{"title":"Human Centricity in the Relationship Between Explainability and Trust in AI","authors":"Zahra Atf;Peter R. Lewis","doi":"10.1109/MTS.2023.3340238","DOIUrl":null,"url":null,"abstract":"Artificial Intelligence (AI) is now applied in various contexts, from casual uses like entertainment and smart homes to critical decisions such as determining medical priorities, drug recommendations, humanitarian aid planning, satellite schedules, privacy, and detecting malicious software. There has been significant research into the societal impacts of algorithmic decision-making. For instance, studies on consumer preferences in user-centered explainable AI (XAI) found that AI is becoming an integral part of our daily experiences, with its influence expected to surge \n<xref>[1]</xref>\n. Researchers have also shed light on racial prejudices in algorithm-based bail decisions, probed the possibility of biases in AI-driven recruitment systems, and detected gender bias in online ads \n<xref>[2]</xref>\n.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10410142","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10410142/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial Intelligence (AI) is now applied in various contexts, from casual uses like entertainment and smart homes to critical decisions such as determining medical priorities, drug recommendations, humanitarian aid planning, satellite schedules, privacy, and detecting malicious software. There has been significant research into the societal impacts of algorithmic decision-making. For instance, studies on consumer preferences in user-centered explainable AI (XAI) found that AI is becoming an integral part of our daily experiences, with its influence expected to surge
[1]
. Researchers have also shed light on racial prejudices in algorithm-based bail decisions, probed the possibility of biases in AI-driven recruitment systems, and detected gender bias in online ads
[2]
.