Research Ethics Frameworks

AI isn’t assisting science anymore. It’s 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝗻𝗴 it. But what if the 𝗮𝘂𝘁𝗵𝗼𝗿 𝗵𝗮𝘀 𝗻𝗼 𝗰𝗼𝗻𝘀𝗰𝗶𝗲𝗻𝗰𝗲? 𝗜𝘁 𝗳𝗮𝗸𝗲𝘀 𝗰𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀. 𝗥𝗲𝘄𝗿𝗶𝘁𝗲𝘀 𝗳𝗶𝗻𝗱𝗶𝗻𝗴𝘀. 𝗗𝗿𝗮𝗳𝘁𝘀 𝗴𝗿𝗮𝗻𝘁𝘀. All before you blink. This isn’t progress. It’s precision without principle. Truth now comes 𝗽𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗲𝗱. And peer review can’t keep up. We’re not 𝘀𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝗻𝗶𝗻𝗴 𝘀𝗰𝗶𝗲𝗻𝗰𝗲. We’re 𝘀𝗵𝗼𝗿𝘁-𝗰𝗶𝗿𝗰𝘂𝗶𝘁𝗶𝗻𝗴 𝗶𝘁. And with no intervention, the tools don’t just drift, they 𝗱𝗶𝘀𝘁𝗼𝗿𝘁 𝘁𝗵𝗲 𝘃𝗲𝗿𝘆 𝗶𝗱𝗲𝗮 𝗼𝗳 𝘁𝗿𝘂𝘁𝗵. The European Commission’s whitepaper isn’t just regulation. It’s a firewall for scientific integrity. For those funding, governing, or scaling AI in research, it’s the baseline for trust, accountability, and future-proof discovery. It’s a must-read. And a call to act.....now. 🔸 Why These Guidelines Matter ➝ GenAI speeds discovery but magnifies risk. ➝ Disinformation and IP abuse are rising. ➝ Trust, transparency, and accountability are non-negotiable. 🔸 Guiding Principles ➝ Reliability: Keep research solid and reproducible. ➝ Honesty: Always disclose AI use. ➝ Respect: Protect data, people, and systems. ➝ Accountability: Humans remain responsible. 🔸 For Researchers ➝ Own every AI-supported output. ➝ Disclose tools used clearly. ➝ Don’t upload sensitive data. ➝ Cite properly. No plagiarism. ➝ Don’t use AI in reviews or evaluations. 🔸 For Research Organisations ➝ Train everyone across roles. ➝ Encourage disclosure without fear. ➝ Track how AI is used internally. ➝ Offer secure, local GenAI tools. ➝ Build this into your ethics policies. 🔸 For Funding Bodies ➝ Link funding to responsible AI use. ➝ Make disclosure a must. ➝ Ban AI in scientific reviews. ➝ Use GenAI responsibly in operations. ➝ Fund ethics training widely. 🔸Research Integrity ➝ Uphold ALLEA’s Code of Conduct: Quality Transparency Fairness Societal Responsibility 🔸Trustworthy AI Pillars ➝ Respect human autonomy ➝ Prevent harm ➝ Ensure fairness ➝ Prioritise explicability ➝ Ensure oversight, privacy, and transparency. 🔸 Evolving Together ➝ These guidelines will evolve. ➝ Updates will track tech and policy shifts. ➝ Community input is welcome. 🔸 Key Takeaways ➝ GenAI should support not steer research. ➝ Disclosure builds trust, not risk. ➝ Researchers, institutions, and funders must align. Bottom Line In research, credibility is everything. GenAI can support it but only when used with care, clarity, and conscience. Alex Wang Cobus Greyling Hr. Dr. Takahisa Karita Sarvex Jatasra Lewis Tunstall Martin Roberts, Michael Spencer Pascal BORNET Dr. Ram Kumar G, Ph.D, CISM, PMP Pavan Belagatti Rafah Knight JOY CASE Sara Simmonds Prasanna Lohar #AI #GenAI #AIinResearch #TrustworthyAI #EthicalAI #Research #Researchers 🔺 Looking to engage with insights that matter? 🔺 Follow Shalini Rao

