Using AI or Not

会議の名前
CHI 2025
Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems
要旨

Representation bias is one of the most common types of biases in artificial intelligence (AI) systems, causing AI models to perform poorly on underrepresented data segments. Although AI practitioners use various methods to reduce representation bias, their effectiveness is often constrained by insufficient domain knowledge in the debiasing process. To address this gap, this paper introduces a set of generic design guidelines for effectively involving domain experts in representation debiasing. We instantiated our proposed guidelines in a healthcare-focused application and evaluated them through a comprehensive mixed-methods user study with 35 healthcare experts. Our findings show that involving domain experts can reduce representation bias without compromising model accuracy. Based on our findings, we also offer recommendations for developers to build robust debiasing systems guided by our generic design guidelines, ensuring more effective inclusion of domain experts in the debiasing process.

受賞
Honorable Mention
著者
Aditya Bhattacharya
KU Leuven, Leuven, Vlaams-Brabant, Belgium
Simone Stumpf
University of Glasgow, Glasgow, United Kingdom
Robin De Croon
KU Leuven, Leuven, Vlaams-Brabant, Belgium
Katrien Verbert
KU Leuven, Leuven, Belgium
DOI

10.1145/3706598.3713497

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713497

動画
To Use or Not to Use: Impatience and Overreliance When Using Generative AI Productivity Support Tools
要旨

Generative AI has the potential to assist people with completing various tasks, but increased productivity is not guaranteed due to challenges such as uncertainty in output quality and unclear processing time. Through an online crowdsourced experiment (N=508), leveraging a “paint by numbers” task to simulate properties of GenAI assistance, we explore how, and how well, users make decisions on whether to use or not use automation to maximize their productivity given varying waiting times and output quality. We observed gaps between user’s actual choices and their optimal choices and characterized these gaps as the “gulf of impatience” and the “gulf of overreliance”. We also distilled strategies that participants adopted when making their decisions. We discuss design considerations in supporting users to make more informed decisions when interacting with GenAI tools and make these tools more useful for improving users’ task performance, productivity and satisfaction.

著者
Han Qiao
Autodesk Research, Toronto, Ontario, Canada
Jo Vermeulen
Autodesk Research, Toronto, Ontario, Canada
George Fitzmaurice
Autodesk Research, Toronto, Ontario, Canada
Justin Matejka
Autodesk Research, Toronto, Ontario, Canada
DOI

10.1145/3706598.3714103

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714103

動画
Public Opinions About Copyright for AI-Generated Art: The Role of Egocentricity, Competition, and Experience
要旨

Breakthroughs in generative AI (GenAI) have fueled debates concerning the artistic and legal status of AI-generated creations. We investigate laypeople's perceptions (N=432) of AI-generated art through the lens of copyright law. We study lay judgments of GenAI images concerning several copyright-related factors and capture people's opinions of who should be the authors and rights-holders of AI-generated images. To do so, we held an incentivized AI art competition in which some participants used a GenAI model to create art while others evaluated these images. We find that participants believe creativity and effort, but not skills, are needed to create AI-generated art. Participants were most likely to attribute authorship and copyright to the AI model's users and to the artists whose creations were used for training. We find evidence of egocentric effects: participants favored their own art with respect to quality, creativity, and effort---particularly when these assessments determined real monetary awards.

著者
Gabriel Lima
MPI-SP, Bochum, Germany
Nina Grgić-Hlača
Max Planck Institute for Software Systems, Saarbrücken, Germany
Elissa M.. Redmiles
Georgetown University, Washington, District of Columbia, United States
DOI

10.1145/3706598.3713338

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713338

動画
Understanding Attitudes and Trust of Generative AI Chatbots for Social Anxiety Support
要旨

Social anxiety (SA) has become increasingly prevalent. Traditional coping strategies often face accessibility challenges. Generative AI (GenAI), known for their knowledgeable and conversational capabilities, are emerging as alternative tools for mental well-being. With the increased integration of GenAI, it is important to examine individuals' attitudes and trust in GenAI chatbots' support for SA. Through a mixed-method approach that involved surveys (n = 159) and interviews (n = 17), we found that individuals with severe symptoms tended to trust and embrace GenAI chatbots more readily, valuing their non-judgmental support and perceived emotional comprehension. However, those with milder symptoms prioritized technical reliability. We identified factors influencing trust, such as GenAI chatbots' ability to generate empathetic responses and its context-sensitive limitations, which were particularly important among individuals with SA. We also discuss the design implications and use of GenAI chatbots in fostering cognitive and emotional trust, with practical and design considerations.

