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https://doi.org/10.21256/zhaw-28966| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Certification labels for trustworthy AI : insights from an empirical mixed-method study |
| Authors: | Scharowski, Nicolas Benk, Michaela Kühne, Swen J. Wettstein, Léane Brühlmann, Florian |
| et. al: | No |
| DOI: | 10.1145/3593013.3593994 10.21256/zhaw-28966 |
| Proceedings: | Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency |
| Page(s): | 248 |
| Pages to: | 260 |
| Conference details: | 6th ACM Conference on Fairness, Accountability, and Transparency (FAccT), Chicago, USA, 12-15 June 2023 |
| Issue Date: | 15-May-2023 |
| Publisher / Ed. Institution: | Association for Computing Machinery |
| ISBN: | 979-8-4007-0192-4 |
| Language: | English |
| Subjects: | Computers and society; Computer science; Artificial intelligence; Audit; Documentation; Label; Seal; Certification; Trust; User study |
| Subject (DDC): | 006: Special computer methods 150: Psychology |
| Abstract: | Auditing plays a pivotal role in the development of trustworthy AI. However, current research primarily focuses on creating auditable AI documentation, which is intended for regulators and experts rather than end-users affected by AI decisions. How to communicate to members of the public that an AI has been audited and considered trustworthy remains an open challenge. This study empirically investigated certification labels as a promising solution. Through interviews (N = 12) and a census-representative survey (N = 302), we investigated end-users' attitudes toward certification labels and their effectiveness in communicating trustworthiness in low- and high-stakes AI scenarios. Based on the survey results, we demonstrate that labels can significantly increase end-users' trust and willingness to use AI in both low- and high-stakes scenarios. However, end-users' preferences for certification labels and their effect on trust and willingness to use AI were more pronounced in high-stake scenarios. Qualitative content analysis of the interviews revealed opportunities and limitations of certification labels, as well as facilitators and inhibitors for the effective use of labels in the context of AI. For example, while certification labels can mitigate data-related concerns expressed by end-users (e.g., privacy and data protection), other concerns (e.g., model performance) are more challenging to address. Our study provides valuable insights and recommendations for designing and implementing certification labels as a promising constituent within the trustworthy AI ecosystem. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/28966 |
| Related research data: | https://osf.io/gzp5k/ |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | Applied Psychology |
| Organisational Unit: | Psychological Institute (PI) |
| Appears in collections: | Publikationen Angewandte Psychologie |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Scharowski-etal_Certification-labels-for-trustworthy-AI.pdf | 1.15 MB | Adobe PDF |  View/Open |
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Scharowski, N., Benk, M., Kühne, S. J., Wettstein, L., & Brühlmann, F. (2023). Certification labels for trustworthy AI : insights from an empirical mixed-method study [Conference paper]. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 248–260. https://doi.org/10.1145/3593013.3593994
Scharowski, N. et al. (2023) ‘Certification labels for trustworthy AI : insights from an empirical mixed-method study’, in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, pp. 248–260. Available at: https://doi.org/10.1145/3593013.3593994.
N. Scharowski, M. Benk, S. J. Kühne, L. Wettstein, and F. Brühlmann, “Certification labels for trustworthy AI : insights from an empirical mixed-method study,” in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, May 2023, pp. 248–260. doi: 10.1145/3593013.3593994.
SCHAROWSKI, Nicolas, Michaela BENK, Swen J. KÜHNE, Léane WETTSTEIN und Florian BRÜHLMANN, 2023. Certification labels for trustworthy AI : insights from an empirical mixed-method study. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. Conference paper. Association for Computing Machinery. 15 Mai 2023. S. 248–260. ISBN 979-8-4007-0192-4
Scharowski, Nicolas, Michaela Benk, Swen J. Kühne, Léane Wettstein, and Florian Brühlmann. 2023. “Certification Labels for Trustworthy AI : Insights from an Empirical Mixed-Method Study.” Conference paper. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 248–60. Association for Computing Machinery. https://doi.org/10.1145/3593013.3593994.
Scharowski, Nicolas, et al. “Certification Labels for Trustworthy AI : Insights from an Empirical Mixed-Method Study.” Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, Association for Computing Machinery, 2023, pp. 248–60, https://doi.org/10.1145/3593013.3593994.
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