Trust in AI-Enabled Decision Support Systems: Preliminary Validation of MAST Criteria

Erin K. Chiou, Pouria Salehi, Erik Blasch, James Sung, Myke C. Cohen, Anna Pan, Michelle Mancenido, Ahmadreza Mosallanezhad, Yang Ba, Shawaiz Bhatti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

This study tests the Multisource AI Scorecard Table for evaluating the trustworthiness of AI-enabled decision support systems. Forty participants reviewed one of four system descriptions, either a low MAST rating or a high MAST rating, and either a face-matching system or a text summarization system. Participants rated the system and completed trust and credibility questionnaires. Results show that MAST items are correlated with validated trust and credibility items, suggesting that the tool can be useful, but more so for text summarization based systems.

Original languageEnglish (US)
Title of host publicationProceedings of the 2022 IEEE International Conference on Human-Machine Systems, ICHMS 2022
EditorsDavid Kaber, Antonio Guerrieri, Giancarlo Fortino, Andreas Nurnberger
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665452380
DOIs
StatePublished - 2022
Externally publishedYes
Event3rd IEEE International Conference on Human-Machine Systems, ICHMS 2022 -
Duration: Jan 1 2022 → …

Publication series

NameProceedings of the 2022 IEEE International Conference on Human-Machine Systems, ICHMS 2022

Conference

Conference3rd IEEE International Conference on Human-Machine Systems, ICHMS 2022
Period1/1/22 → …

Keywords

  • artificial intelligence
  • security
  • standards
  • trust

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Media Technology
  • Control and Optimization

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