서브메뉴
검색
Factors Affecting Appropriate Reliance on Artificial Intelligence Decision Support Systems
Factors Affecting Appropriate Reliance on Artificial Intelligence Decision Support Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152707
- ISBN
- 9798383698587
- DDC
- 620
- 서명/저자
- Factors Affecting Appropriate Reliance on Artificial Intelligence Decision Support Systems
- 발행사항
- [Sl] : Carnegie Mellon University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 305 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: A.
- 주기사항
- Advisor: Fischhoff, Baruch.
- 학위논문주기
- Thesis (Ph.D.)--Carnegie Mellon University, 2024.
- 초록/해제
- 요약Many applications of AI require humans and AI advisors to make decisions collaboratively; however, success depends on how appropriately humans rely on the AI agent. We demonstrated an evaluation method for a platform that used neural network agents of varying skill levels for the simple strategic game of Connect Four. We manipulated the presence, sequence, skill, and information display of Artificial Intelligence (AI) advice in a strategy game against another AI opponent that sometimes varied its skill to measure their effect on users' performance.Human agent teams outperformed unaided subjects with those receiving the AI recommendations simultaneously achieving the best results. Although team performance was higher and subjects improved during game play, there was little evidence of learning from their AI advisors. AI reliability proved to be the greatest determiner of team performance with subjects retaining trust in higher skilled advisors even in varied environments. Those with higher numeracy demonstrated the highest ability to make use of AI advice including more detailed output formats including ranking of choices and probabilities. More reliable AI agents correlated to higher AI trust while higher self-confidence correlated to greater rejection of AI advice, greater confidence in success, but slightly lower performance.The value of these human agent teams depended on AI reliability, users' ability to extract lessons from their advice, and users' trust in that advice. Organizations implementing human agent teams should conduct testing to know how well users appropriately rely on AI recommendations.
- 일반주제명
- Engineering
- 일반주제명
- Public policy
- 키워드
- Decision science
- 키워드
- Trust
- 기타저자
- Carnegie Mellon University Engineering and Public Policy
- 기본자료저록
- Dissertations Abstracts International. 86-02A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017163433
■00520250211152707
■006m o d
■007cr#unu||||||||
■020 ▼a9798383698587
■035 ▼a(MiAaPQ)AAI31488433
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a620
■1001 ▼aDunning, Richard E.▼0(orcid)0009-0005-2790-6499
■24510▼aFactors Affecting Appropriate Reliance on Artificial Intelligence Decision Support Systems
■260 ▼a[Sl]▼bCarnegie Mellon University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a305 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: A.
■500 ▼aAdvisor: Fischhoff, Baruch.
■5021 ▼aThesis (Ph.D.)--Carnegie Mellon University, 2024.
■520 ▼aMany applications of AI require humans and AI advisors to make decisions collaboratively; however, success depends on how appropriately humans rely on the AI agent. We demonstrated an evaluation method for a platform that used neural network agents of varying skill levels for the simple strategic game of Connect Four. We manipulated the presence, sequence, skill, and information display of Artificial Intelligence (AI) advice in a strategy game against another AI opponent that sometimes varied its skill to measure their effect on users' performance.Human agent teams outperformed unaided subjects with those receiving the AI recommendations simultaneously achieving the best results. Although team performance was higher and subjects improved during game play, there was little evidence of learning from their AI advisors. AI reliability proved to be the greatest determiner of team performance with subjects retaining trust in higher skilled advisors even in varied environments. Those with higher numeracy demonstrated the highest ability to make use of AI advice including more detailed output formats including ranking of choices and probabilities. More reliable AI agents correlated to higher AI trust while higher self-confidence correlated to greater rejection of AI advice, greater confidence in success, but slightly lower performance.The value of these human agent teams depended on AI reliability, users' ability to extract lessons from their advice, and users' trust in that advice. Organizations implementing human agent teams should conduct testing to know how well users appropriately rely on AI recommendations.
■590 ▼aSchool code: 0041.
■650 4▼aEngineering
■650 4▼aPublic policy
■653 ▼aAppropriate reliance
■653 ▼aDecision science
■653 ▼aHuman agent teams
■653 ▼aTrust
■690 ▼a0800
■690 ▼a0630
■690 ▼a0537
■71020▼aCarnegie Mellon University▼bEngineering and Public Policy.
■7730 ▼tDissertations Abstracts International▼g86-02A.
■790 ▼a0041
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163433▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
