{"title":"Trusting under risk – comparing human to AI decision support agents","authors":"Hannah Fahnenstich , Tobias Rieger , Eileen Roesler","doi":"10.1016/j.chb.2023.108107","DOIUrl":null,"url":null,"abstract":"<div><p>The growing number of safety-critical technologized workplaces leads to enhanced support of complex human decision-making by artificial intelligence (AI), increasing the relevance of risk in the joint decision process. This online study examined participants' trust, attitude and behavior during a visual estimation task supported by either a human or an AI decision support agent. Throughout the online studyrisk levels were manipulated through different scenarios. Contrary to recent literature, no main effects were found in participants' trust attitude or trust behavior between support agent conditions or risk levels. However, participants using AI support exhibited increased trust behavior under higher risk, while participants with human support agents did not display behavioral differences. Self-confidence vs. trust and an increased feeling of responsibility may be possible reasons. Furthermore, participants reported the human support agent to be more responsible for possible negative outcomes of the joint task than the AI support agent. Hereby, risk did not influence perceived responsibility. However, the study's findings concerning trust behavior underscore the crucial importance of investigating the impact of risk in workplaces, shedding light on the under-researched effect of risk on trust attitude and behavior in AI-supported human decision-making.</p></div>","PeriodicalId":9,"journal":{"name":"ACS Catalysis ","volume":null,"pages":null},"PeriodicalIF":11.3000,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Catalysis ","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0747563223004582","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The growing number of safety-critical technologized workplaces leads to enhanced support of complex human decision-making by artificial intelligence (AI), increasing the relevance of risk in the joint decision process. This online study examined participants' trust, attitude and behavior during a visual estimation task supported by either a human or an AI decision support agent. Throughout the online studyrisk levels were manipulated through different scenarios. Contrary to recent literature, no main effects were found in participants' trust attitude or trust behavior between support agent conditions or risk levels. However, participants using AI support exhibited increased trust behavior under higher risk, while participants with human support agents did not display behavioral differences. Self-confidence vs. trust and an increased feeling of responsibility may be possible reasons. Furthermore, participants reported the human support agent to be more responsible for possible negative outcomes of the joint task than the AI support agent. Hereby, risk did not influence perceived responsibility. However, the study's findings concerning trust behavior underscore the crucial importance of investigating the impact of risk in workplaces, shedding light on the under-researched effect of risk on trust attitude and behavior in AI-supported human decision-making.
期刊介绍:
ACS Catalysis is an esteemed journal that publishes original research in the fields of heterogeneous catalysis, molecular catalysis, and biocatalysis. It offers broad coverage across diverse areas such as life sciences, organometallics and synthesis, photochemistry and electrochemistry, drug discovery and synthesis, materials science, environmental protection, polymer discovery and synthesis, and energy and fuels.
The scope of the journal is to showcase innovative work in various aspects of catalysis. This includes new reactions and novel synthetic approaches utilizing known catalysts, the discovery or modification of new catalysts, elucidation of catalytic mechanisms through cutting-edge investigations, practical enhancements of existing processes, as well as conceptual advances in the field. Contributions to ACS Catalysis can encompass both experimental and theoretical research focused on catalytic molecules, macromolecules, and materials that exhibit catalytic turnover.