understand context, trace decisions, and focus their time where it matters most. + +### Human oversight + +LLM-assisted code must be **reviewed, edited, and tested by humans** before submission. + +* Run your tests and confirm the correctness of the code that you submitted. +* Check for security and quality issues. +* Ensure style, readability, and concise docstrings. +* Explain your review process in your software submission to pyOpenSci. + +Please **don’t offload vetting of generative AI content to volunteer reviewers**. Arrive with human-reviewed code that you understand, have tested, and can maintain. + +### Watch out for licensing issues. + +LLMs are trained on large amounts of open source code; most of that code has licenses that require attribution. +The problem? LLMs sometimes spit out near-exact copies of that training data, but without any attribution or copyright notices. + +Why this matters: + +* Using LLM output verbatim could violate the original code's license +* You might accidentally commit plagiarism or copyright infringement by using that output verbatim in your code +* Due diligence is nearly impossible since you can't trace what the LLM "learned from" (most LLM's are black boxes) + +When licenses clash, it gets messy. Say your package uses an MIT license (common in scientific Python), but an LLM outputs Apache-2.0 or GPL code—those licenses aren't compatible. You can't just add attribution to fix it. Technically, you'd have to delete everything and rewrite it from scratch to comply with the licensing requirements. + +While this is all tricky, here's what you can do, now: + +*Prefer human-edited, transformative outputs you fully understand* + +* Be aware that when you directly use content developed by an LLM, there will be inherent license conflicts. +* Be aware that LLM products can potentially return copyrighted code verbatim. **Don't paste LLM outputs directly into your code**. Instead, review, edit, and transform anything an LLM gives you. Consider using [clean-room techniques](https://en.wikipedia.org/wiki/Clean-room_design) to achieve this. +* **Make sure you fully understand the code before using it:** This is actually in your best interest because you can learn a lot about programming by asking an LLM questions and reviewing the output critically. + +You can't control what's in training data, but you can be thoughtful about how you use these tools. + +
+Examples of how these licensing issues are impacting and stressing our legal systems: + +* [GitHub Copilot litication](https://githubcopilotlitigation.com/case-updates.html) +* [Litigation around text from LLMs](https://arxiv.org/abs/2505.12546) +* [incompatible licenses](https://dwheeler.com/essays/floss-license-slide.html) +
+ +### Review for bias + +Inclusion is part of quality. Treat AI-generated text with the same care as code. +Given the known biases that can manifest in Generative AI-derived text: + +* Review AI-generated text for stereotypes or exclusionary language. +* Prefer plain, inclusive language. +* Invite feedback and review from diverse contributors. + +## Things to consider in your development workflows + +If you are a maintainer or a contributor, some of the above can apply to your development and contribution process, too. +Similar to how peer review systems are being taxed, rapid, AI-assisted pull requests and issues can also overwhelm maintainers too. To combat this: + +* Open an issue first before submitting a pull request to ensure it's welcome and needed +* Keep your pull requests small with clear scopes. +* If you use LLMs, test and edit all of the output before you submit a pull request or issue. +* Flag AI-assisted sections of any contribution so maintainers know where to look closely. +* Be responsive to feedback from maintainers, especially when submitting code that is AI-generated. + +## Where we go from here + +A lot of thought and consideration has gone into the development of pyOpenSci's Generative AI policies. +We will continue to suggest best practices for embracing modern technologies while critically evaluating their realities and the impacts they have on our ecosystem. These guidelines help us maintain the quality and integrity of packages in our peer review process while protecting the volunteer community that makes open peer review possible. As AI tools evolve, so will our approach—but transparency, human oversight, and community trust will always remain at the center of our work. + +## Join the conversation + +This policy is just the beginning. As AI continues to evolve, so will our practices. We invite you to: + +👉 [Read the full draft policy and discussion](https://github.com/pyOpenSci/software-peer-review/pull/344) +👉 Share your feedback and help us shape how the scientific Python community approaches Generative AI in open source. + +The conversation is only starting, and your voice matters. From 86fd7a2e28f5c0274bfa2793438e639f5fb67501 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 13:25:23 -0700 Subject: [PATCH 15/26] Apply suggestion from @willingc Co-authored-by: Carol Willing --- _posts/2025-11-18-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index 97e2a091..93bbf9ec 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -19,7 +19,7 @@ authors: Leah Wasser, Jed Brown, Carter Rhea, Ellie Abrahams ## Generative AI meets scientific open source -Some developers believe that using AI products increases efficiency. However, in scientific open source, speed isn't everything—transparency, quality, and community trust are just as important as understanding the environmental impacts of