Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics

• URL: http://arxiv.org/abs/2608.05656v2
• Date: Sat, 08 Aug 2026 01:51:16 GMT
• Title: Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics
• Abstract summary: Safety risks of AI are becoming increasingly evident in human interactions with AI technologies.<n>To examine this apparent gap in the acceptance of human research, we conduct an expert survey (n=93) and expert interviews (n=17) with AI Safety & Ethics (AISE) researchers.
• Score: 25.781418598536906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safety risks of AI are becoming increasingly evident in human interactions with AI technologies. The prominent approaches to evaluating these risks favor technical methods, such as model benchmarks and LLM simulations, often sidelining empirical research with human subjects. To examine this apparent gap in the acceptance of human research, we conduct an expert survey (n=93) and expert interviews (n=17) with AI Safety & Ethics (AISE) researchers from Technical, Sociotechnical, Governance, and Normative backgrounds. Our findings suggest that although there is a consensus that human research is valuable for generating evidence for AISE, its adoption and acceptance are constrained by perceived validity issues, tangible resource barriers, epistemic and personal preferences in methods, and infrastructural constraints from the broader research community. In particular, Technical researchers tend to value human research less and collaborate across disciplines less, suggesting an epistemic tension towards human methods. We propose recommendations for establishing the epistemic fit of human research within AISE and bridging the prohibitive limitations that researchers face, while avoiding performative 'human-washing'.

Related papers

• RCTs & Human Uplift Studies: Methodological Challenges and Practical Solutions for Frontier AI Evaluation [7.662976269800995]
  Human uplift studies measure AI effects on human performance relative to a status quo.<n>These studies are increasingly used to inform deployment, governance, and safety decisions for frontier AI systems.<n>While the methods underlying these studies are well-established, their interaction with the distinctive properties of frontier AI systems remains underexamined.
  arXiv  Detail & Related papers  (2026-03-11T17:25:03Z)
• Report on the Scoping Workshop on AI in Science Education Research 2025 [2.0613397238713778]
  Report summarizes the outcomes of a two-day international scoping workshop on the role of artificial intelligence (AI) in science education research.<n>As AI rapidly reshapes scientific practice, classroom learning, and research methods, the field faces both new opportunities and significant challenges.<n>Report concludes with actionable recommendations for training, infrastructure, and standards, along with guidance for funders, policymakers, professional organizations, and academic departments.
  arXiv  Detail & Related papers  (2025-11-18T10:23:22Z)
• Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
  We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment.<n>We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
  arXiv  Detail & Related papers  (2025-04-01T13:53:11Z)
• On Benchmarking Human-Like Intelligence in Machines [77.55118048492021]
  We argue that current AI evaluation paradigms are insufficient for assessing human-like cognitive capabilities.<n>We identify a set of key shortcomings: a lack of human-validated labels, inadequate representation of human response variability and uncertainty, and reliance on simplified and ecologically-invalid tasks.
  arXiv  Detail & Related papers  (2025-02-27T20:21:36Z)
• AI-Empowered Human Research Integrating Brain Science and Social Sciences Insights [1.7146585621340318]
  We argue that it is high time for researchers to transition to human-AI joint research. We introduce three human-AI collaboration models: AI as a research tool (ART), AI as a research assistant (ARA), and AI as a research participant (ARP)
  arXiv  Detail & Related papers  (2024-11-16T11:13:23Z)
• An evidence-based methodology for human rights impact assessment (HRIA) in the development of AI data-intensive systems [49.1574468325115]
  We show that human rights already underpin the decisions in the field of data use. This work presents a methodology and a model for a Human Rights Impact Assessment (HRIA) The proposed methodology is tested in concrete case-studies to prove its feasibility and effectiveness.
  arXiv  Detail & Related papers  (2024-07-30T16:27:52Z)
• Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy [65.77763092833348]
  This perspective examines vulnerabilities in AI scientists, shedding light on potential risks associated with their misuse.<n>We take into account user intent, the specific scientific domain, and their potential impact on the external environment.<n>We propose a triadic framework involving human regulation, agent alignment, and an understanding of environmental feedback.
  arXiv  Detail & Related papers  (2024-02-06T18:54:07Z)
• Control Risk for Potential Misuse of Artificial Intelligence in Science [85.91232985405554]
  We aim to raise awareness of the dangers of AI misuse in science. We highlight real-world examples of misuse in chemical science. We propose a system called SciGuard to control misuse risks for AI models in science.
  arXiv  Detail & Related papers  (2023-12-11T18:50:57Z)
• Human-Centered Responsible Artificial Intelligence: Current & Future Trends [76.94037394832931]
  In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. All of this work is aimed at developing AI that benefits humanity while being grounded in human rights and ethics, and reducing the potential harms of AI. In this special interest group, we aim to bring together researchers from academia and industry interested in these topics to map current and future research trends.
  arXiv  Detail & Related papers  (2023-02-16T08:59:42Z)
• Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies [22.214709837295906]
  There is growing interest in the research community to augment human decision making with AI assistance. The emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI.
  arXiv  Detail & Related papers  (2021-12-21T19:00:02Z)
• Human Factors in Security Research: Lessons Learned from 2008-2018 [8.255966566768484]
  We focus our analysis on the research on the crucial population of experts, whose human errors can impact many systems at once. We analyzed the past decade of human factors research in security and privacy, identifying 557 relevant publications.
  arXiv  Detail & Related papers  (2021-03-24T15:58:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.