🤔 Curious about using GenAI in your research but worried about crossing ethical lines? When do you need to disclose your use of these tools? Drawing on the European Code of Conduct for Research Integrity (ALLEA) principles, our latest preprint explores how GenAI tools might impact every stage of the research workflow, from initial proposal writing through to peer review. The team has a broad range of disciplines and some very differing views about GenAI, and we think this has been a key strength in helping us map out the key ethical challenges involved. We show: 📊 Detailed analysis of ethical issues across 8 distinct research phases 🚫 Critical evaluation of which tasks GenAI should (and shouldn't) be used for ✅ Evidence-based recommendations for maintaining research integrity while taking advantage of AI capabilities Many thanks to the whole team: Sonja Bjelobaba, Lorna Waddington,Tomas Foltynek, Sabuj Bhattacharyya and Debora Weber-Wulff for getting this out! #ResearchIntegrity #AcademicResearch #GenAI #ResearchEthics #HigherEd #ENAI

I deeply resonate with the critique of extractive research. Research has absolutely been complicit in empire, control, and the reproduction of power. That is not controversial. But I also think we need to be careful with how easily certain concepts become moral badges: - Participatory Action Research. - Decolonial methodologies. - Community-led processes. On paper, they read beautifully. In practice? It’s more complicated. - Who exactly is “the community” in participatory research? - Who gets invited to participate? - Who speaks? - Who decides what counts as knowledge? - And who is quietly excluded? In a recent evaluation I’ve been conducting, several projects proudly described themselves as participatory and decolonial. Yet within these “community-led” spaces, certain groups—particularly LGBT youth—were marginalised, silenced, or framed as disruptive to the collective. So the question is not only whether research is extractive. The question is also whether participation itself can reproduce exclusion (which obviously does, by the way). Communities are not inherently egalitarian. They have hierarchies, moral orders, internal power struggles. Sometimes “community ownership” means reinforcing dominant voices within that community. Decolonial language does not automatically dismantle patriarchy. Participatory design does not automatically redistribute power. And stepping back is not always the same as transforming structures. If we are serious about ethical research, then we need to move beyond slogans and into uncomfortable reflection: • Who benefits from participation? • Who pays the cost of inclusion? • What happens when “community values” clash with individual rights? • Are we willing to challenge exclusion even when it comes from within marginalised groups? Critical research is necessary. But so is critical reflection about our own methodological vocabularies. Otherwise, “decolonial” becomes a fancy brand to win proposals. “Participatory” becomes a checkbox. And power quietly rearranges itself while we congratulate ourselves for being reflexive. Ethics is about interrogating power everywhere it shows up—including inside the communities we romanticise. #ResearchEthics #DecolonizingResearch #ParticipatoryResearch #CriticalMethodology #KnowledgeProduction #PowerAndPolitics #GlobalDevelopment #SocialImpact #EquityInResearch #InclusionMatters #HumanRights #Academia #PhDLife #ImpactEvaluation #EvidenceBased

📈 📲 The rapid growth of wearable and app derived health data has outpaced our consent infrastructure. A new paper offers one of the clearest attempts to close that gap. A perspective from Stefanie Brückner, Stephen Gilbert, & colleagues, presents a thoughtful framework for responsible use of health app and wearable data in research. As funders and regulators expect stronger transparency and participant centered governance, models like this will be important for future approval pathways and for the long term sustainability of digital research. Many EU-based efforts related to electronic health records are moving toward opt out structures for secondary use. This may work for clinical data collected inside health systems but is not appropriate for data generated through wearables and consumer apps. #PGHD are created voluntarily, outside clinical care, and often on self purchased devices. For this category, the European Data Protection Board has argued that explicit and informed consent is necessary. The framework proposed here is designed for that need. The authors introduce a user driven consent platform that gives individuals a consistent way to decide how their data are shared across apps, clinical systems, and research. As patient generated data become central to public health, clinical trials, and population research globally, this work addresses a foundational gap. Key themes: 🔐 Granular and revocable consent Participants can specify which types of data can be used for personal care or research, update preferences at any time, and rely on pseudonymized identifiers. 📑 Alignment with governance structures Standardized, informed, and revocable consent supports the General Data Protection Regulation and the emerging European Health Data Space, and it provides the clarity global regulators seek in real world evidence. 🔗 Interoperability The platform uses HL7 FHIR and open identity standards, enabling integration with electronic health records and digital health services. This supports international research and ethical data sharing. 🤝 A stronger foundation for trust Transparent governance and clear communication are essential for long term engagement and for high quality datasets. Open Access Paper 🔗 https://www.nature.com/articles/s41746-025-02147-3 At GSD Health Research we are building large scale cohort studies that rely on participant generated data including wearable streams and patient reported outcomes. Our work depends on trust and clarity. This perspective illustrates how consent infrastructure can support ethical real world evidence and accelerate discovery in ways that respect the people who make research possible. Thank you to the full author team for a timely contribution. #digitalhealth #clinicalresearch #realworlddata #datagovernance #PGHD

