{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T14:28:29Z","timestamp":1783434509564,"version":"3.54.6"},"reference-count":88,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:p>Transparency is widely regarded as crucial for the responsible real-world deployment of artificial intelligence (AI) and is considered an essential prerequisite to establishing trust in AI. There are several approaches to enabling transparency, with one promising attempt being human-centered explanations. However, there is little research into the effectiveness of human-centered explanations on end-users' trust. What complicates the comparison of existing empirical work is that trust is measured in different ways. Some researchers measure subjective trust using questionnaires, while others measure objective trust-related behavior such as reliance. To bridge these gaps, we investigated the effects of two promising human-centered <jats:italic>post-hoc<\/jats:italic> explanations, <jats:italic>feature importance<\/jats:italic> and <jats:italic>counterfactuals<\/jats:italic>, on trust <jats:italic>and<\/jats:italic> reliance. We compared these two explanations with a control condition in a decision-making experiment (<jats:italic>N<\/jats:italic> = 380). Results showed that human-centered explanations can significantly increase reliance but the type of decision-making (increasing a price vs. decreasing a price) had an even greater influence. This challenges the presumed importance of transparency over other factors in human decision-making involving AI, such as potential heuristics and biases. We conclude that trust does not necessarily equate to reliance and emphasize the importance of appropriate, validated, and agreed-upon metrics to design and evaluate human-centered AI.<\/jats:p>","DOI":"10.3389\/fcomp.2023.1151150","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T08:26:51Z","timestamp":1689582411000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["Exploring the effects of human-centered AI explanations on trust and reliance"],"prefix":"10.3389","volume":"5","author":[{"given":"Nicolas","family":"Scharowski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian A. 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