Demo Data from VLDB 2026 / reviewers in the wild / expert
Nina Chen
dblp:230/7626
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0001-8980-2779ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 2 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.
| Artificial intelligence
1 paper |
Trustworthy machine learning · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
User interface design and tools · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning › interpretability › neural network interpretation
interpretability of generative models |
0.5 | 1 | 2021 | Evaluating the Interpretability of Generative Models by Interactive Reconstruction · CHI 2021 |
Methods — techniques the papers use, named apart from their topics
interactive reconstruction · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Systemic Gender Inequities in Who Reviews CodeabstractCode review is an essential task for modern software engineers, where the author of a code change assigns other engineers the task of providing feedback on the author's code. In this paper, we investigate the task of code review through the lens of equity, the proposition that engineers should share reviewing responsibilities fairly. Through this lens, we quantitatively examine gender inequities in code review load at Google. We found that, on average, women perform about 25% fewer reviews than men, an inequity with multiple systemic antecedents, including authors' tendency to choose men as reviewers, a recommender system's amplification of human biases, and gender differences in how reviewer credentials are assigned and earned. Although substantial work remains to close the review load gap, we show how one small change has begun to do so. Emerson R. Murphy-Hill, Jillian Dicker, Amber Horvath, Margaret Morrow Hodges, Carolyn D. Egelman, Laurie R. Weingart, Ciera Jaspan, Collin Green, Nina Chen |
Proc. ACM Hum. Comput. Interact. | 9 |
| 2021 | Evaluating the Interpretability of Generative Models by Interactive Reconstruction
Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez |
CHI | 2 |