Our latest IEEE TTS paper is by Staff Research Scientist Kevin McKee at Google DeepMind, who is focused on developing #cooperative and #inclusiveAI. If you are engaged in the secondary use of #data (e.g., #health/ #financial/ #taxpayer/ #education), or are dealing with data that has been provided by a #humansubject, and working in the #AI / #ML space, then this is a must-read article. ➡ https://lnkd.in/gFi6sniw The claim is made by the author that fewer than 1 out of 4 #AAAI and #NeurIPS papers confirm independent ethical review, the collection of informed consent, or participant compensation. We might then pose the question: what's gone wrong in the process of publication at these conferences, if anything at all? From where does the problem stem? What can be done to curb the rampant use of primary data without the consent of the participant? Excerpt: In recent years, research involving human participants has been critical to advances in AI and ML, particularly in the areas of conversational, human-compatible, and cooperative AI. For example, roughly 9% of publications at recent AAAI and NeurIPS conferences indicate the collection of original human data. Yet AI and ML researchers lack guidelines for ethical research practices with human participants. Fewer than one out of every four of these AAAI and NeurIPS papers confirm independent ethical review, the collection of informed consent, or participant compensation. This paper aims to bridge this gap by examining the normative similarities and differences between AI research and related fields that involve human participants. Though psychology, human-computer interaction, and other adjacent fields offer historic lessons and helpful insights, AI research presents several distinct considerations—namely, participatory design, crowdsourced dataset development, and an expansive role of corporations—that necessitate a contextual ethics framework. Citation: K. R. McKee, "Human Participants in AI Research: Ethics and Transparency in Practice," in IEEE Transactions on Technology and Society, vol. 5, no. 3, pp. 279-288, Sept. 2024, doi: 10.1109/TTS.2024.3446183. Keywords: #Ethics #Psychology #Guidelines #Companies Rob Nicholls George Roussos Rafael A. Calvo Jason Sargent Ehsan Nabavi Suchit Ahuja Dr Greg Adamson Ruth Lewis Peter Lewis Peter Asaro Evan Selinger Julia Powles Michael Zimmer Constance de Saint Laurent Edgar Whitley Professor Carolyn McGregor AM 💡 Rebecca Herold Deborah Lupton Sonia Livingstone Carole McCartney Susan Halford FAcSS, FRSA Michael King Grace McCarthy Lucy Batley Dr Genevieve Smith-Nunes Bozenna Pasik-Duncan Lyria Bennett Moses Dilan Thampapillai Andrew Whelan Savvas Papagiannidis Dinara Davlembayeva Ilias Pappas Marijn Janssen Efpraxia Zamani Anastasia Griva Patrick Mikalef

Using AI in Research? Transparency Isn’t Optional. As more researchers integrate AI tools for transcription, coding, or analysis, we’re also seeing a rise in participant concerns — and, increasingly, refusals — based on misconceptions about what AI actually does with their data. And honestly? Those concerns are valid. AI introduces new questions about privacy, data flow, and security. Participants deserve clarity, not jargon. Here’s the approach I’ve been championing, grounded in the STRESS Framework™ (Sensitivity, Transparency, Responsibility, Ethics, Skepticism, Security): 🔍 Be transparent: Tell participants when AI is used, what it does and doesn’t do, and how long data is stored. 🛡️ Prioritize security: Use vetted tools, encryption, and clear deletion timelines. 🧭 Stay ethical: Participation should always be voluntary — misconceptions are an opportunity to clarify, not persuade. 🤝 Build trust: Explain that AI assists with tasks like transcription, but human researchers still verify and interpret everything. 📄 Document responsibly: Keep clear records of how AI is used, how decisions are made, and how risks are mitigated. When participants understand the process, they’re more empowered — and our research becomes more ethical, transparent, and trustworthy. If you're looking to strengthen your own AI-use statements, consent materials, or research protocols, the STRESS Framework Assistant is an excellent tool to help you structure responsible AI documentation: 👉 https://lnkd.in/esFZEx34

