{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T01:02:04Z","timestamp":1790989324586,"version":"4.1.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686110","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,22]]},"abstract":"<jats:p>This paper investigates the effectiveness of frequency-based electroencephalogram (EEG) measures in capturing self-reported trust and trust mis-calibration in artificial intelligence (AI) systems used as decision support tools. It examines a collaborative human-AI decision-making task where 50 human users interacted with an explainable speech-based AI system to detect deceptive speech. Correlation analysis indicates that Alpha and Theta power values of EEG measured from central, frontal, and parietal regions depict negative correlation with self-reported trust, and Delta power values from frontal regions are negatively correlated with self-reported trust. A machine learning model using EEG power measures to estimate self-reported trust depicted a Spearman\u2019s correlation value \u03c1 = 0.63 (p &lt;0.01) between predicted and actual self-reported trust. Additionally, the Beta-band-power values from left frontal and left central areas are higher during trust calibration compared to under-trust. The Gamma-band-power levels are also higher during trust-calibration compared to over-trust. Machine learning models based on these EEG measures predict different trust dimensions (i.e., over-trust, under-trust, trust-calibration) with moderate macro-F1 scores (i.e., 53-55%). Finally, the most effective trust assessment models leverage data from all considered brain regions \u2013 frontal, central, and parietal \u2013 achieving similar performance compared to models using central regions only and outperforming models using frontal or parietal regions alone.<\/jats:p>","DOI":"10.3233\/faia250631","type":"book-chapter","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T14:35:50Z","timestamp":1758638150000},"source":"Crossref","is-referenced-by-count":1,"title":["Detecting Trust Calibration Traits in AI with EEG Signals for Speech Deception"],"prefix":"10.3233","author":[{"given":"Abdullah Aman","family":"Tutul","sequence":"first","affiliation":[{"name":"Texas A&M University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Theodora","family":"Chaspari","sequence":"additional","affiliation":[{"name":"University of Colorado Boulder, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah Ita","family":"Levitan","sequence":"additional","affiliation":[{"name":"Hunter College, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"Hirschberg","sequence":"additional","affiliation":[{"name":"Columbia University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","HHAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250631","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T14:35:51Z","timestamp":1758638151000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250631"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,22]]},"ISBN":["9781643686110"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250631","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,22]]}}}