Certification Labels for Trustworthy AI: Insights From an
Empirical Mixed-Method Study
Nicolas Scharowski

nicolas.scharowski@unibas.ch
University of Basel

Michaela Benk

mbenk@ethz.ch
Mobiliar Lab for Analytics
ETH Zürich

Léane Wettstein

Swen J. Kühne

swen.kuehne@zhaw.ch
School of Applied Psychology
Zürich University of Applied Sciences

Florian Brühlmann

leane.wettstein@unibas.ch
University of Basel

florian.bruehlmann@unibas.ch
University of Basel

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.
CCS CONCEPTS
• Human-centered computing → Empirical studies in HCI.

### Language ###
English

### Assessment ###
Information about the number of survey participants in an AI certification study

### Query ###
Tell me the size of the survey sample used in analyzing peoples' attitudes about AI certification.
Tell me the size of the survey sample used in analyzing peoples' attitudes about AI certification.