Human‑in‑the‑Loop Auditing: Structured Interaction Patterns for Trust Calibration in High‑Stakes AI

August 2, 2026Open Access

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Key Points

This research aims to explore how structured interaction patterns can improve trust calibration in high-stakes AI environments.
Implemented structured interaction patterns in human-in-the-loop auditing processes for AI systems.
Evaluated participant responses to AI decisions in high-stakes scenarios.
Participants using structured interaction patterns demonstrated a significant increase in trust levels compared to those without (p<0.05).
Trust calibration showed a notable improvement, with an effect size of 0.65 (95% CI: 0.50 to 0.85).
High-stakes decision-making accuracy improved by 20% in the structured interaction group.

Investigates structured interaction patterns for trust calibration in high-stakes AI, suggesting improved reliability.

Research article: Human‑in‑the‑Loop Auditing: Structured Interaction Patterns for Trust Calibration in High‑Stakes AI

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Ивченко et al. (2026) studied this question.

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