using large language models in our everyday work. Similarly, the ethical questions that these tools raise are also a concern as some communities may benefit from the same tools that hurt others. +Some developers believe that using AI products increases efficiency. However, in scientific open source, speed isn't everything—transparency, quality, and community trust are just as important as understanding the environmental impacts of using large language models in our everyday work. Similarly, ethical questions arise when tools may benefit some communities while harming others. ## Why we need guidelines From a22e9f5faf0a8c5e2562a9c50ee49f2b127bd693 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 13:25:46 -0700 Subject: [PATCH 16/26] Apply suggestion from @willingc Co-authored-by: Carol Willing --- _posts/2025-11-18-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index 93bbf9ec..fea9d908 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -25,7 +25,7 @@ Some developers believe that using AI products increases efficiency. However, in At pyOpenSci, we’ve drafted a new policy for our peer review process to set clear expectations for disclosing the use of LLMs in scientific open-source software. -This is not about banning AI tools. We recognize their value to some people. Instead, our goal is transparency. We want maintainers to **disclose when and how they’ve used LLMs** so editors and reviewers can fairly and efficiently evaluate submissions. Further, we want to avoid burdening our volunteer editorial and reviewer team with being the first to review generated code. +Our goal is transparency and fostering reproducible research. For scientific rigor, we want maintainers to **disclose when and how they’ve used LLMs** so editors and reviewers can fairly and efficiently evaluate submissions. Further, we want to avoid burdening our volunteer editorial and reviewer team with being the initial reviewers of generated code. ## A complex topic: Benefits and concerns From e119c2a775232517efe2f22362f8607ca9288fb9 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 13:26:45 -0700 Subject: [PATCH 17/26] Apply suggestion from @willingc Co-authored-by: Carol Willing --- _posts/2025-11-18-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index fea9d908..16175c55 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -36,7 +36,7 @@ LLMs are perceived as helping developers: * In some cases, simplifying language barriers for participants in open source around the world * Speeding up everyday workflows -Some contributors also perceive these products as making open source more accessible. However, LLM's also present +Some contributors also perceive these products as making open source more accessible. However, LLMs also present unprecedented social and environmental challenges. ### Incorrectness of LLMs and misleading time benefits From 5cd3d28cfaff1ae65e685781af6944eb12284788 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 13:26:59 -0700 Subject: [PATCH 18/26] Apply suggestion from @willingc Co-authored-by: Carol Willing --- _posts/2025-11-18-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index 16175c55..ce2d5b93 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -45,7 +45,7 @@ Although it is commonly stated that LLMs help improve the productivity of high-l ### Environmental impacts -Training and running LLMs [requires massive energy consumption](https://www.unep.org/news-and-stories/story/ai-has-environmental-problem-heres-what-world-can-do-about), raising sustainability concerns that sit uncomfortably alongside much of the global scale scientific research that our community supports. +Training and running LLMs [requires massive energy consumption](https://www.unep.org/news-and-stories/story/ai-has-environmental-problem-heres-what-world-can-do-about), raising sustainability concerns that sit uncomfortably alongside much of the global-scale scientific research that our community supports. ### Impact on learning From 8e6c94a0bcfb7101b476516ac7012ac87be89093 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 13:27:15 -0700 Subject: [PATCH 19/26] Apply suggestion from @willingc Co-authored-by: Carol Willing --- _posts/2025-11-18-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index ce2d5b93..9565201a 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -49,7 +49,7 @@ Training and running LLMs [requires massive energy consumption](https://www.unep ### Impact on learning -Heavy reliance on LLMs risks producing developers who can prompt, but not debug or maintain, code—undermining long-term project sustainability and growth. This also in the long run will make it [harder for young developers to learn how to code, and troubleshoot independently](https://knowledge.wharton.upenn.edu/article/without-guardrails-generative-ai-can-harm-education/). +Heavy reliance on LLMs risks producing developers who can prompt, but not debug, maintain, or secure production code. This risk undermines long-term project sustainability and growth. In the long run, it will make it [harder for young developers to learn how to code and troubleshoot independently](https://knowledge.wharton.upenn.edu/article/without-guardrails-generative-ai-can-harm-education/). > We’re really worried that if humans don’t learn, if they start using these tools as a crutch and rely on it, then they won’t actually build those fundamental skills to be able to use these tools effectively in the future. *Hamsa Bastani* From 6dd3a9ab81f045dcc45c3692340cd70aef9a20c1 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 13:27:29 -0700 Subject: [PATCH 20/26] Apply