Demo Data from EDBT 2026 / reviewers in the wild / expert
Bill Boyuan Guo
dblp:330/9530
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-8303-4649ORCID · 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.
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 100% | |
| Artificial intelligence
1 paper |
Trustworthy machine learning · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction
algorithmic auditing |
0.7 | 1 | 2023 | Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice · CHI 2023 |
Empirical software engineering › software engineering practice
industrial practice |
0.7 | 1 | 2023 | Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice · CHI 2023 |
Empirical software engineering
practitioner studies |
0.7 | 1 | 2023 | Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice · CHI 2023 |
Methods — techniques the papers use, named apart from their topics
interview study · 2.0co-design · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry PracticeabstractRecent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems. However, we know little about industry practitioners’ current practices and challenges around user-engaged auditing, nor what opportunities exist for them to better leverage such approaches in practice. To investigate, we conducted a series of interviews and iterative co-design activities with practitioners who employ user-engaged auditing approaches in their work. Our findings reveal several challenges practitioners face in appropriately recruiting and incentivizing user auditors, scaffolding user audits, and deriving actionable insights from user-engaged audit reports. Furthermore, practitioners shared organizational obstacles to user-engaged auditing, surfacing a complex relationship between practitioners and user auditors. Based on these findings, we discuss opportunities for future HCI research to help realize the potential (and mitigate risks) of user-engaged auditing in industry practice. Wesley Deng, Bill Boyuan Guo, Alicia DeVrio, Hong Shen 0004, Motahhare Eslami, Kenneth Holstein |
CHI | 2 |