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TRUVACE RECORD VERSION
record: TRV-2026-1727
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-17T18:13:39.729148Z
status: published
lens: p_space
sector: science
headline: Anticipating the unpredictable: A qualitative study on ethics experts' perspectives on review challenges related to AI
dek: Background Artificial intelligence (AI) is increasingly integrated into research across disciplines, raising various ethical concerns related to bias, privacy, transparency, and accountability. However, less is known about how these challenges are addressed within research ethics review practices. This paper examines how ethics experts perceive and manage AI-related challenges in ethics review. Methods Drawing on a qualitative study of 13 focus groups with 67 European ethics experts, we applied thematic analysis…
gain_title: (none)
problem_title: Ethics experts reported that AI is hard to define, evolves quickly, and raises ethical issues after initial review, so one-time ex-ante ethics review fits it poorly.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Ethics experts reported that AI is hard to define, evolves quickly, and raises ethical issues after initial review, so one-time ex-ante ethics review fits it poorly.
problem_evidence: AI challenges basic assumptions of ethics review | Ethical issues were seen as emerging across the research lifecycle, often after initial review
quick_read: A qualitative study drew on 13 focus groups with 67 European ethics experts to examine how AI-related challenges are handled in research ethics review. Using thematic analysis, the authors found that participants saw AI as difficult to define, fast-changing, and poorly suited to frameworks that assess risks only in advance.

The findings matter because ethics review systems may be built on assumptions that AI research does not meet, especially when ethical problems emerge after approval. The authors suggest iterative review, interdisciplinary expertise, and continuous ethical engagement, but the study reports expert perceptions and does not test whether these approaches work.
limitation: The evidence comes from a qualitative study of European ethics experts' perceptions, so it reflects their views and does not measure whether the proposed governance changes improve review outcomes.
tag: Evidence-backed problem
key_points: The study included 13 focus groups with 67 European ethics experts. | Participants described AI as hard to define and rapidly evolving. | Ethical issues were seen as arising throughout the research lifecycle, often after initial review. | The authors identify tension between compliance-focused rules and trust-based models built on researcher reflexivity.
rundown: The paper starts from the growing use of AI across research disciplines and the ethical concerns of bias, privacy, transparency, and accountability. It notes that little is known about how research ethics review practices address these concerns.

Thematic analysis of the focus group data identified tensions shaping current review processes. Experts said ex-ante regulatory frameworks do not readily accommodate AI, which creates a conflict between rule-based approaches focused on compliance and risk minimization and trust-based models emphasizing ongoing ethical judgment.

The authors conclude that AI exposes epistemic, temporal, and procedural limits in existing ethics review systems. They suggest more anticipatory and flexible governance, including iterative review mechanisms, interdisciplinary expertise, and continuous ethical engagement.
sources:
- peer_reviewed | Accountability in Research | https://doi.org/10.1080/08989621.2026.2744222 | 2026-10-08
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