suggestion from @willingc Co-authored-by: Carol Willing --- _posts/2025-11-18-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index 9565201a..4da40381 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -61,7 +61,7 @@ LLM outputs can reflect and amplify bias in training data. In documentation and We acknowledge that social and ethical norms, as well as concerns about environmental and societal impacts, vary widely across the community. We are not here to judge anyone who uses or doesn't use LLMs. Our focus centers on supporting informed decision-making and consent regarding LLM use in the pyOpenSci software submission, review, and editorial process. -Our community’s expectation is simple: **be open about and disclose any Generative AI use in your package** when you submit it to our open software review process. +Our community’s expectation is simple: **be open and disclose any Generative AI use in your package** when you submit it to our open software review process. * Disclose LLM use in your README and at the top of relevant modules. * Describe how the Generative AI tools were used in your package's development. From ab501542b46adafe5ec5b71358536fc29929d03a Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 13:51:21 -0700 Subject: [PATCH 21/26] Apply suggestion from @lwasser --- _posts/2025-11-18-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index 4da40381..1e19607a 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -88,7 +88,7 @@ The problem? LLMs sometimes spit out near-exact copies of that training data, bu Why this matters: * Using LLM output verbatim could violate the original code's license -* You might accidentally commit plagiarism or copyright infringement by using that output verbatim in your code +* License conflicts can occur if your package's license (e.g., MIT) is incompatible with code patterns that the LLM learned on such as code licensed as GPL or Apache-2.0. * Due diligence is nearly impossible since you can't trace what the LLM "learned from" (most LLM's are black boxes) When licenses clash, it gets messy. Say your package uses an MIT license (common in scientific Python), but an LLM outputs Apache-2.0 or GPL code—those licenses aren't compatible. You can't just add attribution to fix it. Technically, you'd have to delete everything and rewrite it from scratch to comply with the licensing requirements. From 6a9998d68c9973dbf4e092f92bafe35ebffc1e2a Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 13:51:28 -0700 Subject: [PATCH 22/26] Apply suggestion from @lwasser --- _posts/2025-11-18-generative-ai-peer-review.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index 1e19607a..9a0dca68 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -93,7 +93,9 @@ Why this matters: When licenses clash, it gets messy. Say your package uses an MIT license (common in scientific Python), but an LLM outputs Apache-2.0 or GPL code—those licenses aren't compatible. You can't just add attribution to fix it. Technically, you'd have to delete everything and rewrite it from scratch to comply with the licensing requirements. -While this is all tricky, here's what you can do, now: +The reality of all of this is that you can't eliminate this risk of license infringement or plagiarism with current LLM technology. But you can be more thoughtful about how you use the technology. + +**What you can do now:** *Prefer human-edited, transformative outputs you fully understand* From 7b838202ad0d9292f543e0dce17eaf4107ef01e8 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 13:51:38 -0700 Subject: [PATCH 23/26] Apply suggestion from @lwasser --- _posts/2025-11-18-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index 9a0dca68..866777a0 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -89,7 +89,7 @@ Why this matters: * Using LLM output verbatim could violate the original code's license * License conflicts can occur if your package's license (e.g., MIT) is incompatible with code patterns that the LLM learned on such as code licensed as GPL or Apache-2.0. -* Due diligence is nearly impossible since you can't trace what the LLM "learned from" (most LLM's are black boxes) +* * You can't trace what content the LLM learned from (the black box problem); this makes due diligence impossible on your part. You might accidentally commit plagiarism or copyright infringement by using LLM output in your code even if you modify it. When licenses clash, it gets messy. Say your package uses an MIT license (common in scientific Python), but an LLM outputs Apache-2.0 or GPL code—those licenses aren't compatible. You can't just add attribution to fix it. Technically, you'd have to delete everything and rewrite it from scratch to comply with the licensing requirements. From 57c41824bcdb84ee67449ddce20f63cc5db7d759 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 13:51:47 -0700 Subject: [PATCH 24/26] Apply suggestion from @lwasser --- _posts/2025-11-18-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index 866777a0..90629c45 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -87,7 +87,7 @@ The problem? LLMs sometimes spit out near-exact copies of that training data, bu Why this matters: -* Using LLM output verbatim could violate the original code's license +* LLM-generated code may be *substantially similar* to copyrighted training data; sometimes it is identical. Copyright law focuses on how similar your content is compared to the original. * License conflicts can occur if your package's license (e.g., MIT) is incompatible with code patterns that the LLM learned on such as code licensed as GPL or Apache-2.0. * * You can't trace what content the LLM learned from (the black box problem); this makes due diligence impossible on your part. You might accidentally commit plagiarism or copyright infringement by using LLM output in your code even if you modify it. From d81a83fc2ef1e4d8b02962a5853354eece1855c3 Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 14:07:08 -0700 Subject: [PATCH 25/26] enh: edits from review --- _posts/2025-11-18-generative-ai-peer-review.