activity
20162025
most citedModeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms

57 citations · 103 across the 11 of their papers we have counts for

collaborators

12 papers

cs.HC2025

SnuggleSense: Empowering Online Harm Survivors Through a Structured Sensemaking Process

Sijia Xiao, Haodi Zou, Amy Mathews +3

Online interpersonal harm, such as cyberbullying and sexual harassment, remains a pervasive issue on social media platforms. Traditional approaches, primarily content moderation, o…

cs.HC2025★ 22 cited

Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Making

Bhada Yun, Dana Feng, Ace S. Chen +2

Our study of 20 knowledge workers revealed a common challenge: the difficulty of synthesizing unstructured information scattered across multiple platforms to make informed decision…

cs.HC2025

Sustaining Human Agency, Attending to Its Cost: An Investigation into Generative AI Design for Non-Native Speakers' Language Use

Yimin Xiao, Cartor Hancock, Sweta Agrawal +4

AI systems and tools today can generate human-like expressions on behalf of people. It raises the crucial question about how to sustain human agency in AI-mediated communication. W…

cs.CY2024★ 9 cited

(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court

Angela Jin, Niloufar Salehi

Accountable use of AI systems in high-stakes settings relies on making systems contestable. In this paper we study efforts to contest AI systems in practice by studying how public…

cs.CL2023★ 1 cited

Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors

Nikita Mehandru, Sweta Agrawal, Yimin Xiao +4

A major challenge in the practical use of Machine Translation (MT) is that users lack guidance to make informed decisions about when to rely on outputs. Progress in quality estimat…

cs.HC2022★ 1 cited

Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust

Wesley Hanwen Deng, Nikita Mehandru, Samantha Robertson +1

Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals. However, in order to use MT…