Supporting Trust Calibration and the Effective Use of Decision Aids by Presenting Dynamic System Confidence Information

The introduction of automated decision aids in high-risk, complex domains such as aviation often leads to performance breakdowns that are related to automation bias and trust miscalibration. This can be partially explained by the fact that operators are informed only about overall system reliability, which makes it difficult for them to decide on a case-by-case basis whether to follow the system's advice. This study examines whether continually updated information about a system's confidence in its ability to perform assigned tasks improves operators' trust calibration in, and use of, an automated decision support system. The particular application for the current experiment was a neural net-based decision aid that assists pilots with detecting and handling in-flight icing encounters. A multifactorial experiment was carried out with two groups of 15 instructor pilots each flying a series of 28 approaches in a motion-base simulator. One group was informed about the system's overall reliability only, while the other group received updated system confidence information. Results showed that pilots in the updated group experienced significantly fewer icing-related stalls and were more likely to reverse their initial response to an icing condition when it did not produce desired results. Their estimate of the system's accuracy also was more precise than that of the fixed group. These findings indicate that the presentation of continually updated system confidence information can improve trust calibration and performance.

  • Availability:
  • Authors:
    • McGuirl, John M
    • Sarter, Nadine B
  • Publication Date: 2006


  • English

Media Info

  • Media Type: Print
  • Features: Appendices; Figures; References; Tables;
  • Pagination: pp 656-665
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01042351
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 11 2007 10:59PM