@inproceedings{faa9d88db8a244f18215cb71540b2db7,
title = "Trust in AI-Enabled Decision Support Systems: Preliminary Validation of MAST Criteria",
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.",
keywords = "artificial intelligence, security, standards, trust",
author = "Chiou, {Erin K.} and Pouria Salehi and Erik Blasch and James Sung and Cohen, {Myke C.} and Anna Pan and Michelle Mancenido and Ahmadreza Mosallanezhad and Yang Ba and Shawaiz Bhatti",
note = "Funding Information: This material is based on work supported by the U.S. Department ofHomeland Security under GrantAward Number 17STQAC00001-05-00. The views and conclusions contained in this document are those of the authors and should notbe interpreted as representing the official policies, either expressed or implied, ofthe Department ofHomeland Security. Publisher Copyright: {\textcopyright} 2022 IEEE.; 3rd IEEE International Conference on Human-Machine Systems, ICHMS 2022 ; Conference date: 17-11-2022 Through 19-11-2022",
year = "2022",
doi = "10.1109/ICHMS56717.2022.9980623",
language = "English (US)",
series = "Proceedings of the 2022 IEEE International Conference on Human-Machine Systems, ICHMS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "David Kaber and Antonio Guerrieri and Giancarlo Fortino and Andreas Nurnberger",
booktitle = "Proceedings of the 2022 IEEE International Conference on Human-Machine Systems, ICHMS 2022",
}