arXiv subjects

Kimi Wenzel

Publications and source records attributed to Kimi Wenzel.

5 recordsLinked to original sources

arXiv2025

Explaining the Reputational Risks of AI-Mediated Communication: Messages labeled as AI-assisted are viewed as less diagnostic of the sender's moral character

When someone sends us a thoughtful message, we naturally form judgments about their character. But what happens when that message carries a label indicating it was written with the help of AI? This paper investigates how the appearance of AI assistance affects our perceptions of message senders. Adding nuance to previous research, through two studies (N=399) featuring vignette scenarios, we find that AI-assistance labels don't necessarily make people view senders negatively. Rather, they dampen the strength of character signals in communication. We show that when someone sends a warmth-signalling message (like thanking or apologizing) without AI help, people more strongly categorize the sender as warm. At the same time, when someone sends a coldness-signalling message (like bragging or blaming) without assistance, people more confidently categorize them as cold. Interestingly, AI labels weaken both these associations: An AI-assisted apology makes the sender appear less warm than if they had written it themselves, and an AI-assisted blame makes the sender appear less cold than if they had composed it independently. This supports our signal diagnosticity explanation: messages labeled as AI-assisted are viewed as less diagnostic than messages which seem unassisted. We discuss how our findings shed light on the causal origins of previously reported observations in AI-Mediated Communication.

cs.HC↗

arXiv2024

Designing for Harm Reduction: Communication Repair for Multicultural Users' Voice Interactions

Voice assistants' inability to serve people-of-color and non-native English speakers has largely been documented as a quality-of-service harm. However, little work has investigated what downstream harms propagate from this poor service. How does poor usability materially manifest and affect users' lives? And what interaction designs might help users recover from these effects? We identify 6 downstream harms that propagate from quality-of-service harms in voice assistants. Through interviews and design activities with 16 multicultural participants, we unveil these 6 harms, outline how multicultural users uniquely personify their voice assistant, and suggest how these harms and personifications may affect their interactions. Lastly, we employ techniques from psychology on communication repair to contribute suggestions for harm-reducing repair that may be implemented in voice technologies. Our communication repair strategies include: identity affirmations (intermittent frequency), cultural sensitivity, and blame redirection. This work shows potential for a harm-repair framework to positively influence voice interactions.

cs.HC↗

arXiv2023

Beyond Fairness: Alternative Moral Dimensions for Assessing Algorithms and Designing Systems

The ethics of artificial intelligence (AI) systems has risen as an imminent concern across scholarly communities. This concern has propagated a great interest in algorithmic fairness. Large research agendas are now devoted to increasing algorithmic fairness, assessing algorithmic fairness, and understanding human perceptions of fairness. We argue that there is an overreliance on fairness as a single dimension of morality, which comes at the expense of other important human values. Drawing from moral psychology, we present five moral dimensions that go beyond fairness, and suggest three ways these alternative dimensions may contribute to ethical AI development.

cs.CY↗

arXiv2023

Challenges in Designing Racially Inclusive Language Technologies

We take a critical lens toward the pursuit of racially inclusive language technologies and identify several areas necessitating future work. We discuss the potential harms of conversational technologies, outline three challenges that arise in inclusive design, and lastly, argue that conversational user interface designers and researchers should go beyond racially inclusive design toward anti-racist and race positive designs.

cs.HC↗

arXiv2023

Can Voice Assistants Be Microaggressors? Cross-Race Psychological Responses to Failures of Automatic Speech Recognition

Language technologies have a racial bias, committing greater errors for Black users than for white users. However, little work has evaluated what effect these disparate error rates have on users themselves. The present study aims to understand if speech recognition errors in human-computer interactions may mirror the same effects as misunderstandings in interpersonal cross-race communication. In a controlled experiment (N=108), we randomly assigned Black and white participants to interact with a voice assistant pre-programmed to exhibit a high versus low error rate. Results revealed that Black participants in the high error rate condition, compared to Black participants in the low error rate condition, exhibited significantly higher levels of self-consciousness, lower levels of self-esteem and positive affect, and less favorable ratings of the technology. White participants did not exhibit this disparate pattern. We discuss design implications and the diverse research directions to which this initial study aims to contribute.

cs.HC↗