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Nina Chen

dblp:230/7626 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning › interpretability › neural network interpretation
interpretability of generative models
0.512021
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
YearPublicationVenuePosition
2023 Systemic Gender Inequities in Who Reviews Code
abstract
Code 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
CHI2