The uncritical-acceptance failure that supervision must counter recurs across roles and domains: Tsamados et al. 2024's operators 'tend to over-trust and accept uncritically the outputs of AI systems,' Singh 2026's students are 'seduced by the fluency' of LLM outputs into weakened verification, and Humphreys et al. 2024 name 'overreliance and over-trust in generative AI' as ethical hazards of business adoption — the supervisee's trust psychology, not only system error, is the common supervisory problem.
All three quotes appear verbatim in E47, E20, and E55, and each cited source identifies human-side over-trust, seduction by fluency, or overreliance rather than system error, directly supporting the common trust-psychology framing.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 30 Oct, 04:57
Source chain
AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“Human operators tend to over-trust and accept uncritically the outputs of AI systems”
Full-text passage · no page number · evidence E47
Because of these changes, it is crucial to reconsider assumptions about human operators' role in supervisory control and the type of human–AI interactions that the literature should be designing for and optimising towards. The third limitation concerns the level of trust that a human operator has vis-à-vis the AI systems, and it can stem directly from the vicious cycle that the loss of situational awareness can trigger. Human operators tend to over-trust and accept uncritically the outputs of AI systems, which induces operational complacency that is counterproductive to the SHC rationale [34, 35]. The longer a given AI system has performed well, the more likely a human operator is to trust it and perceive some kind of “mechanistic objectivity” associated with computer-generated analytics, for example [2, p. 212], [36]. This can lead to over-trust dynamics, whereby the human agents ignore their experienced assessments—so-called automation bias [37]—or even “shirking part of their responsibility for decisions”, when this would contradict the behaviour of the AI system. The fourth limitation becomes evident when considering the specific types of risks that SHC aims to mitigate.
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.
- 02
“seduced by the fluency of large language models’ (LLMs) outputs”
Abstract · no page number · evidence E20
- 03
“overreliance and over-trust in generative AI”
Abstract · no page number · evidence E55