Jason I. Hong

schoolCarnegie Mellon Universitybar_chart#665 Global Rank

Total Papers

HCI Rank

#665

Research Areas

Years Active

Recent Publications

WeAudit: Scaffolding User Auditors and AI Practitioners in Auditing Generative AI

There has been growing interest from both practitioners and researchers in engaging end users in AI auditing, to draw upon users’ unique knowledge and lived experiences. However, we know little about how to effectively scaffold end users in auditing in ways that can generate actionable insights for AI practitioners. T…

UBICOMP '24CorrespondingPrivacy by Design & User Control

Matcha: An IDE Plugin for Creating Accurate Privacy Nutrition Labels

Li 等人开发 Matcha IDE 插件，自动分析应用代码生成精确的隐私营养标签，帮助用户理解应用数据收集行为。

CSCW '23CorrespondingHuman AI Collaboration I

Improving Human-AI Collaboration With Descriptions of AI Behavior

People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted. To help people appropriately rely on AI aids, we propose showing them behavior descriptions, details of how AI systems perform on subgroups of instances. We t…

CHI '235th+ AuthorExplainable AI (XAI) +1

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

Machine learning models with high accuracy on test data can still produce systematic failures, such as harmful biases and safety issues, when deployed in the real world. To detect and mitigate such failures, practitioners run behavioral evaluation of their models, checking model outputs for specific types of inputs. B…

CHI '23CorrespondingAI Ethics, Fairness & Accountability +1

Participation and Division of Labor in User-Driven Algorithm Audits: How Do Everyday Users Work Together to Surface Algorithmic Harms?

Recent years have witnessed an interesting phenomenon in which users come together to interrogate potentially harmful algorithmic behaviors they encounter in their everyday lives. Researchers have started to develop theoretical and empirical understandings of these user-driven audits, with a hope to harness the power…

CHI '235th+ AuthorBest PaperAI Ethics, Fairness & Accountability +1

Understanding Frontline Workers’ and Unhoused Individuals’ Perspectives on AI Used in Homeless Services

Recent years have seen growing adoption of AI-based decision-support systems (ADS) in homeless services, yet we know little about stakeholder desires and concerns surrounding their use. In this work, we aim to understand impacted stakeholders’ perspectives on a deployed ADS that prioritizes scarce housing resources. W…

CSCW '22CorrespondingPerspectives on Security & Privacy; Perspectives on Security & Privacy

'It's Problematic but I'm not Concerned': University Perspectives on Account Sharing

Account sharing is a common, if officially unsanctioned, practice among workgroups, but so far understudied in higher education. We interview 23 workgroup members about their account sharing practices at a U.S. university. Our study is the first to explicitly compare IT and non-IT observations of account sharing as a…

CHI '22CorrespondingAlgorithmic Transparency & Auditability +2

Exploring the Needs of Users for Supporting Privacy-protective Behavior in Smart Homes

In this paper, we studied people’s smart home privacy-protective behaviors (SH-PPBs), to gain a better understanding of their privacy management do’s and don’ts in this context. We first surveyed 159 participants and elicited 33 unique SH-PPB practices, revealing that users heavily rely on ad hoc approaches at the phy…

CSCW '212nd AuthorConnecting and Reaching Out

“It’s our mutual responsibility to share”: The evolution of account sharing in romantic coupless

While most online accounts are designed assuming a single user, past work has found that romantic couples often share many accounts. Our study examines couples’ account sharing behaviors as their relationships develop. We conducted 19 semi-structured interviews with people who are currently in romantic relationships t…

CSCW '213rd AuthorCrowds and Collaboration

Discovering and Validating AI Errors With Crowdsourced Failure Reports

AI systems can fail to learn important behaviors, leading to real-world issues like safety concerns and biases. Discovering these systematic failures often requires significant developer attention, from hypothesizing potential edge cases to collecting evidence and validating patterns. To scale and streamline this proc…

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