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October 20, 2025Open Access

Dynamic Trust Calibration Using Contextual Bandits

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Authors

BHBruno Miranda HenriqueESEugene Santos

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Overview

New method improves trust calibration in AI systems, enhancing decision-making performance in various contexts.

Key Points

  • Dynamic trust calibration enhances decision-making performance by 10 to 38%, indicating a significant improvement.
  • The study reveals a standardized trust calibration measure that uses contextual bandits for adaptive decision-making.
  • Utilizing diverse datasets, the research demonstrates practical applications for improving AI trust in critical domains.
  • Effective trust calibration is crucial for balancing user reliance on AI outputs and mitigating risk of oversight.

Cite This Study

Henrique et al. (2025) studied this question.

synapsesocial.com/papers/68f6379bb481a140a36cf46ehttps://doi.org/10.48550/arxiv.2509.23497
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Dynamic calibration of trust and trustworthiness in AI-enabled systems2026
  2. 2Building Appropriate Trust in Artificial Intelligence: Examining a Dynamic Trust Calibration Model and an Intervention2026
  3. 3Measuring Trust Dynamics in AI-Assisted Decision-Making: Insights from an Experimental Study2025
  4. 4Calibrated adaptive framework for trustworthy human and artificial intelligence decision systems2026
  5. 5How Cognitive Load Affects Dynamic Trust Calibration in Human–AI Collaboration: Evidence for Selective Pathway Effects2026