EDBT 2026 Demo / reviewers in the wild / expert
Alisa Scharmann
dblp:381/3825
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction › reliance on AI
appropriate reliance on AI |
1.0 | 1 | 2026 | 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 |
1.0 | 1 | 2026 | 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 |
1.0 | 1 | 2026 | 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 |
0.3 | 1 | 2026 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Certified But Imperfect: Investigating The Role of AI Certifications And System Performance on Trust in And Reliance on AI SystemsabstractWhile 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 |
CHI | 2 |