Evidence and limits
Finding: Automation bias appeared across settings, while its frequency and mitigation depended on task, user, workload, accountability, training, and interface design.
Impact: Strengthen verification and accountability design. Reject alerts, confidence displays, or explanations as universal mitigations.
Boundary: Much of the evidence concerns clinical or legacy decision support and reporting was heterogeneous. Next: which mitigations transfer to open-ended generative work without excessive burden?
Flags: healthcare-heavy and pre-LLM evidence
Record State: Catalog review recorded; not a performance endorsement
What was reviewed: Structure and relevance screened; not a full evaluation