What it is
People catch the errors they know how to see. The randomized study cited below found that people caught surface errors far more often than errors that needed conceptual judgment. How hard they checked, how much they trusted AI to begin with, and how visible the error was made the difference. Payment and time spent did not. Verification training teaches the harder half: tracing a claim back to its source and asking whether the reasoning holds.
What it pushes on in the Lab
In the Lab, this lever raises the checking and correcting that the people using the system can do. It also holds down operator deference drift, because people defer less to output they can check themselves.
You can also pull this lever at a strong tier, which costs more. At the strong tier, each effect below the Lab's strongest setting pushes harder.
In the modes that offer aiming, you can aim this lever at particular parts and pathways of a network. Otherwise it applies to the whole network.
Its pattern in the Practice Library
The Practice Library describes the pattern behind this lever:Verification training
The pressures it answers
No pressure page lists this lever among the levers that answer it.
Where you can pull it
No example network in the Lab offers this lever. Every network the Lab builds at random offers it.
The evidence behind its effects
The Lab cites these claims from the evidence registry for this lever's effects.
In a randomized study (N=2,784) with objective ground truth, humans accepted incorrect AI suggestions about a third of the time, and their rate of catching AI errors was governed by verification effort, prior trust in AI, and error legibility — surface errors were caught ~82% of the time versus ~31% for errors requiring conceptual judgment — not by financial incentives or time spent.[†]
Beck, J., Eckman, S., Kern, C., & Kreuter, F. (2026). Bias in the Loop: How Humans Evaluate AI-Generated Suggestions. Harvard Data Science Review, 8(2). https://hdsr.mitpress.mit.edu/pub/nrcn4h7d/release/1
https://hdsr.mitpress.mit.edu/pub/nrcn4h7d/release/1
Appears in: PAN framework development
Topics: algorithmic-fairness, human-ai-interaction
A validated collaborative-AI metacognition scale (planning, monitoring, evaluation of one's own reliance) predicted collaboration benefits incrementally beyond general metacognition — verification-skill training, not generic AI knowledge, is the calibrated counter to over-reliance.[2]
Sidra, S., & Mason, C. (2026). Generative AI in Human-AI Collaboration: Validation of the Collaborative AI Literacy and Collaborative AI Metacognition Scales for Effective Use. International Journal of Human–Computer Interaction, 42(7), 5084–5108. https://doi.org/10.1080/10447318.2025.2543997
doi.org/10.1080/10447318.2025.2543997
Appears in: Evidence reverification (2026); Paramerge authored research
Topics: ai-safety, human-ai-interaction
Bucinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), Article 188. https://doi.org/10.1145/3449287
Appears in: Evidence reverification (2026)
Topics: human-ai-interaction