The Relationship Between Performance and Trust in AI in E-Finance.

artificial intelligence finance simulation hidden Markov model robo-advisor trust

Journal

Frontiers in artificial intelligence
ISSN: 2624-8212
Titre abrégé: Front Artif Intell
Pays: Switzerland
ID NLM: 101770551

Informations de publication

Date de publication:
2022
Historique:
received: 07 03 2022
accepted: 30 05 2022
entrez: 8 7 2022
pubmed: 9 7 2022
medline: 9 7 2022
Statut: epublish

Résumé

Artificial intelligence (AI) is fundamentally changing how people work in nearly every field, including online finance. However, our ability to interact with AI is moderated by factors such as performance, complexity, and trust. The work presented in this study analyzes the effect of performance on trust in a robo-advisor (AI which assists in managing investments) through an empirical investment simulation. Results show that for applications where humans and AI have comparable capabilities, the difference in performance (between the human and AI) is a moderate indicator of change in trust; however, human or AI performance individually were weak indicators. Additionally, results indicate that biases typically seen in human-human interactions may also occur in human-AI interactions when AI transparency is low.

Identifiants

pubmed: 35800065
doi: 10.3389/frai.2022.891529
pmc: PMC9253559
doi:

Types de publication

Journal Article

Langues

eng

Pagination

891529

Informations de copyright

Copyright © 2022 Maier, Menold and McComb.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Torsten Maier (T)

Department of Industrial and Manufacturing Engineering, Kettering University, Flint, MI, United States.

Jessica Menold (J)

School of Engineering Design, Technology, and Professional Programs, The Pennsylvania State University, University Park, State College, PA, United States.

Christopher McComb (C)

Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.

Classifications MeSH