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index 90629c45..5262140b 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -36,7 +36,7 @@ LLMs are perceived as helping developers: * In some cases, simplifying language barriers for participants in open source around the world * Speeding up everyday workflows -Some contributors also perceive these products as making open source more accessible. However, LLMs also present +Some contributors also believe these products open source more accessible. And for some, maybe they do. However, LLMs also present unprecedented social and environmental challenges. ### Incorrectness of LLMs and misleading time benefits @@ -88,20 +88,20 @@ The problem? LLMs sometimes spit out near-exact copies of that training data, bu Why this matters: * LLM-generated code may be *substantially similar* to copyrighted training data; sometimes it is identical. Copyright law focuses on how similar your content is compared to the original. -* License conflicts can occur if your package's license (e.g., MIT) is incompatible with code patterns that the LLM learned on such as code licensed as GPL or Apache-2.0. -* * You can't trace what content the LLM learned from (the black box problem); this makes due diligence impossible on your part. You might accidentally commit plagiarism or copyright infringement by using LLM output in your code even if you modify it. +* You can't trace what content the LLM learned from (the black box problem); this makes due diligence impossible on your part. You might accidentally commit plagiarism or copyright infringement by using LLM output in your code even if you modify it. +* License conflicts can occur because of both items above. Read on... When licenses clash, it gets messy. Say your package uses an MIT license (common in scientific Python), but an LLM outputs Apache-2.0 or GPL code—those licenses aren't compatible. You can't just add attribution to fix it. Technically, you'd have to delete everything and rewrite it from scratch to comply with the licensing requirements. -The reality of all of this is that you can't eliminate this risk of license infringement or plagiarism with current LLM technology. But you can be more thoughtful about how you use the technology. +The reality of all of this is that you can't eliminate this risk of license infringement or plagiarism with current LLM technology. But you can be more thoughtful about how you use the technology. **What you can do now:** -*Prefer human-edited, transformative outputs you fully understand* - * Be aware that when you directly use content developed by an LLM, there will be inherent license conflicts. -* Be aware that LLM products can potentially return copyrighted code verbatim. **Don't paste LLM outputs directly into your code**. Instead, review, edit, and transform anything an LLM gives you. Consider using [clean-room techniques](https://en.wikipedia.org/wiki/Clean-room_design) to achieve this. -* **Make sure you fully understand the code before using it:** This is actually in your best interest because you can learn a lot about programming by asking an LLM questions and reviewing the output critically. +* Understand and transform code that is returned from a LLM: Don't paste LLM outputs directly. Review, edit, and ensure you fully understand what you're using. You can ask the LLM questions to better understand it's outputs. This approach also helps you learn which addresses the education concerns that we raised earlier. +* **Use LLMs as learning tools**: Ask questions, review outputs critically, then write your own implementation based on understanding. Often the outputs of LLMs are messy or inefficient. Use them to learn, not to copy. +* Consider [clean-room techniques](https://en.wikipedia.org/wiki/Clean-room_design): Have one person review LLM suggestions for approach; have another person implement from that high-level description +* **Document your process**: If you plan to submit a Python package for pyOpenSci review, we will ask you about your use of LLM's in your work. Document the use of LLMs in your project's README file and in any modules with LLM outputs have been applied. Confirm that it has been reviewed by a human prior to submitting it to us, or any other volunteer lead peer review process. You can't control what's in training data, but you can be thoughtful about how you use these tools. From 06881df5d54f7ee8f382234e73eb53771567d06b Mon Sep 17 00:00:00 2001 From: Leah Wasser Date: Wed, 19 Nov 2025 14:09:33 -0700 Subject: [PATCH 26/26] Apply suggestion from @lwasser --- _posts/2025-11-18-generative-ai-peer-review.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2025-11-18-generative-ai-peer-review.md b/_posts/2025-11-18-generative-ai-peer-review.md index 5262140b..17556a4c 100644 --- a/_posts/2025-11-18-generative-ai-peer-review.md +++ b/_posts/2025-11-18-generative-ai-peer-review.md @@ -15,7 +15,7 @@ comments: true last_modified: 2025-09-16 --- -authors: Leah Wasser, Jed Brown, Carter Rhea, Ellie Abrahams +authors: Leah Wasser, Jed Brown, Carter Rhea, Ellie Abrahams, Carol Willing ## Generative AI meets scientific open source