Alisa Scharmann

dblp:381/3825 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0005-7367-7914ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 91% Usability and user experience research · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction › reliance on AI
appropriate reliance on AI
1.012026
Certified But Imperfect: Investigating The Role of AI Certifications And System Performance on Trust in And Reliance on AI Systems · CHI 2026
Human-AI interaction
reliance on AI
1.012026
Certified But Imperfect: Investigating The Role of AI Certifications And System Performance on Trust in And Reliance on AI Systems · CHI 2026
Human-AI interaction
trust in AI
1.012026
Certified But Imperfect: Investigating The Role of AI Certifications And System Performance on Trust in And Reliance on AI Systems · CHI 2026
Usability and user experience research
user expectations
0.312026
Certified But Imperfect: Investigating The Role of AI Certifications And System Performance on Trust in And Reliance on AI Systems · CHI 2026

Methods — techniques the papers use, named apart from their topics

between-subjects online study · 1.0
YearPublicationVenuePosition
2026 Certified But Imperfect: Investigating The Role of AI Certifications And System Performance on Trust in And Reliance on AI Systems
abstract
While regulatory frameworks call for the implementation of AI certifications, empirical knowledge about how such certifications affect interactions is still scarce. In this work, we examined how AI certifications affect users’ trust and reliance. In addition, we examined whether certifications elevate user expectations and whether unmet expectations subsequently reduce trust. In a 2 (certification vs no certification) x 2 (reliability: high vs low) between-subjects online study, N = 644 participants had to identify bacterial infestation in pictures with the help of an AI. Our results show that, before interacting with the AI, participants trusted the certified system more and showed reduced vigilance. However, these effects disappeared post-interaction, where, instead of the certification, system reliability significantly affected trust and vigilance. Notably, certifications did not raise expectations per se, but instead amplified the impact of system reliability on user trust. Additional exploratory results showed that the certification supported appropriate reliance.
Magdalena Wischnewski, Alisa Scharmann, Annika Ridder, Nicole C. Krämer
CHI2