著者
Yimeng Wang
William & Mary, Williamsburg, Virginia, United States
Yinzhou Wang
William & Mary, Williamsburg, Virginia, United States
Kelly Crace
University of Virginia, Charlottesville, Virginia, United States
Yixuan Zhang
William & Mary, Williamsburg, Virginia, United States
DOI

10.1145/3706598.3714286

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714286

動画
Creative Writers’ Attitudes on Writing as Training Data for Large Language Models
要旨

The use of creative writing as training data for large language models (LLMs) is highly contentious and many writers have expressed outrage at the use of their work without consent or compensation. In this paper, we seek to understand how creative writers reason about the real or hypothetical use of their writing as training data. We interviewed 33 writers with variation across genre, method of publishing, degree of professionalization, and attitudes toward and engagement with LLMs. We report on core principles that writers express (support of the creative chain, respect for writers and writing, and the human element of creativity) and how these principles can be at odds with their realistic expectations of the world (a lack of control, industry-scale impacts, and interpretation of scale). Collectively these findings demonstrate that writers have a nuanced understanding of LLMs and are more concerned with power imbalances than the technology itself.

受賞
Best Paper
著者
Katy Ilonka. Gero
Harvard University, Cambridge, Massachusetts, United States
Meera Desai
University of Michigan, Ann Arbor, Michigan, United States
Carly Schnitzler
Johns Hopkins University, Baltimore, Maryland, United States
Nayun Eom
Harvard University, Cambridge, Massachusetts, United States
Jack Cushman
Harvard University, Cambridge, Massachusetts, United States
Elena L.. Glassman
Harvard University, Allston, Massachusetts, United States
DOI

10.1145/3706598.3713287

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713287

動画
AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances
要旨

Large language models (LLMs) are being increasingly integrated into everyday products and services, such as coding tools and writing assistants. As these embedded AI applications are deployed globally, there is a growing concern that the AI models underlying these applications prioritize Western values. This paper investigates what happens when a Western-centric AI model provides writing suggestions to users from a different cultural background. We conducted a cross-cultural controlled experiment with 118 participants from India and the United States who completed culturally grounded writing tasks with and without AI suggestions. Our analysis reveals that AI provided greater efficiency gains for Americans compared to Indians. Moreover, AI suggestions led Indian participants to adopt Western writing styles, altering not just what is written but also how it is written. These findings show that Western-centric AI models homogenize writing toward Western norms, diminishing nuances that differentiate cultural expression.

著者
Dhruv Agarwal
Cornell University, Ithaca, New York, United States
Mor Naaman
Cornell Tech, New York, New York, United States
Aditya Vashistha
Cornell University, Ithaca, New York, United States
DOI

10.1145/3706598.3713564

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713564

動画
The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers
要旨

The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices. We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so. Participants shared 936 first-hand examples of using GenAI in work tasks. Quantitatively, when considering both task- and user-specific factors, a user's task-specific self-confidence and confidence in GenAI are predictive of whether critical thinking is enacted and the effort of doing so in GenAI-assisted tasks. Specifically, higher confidence in GenAI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking. Qualitatively, GenAI shifts the nature of critical thinking toward information verification, response integration, and task stewardship. Our insights reveal new design challenges and opportunities for developing GenAI tools for knowledge work.

著者
Hao-Ping (Hank) Lee
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Advait Sarkar
Microsoft Research, Cambridge, United Kingdom
Lev Tankelevitch
Microsoft Research, Cambridge, United Kingdom
Ian Drosos
Microsoft Research, Cambridge, United Kingdom
Sean Rintel
Microsoft Research, Cambridge, United Kingdom
Richard Banks
Microsoft Research Cambridge, Cambridge, United Kingdom
Nicholas Wilson
Microsoft Research, Cambridge, United Kingdom
DOI

10.1145/3706598.3713778

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713778

動画