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Cited by 34

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

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

Maier, Torsten, Menold, Jessica, McComb, Christopher·2022·Frontiers in Artificial Intelligence
DOI 10.3389/frai.2022.891529
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Abstract

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.

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Cited by

Metadata

Authors
Maier, T., Menold, J., & McComb, C.
Venue
Frontiers in Artificial Intelligence
Publisher
Frontiers Media SA
Published
2022-06-21
Volume
5
ISSN
2624-8212
DOI
10.3389/frai.2022.891529
Cited by
34
References
11
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.