Demo Data from EDBT 2026 / reviewers in the wild / expert
Zhamilya Bilyalova
dblp:433/3065
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0007-3514-2016ORCID · 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 · 87% Design research and methods · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction › user perception of AI
fairness perceptions |
1.0 | 1 | 2026 | Balancing Automation and Discretion: How Decision Stakes and Human-AI Collaboration Affect Citizen Perceptions in Public Administration · CHI 2026 |
Human-AI interaction
human-AI collaboration |
1.0 | 1 | 2026 | Balancing Automation and Discretion: How Decision Stakes and Human-AI Collaboration Affect Citizen Perceptions in Public Administration · CHI 2026 |
Design research and methods › research methodology
mixed-methods study |
0.3 | 1 | 2026 | Balancing Automation and Discretion: How Decision Stakes and Human-AI Collaboration Affect Citizen Perceptions in Public Administration · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
wizard-of-oz study · 1.0quantitative analysis · 1.0qualitative interviews · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Balancing Automation and Discretion: How Decision Stakes and Human-AI Collaboration Affect Citizen Perceptions in Public AdministrationabstractThe growing use of AI in public administration improves efficiency, yet its use in discretionary decisions raises concerns about fairness and legitimacy. While prior research examined decision stakes and Human–AI decision-making configurations separately, their combined effect on citizens’ perceptions of fairness and adoption remains underexplored. We conducted a mixed-method Wizard-of-Oz study (n=43) using an Intelligent-Self-Service-Kiosk. Participants completed a low-stakes (ID-renewal) and a high-stakes (social-housing) task under one of three decision-making configurations: AI alone, AI with human-supervision, and human with AI advice or recommendation. Quantitative analysis found no significant effects, highlighting the limits of standard metrics. However, qualitative interviews revealed that citizens valued human involvement, requiring meaningful over symbolic oversight. They emphasized interactive dialogue before decisions to capture their circumstances and after, to facilitate appeals. We contribute evidence of tensions between citizens’ desire for efficiency and need for human-control and fairness. We provide guidance for designing citizen-centered AI systems that align with democratic values. Saja Aljuneidi, Wilko Heuten, Zhamilya Bilyalova, Maria Klara Wolters, Susanne Boll |
CHI | 3 |