🔬 New NSF-funded guidelines for responsible AI use in STEM education research are out (Smith & McGill, 2026) with a little help from our friends! iacomputinged.org/graiser Fellow UIC colleague 🎓 Jeremy Riel was also at the table, great to see University of Illinois Chicago institution represented in this work. Thanks Monica McGill, Dr. Julie M. Smith & Institute for Advancing Computing Education! The Guidelines for the Responsible Use of AI in STEM Education Research (National Science Foundation (NSF) Award No. 2519885) is one of the most actionable frameworks I've seen for researchers navigating AI right now. Five priorities stood out to me: 🧠 Human critique first. AI is assistive, researchers must have the expertise to evaluate what it produces, not just accept it. 🤔 Decide holistically. Ask whether AI is actually the best option for each task, not just the fastest. 🔒 Protect participant data. Re-identification risks are growing as AI gets more powerful. Rethink how you share datasets. 📋 Document AI use. Keep a record of what tools you used, when, and how — throughout the full research process. 📣 Disclose AI use. In presentations and publications. Every time. The modified CRediT statement approach in this report is worth adopting. These aren't abstract ethics principles — they're operational decisions that belong in your research workflow before you open ChatGPT. Full report is open access. Worth sharing with your research teams. https://lnkd.in/gS7vhc3V #AIinEducation #STEMEducationResearch #ResponsibleAI #HigherEducation #NSF #EdResearch Great to work with you Justin Reich, Lisa Bosman, PhD, Aman Yadav, Megan Stubbs-Richardson and others!

The National Institute of Standards and Technology (NIST) team have spent over 12 months exploring how the key ethical research principles for biomedical and behavioral research with human subjects in the United States can be integrated into AI research. One being that by obtaining informed consent from research participants and designing studies to minimize risks they can ensure transparency and protect individuals' data. Additionally, selecting subjects fairly and avoiding inappropriate exclusion can help address biases in AI datasets. It is important to note that the authors of this document emphasize thoughtfulness rather than advocating for more government regulation. By adopting these ethical principles voluntarily, companies can demonstrate their commitment to responsible AI development and usage. You can read this fascinating report here: https://lnkd.in/d7-t5e8d

Highly Recommended Great Workbook by The Alan Turing Institute "AI Ethics and Governance in Practice: An Introduction" AI systems may have transformative and long-term effects on individuals and society. To manage these impacts responsibly and direct the development of AI systems toward optimal public benefit, considerations of AI ethics and governance must be a first priority. In this workbook, we introduce fundamental concepts of AI, responsible research and innovation, and AI ethics and governance, such as the SSAFE-D Principles – which stands for Sustainability, Safety, Accountability, Fairness, Explainability, and Data-Stewardship. The SSAFE-D Principles are a set of ethical principles that serve as starting points for reflection and deliberation about possible harms and benefits associated with data-driven technologies. We then go on to describe the PBG Framework, a multi-tiered governance model that enables project teams to integrate ethical values and practical principles into their innovation practices and to have clear mechanisms for demonstrating and documenting this. #AI #aiethics #governance #safety

Please do NOT start research on human subjects unless you have taken into account the ethics part. I beg you, please! 😂 I've encountered multiple cases of my mentees who started a project without the necessary approvals, and when it came to journal publication, they were stuck! Let's see what we need to get started 👇 1. Informed consent Ensures participants fully understand the research, its potential risks and benefits, and their right to withdraw without consequence (you must include this in your submission!) 2. Privacy and confidentiality Safeguarding participant data, including anonymization, encryption, and secure storage (you'll have to describe this in your method section.) 3. Vulnerable populations If research involves children, the elderly, prisoners, or those with cognitive impairments, additional measures protect their rights and well-being. 4. Benefit-risk assessment Potential benefits or risks to participants considering not only physical harm but also psychological and social impacts. 5. Data integrity and transparency Accurate data collection, analysis, and reporting. 6. Researcher bias and conflicts of interest Addressing personal biases and financial conflicts and transparent disclosure and mitigation strategies. 7. Cultural sensitivity Respecting diverse cultural values and beliefs AND, here comes the tough one 👇 8. Institutional review board (IRB) approval An approval letter generated by an IRB is compulsory for every single submission that involves research on human subjects. ___________________ 🔔 This is Dr. Samira Hosseini. Scholars who took my training published +2,000 articles in top-tier journals. Join my inner circle not to miss even one single bit of learning: https://lnkd.in/eVNSihCM