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Author: 2520

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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…

Wesley Hanwen Deng et al.Carnegie Mellon University

Online & AI Harms

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。

Tianshi Li et al.Carnegie Mellon University

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…

ÁC

Ángel Alexander Cabrera et al.Carnegie Mellon University

Human AI Collaboration I

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…

ÁC

Ángel Alexander Cabrera et al.Carnegie Mellon University

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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…

Rena Li et al.Carnegie Mellon University

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…

Tzu-Sheng Kuo et al.Carnegie Mellon University

'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…

Serena Wang et al.Carnegie Mellon University

Perspectives on Security & Privacy; Perspectives on Security & Privacy

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…

Haojian Jin et al.Carnegie Mellon University

“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…

Junchao Lin et al.Carnegie Mellon University

Connecting and Reaching Out

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…

ÁC

Ángel Alexander Cabrera et al.Carnegie Mellon University

Crowds and Collaboration

MessageOnTap: A Suggestive Interface to Facilitate Messaging-related Tasks

Text messages are sometimes prompts that lead to information related tasks, e.g. checking one's schedule, creating reminders, or sharing content. We introduce MessageOnTap, a suggestive inter-face for smartphones that uses the text in a conversation to suggest task shortcuts that can streamline likely next actions. Wh…

Fanglin Chen et al.Carnegie Mellon University

The Memory Palace: Exploring Visual-Spatial Paths for Strong, Memorable, Infrequent Authentication

Many accounts and devices require only infrequent authentication by an individual, and thus authentication secrets should be both secure and memorable without much reinforcement. Inspired by people's strong visual-spatial memory, we introduce a novel system to help address this problem: the Memory Palace. The Memory P…

Sauvik Das et al.Georgia Institute of Technology

Paper Title	Authors	Research Topics
emoji_events 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…	WD Wesley Hanwen Deng et al.Carnegie Mellon University	Online & AI Harms
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。	TL Tianshi Li et al.Carnegie Mellon University
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…	ÁC Ángel Alexander Cabrera et al.Carnegie Mellon University	Human AI Collaboration I
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…	ÁC Ángel Alexander Cabrera et al.Carnegie Mellon University
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
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…	RL Rena Li et al.Carnegie Mellon University
emoji_events 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…	TK Tzu-Sheng Kuo et al.Carnegie Mellon University
'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…	SW Serena Wang et al.Carnegie Mellon University	Perspectives on Security & Privacy; Perspectives on Security & Privacy
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…	HJ Haojian Jin et al.Carnegie Mellon University
“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…	JL Junchao Lin et al.Carnegie Mellon University	Connecting and Reaching Out
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…	ÁC Ángel Alexander Cabrera et al.Carnegie Mellon University	Crowds and Collaboration
MessageOnTap: A Suggestive Interface to Facilitate Messaging-related Tasks Text messages are sometimes prompts that lead to information related tasks, e.g. checking one's schedule, creating reminders, or sharing content. We introduce MessageOnTap, a suggestive inter-face for smartphones that uses the text in a conversation to suggest task shortcuts that can streamline likely next actions. Wh…	FC Fanglin Chen et al.Carnegie Mellon University
The Memory Palace: Exploring Visual-Spatial Paths for Strong, Memorable, Infrequent Authentication Many accounts and devices require only infrequent authentication by an individual, and thus authentication secrets should be both secure and memorable without much reinforcement. Inspired by people's strong visual-spatial memory, we introduce a novel system to help address this problem: the Memory Palace. The Memory P…	SD Sauvik Das et al.Georgia Institute of Technology