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Betsy DiSalvo

Publications and source records attributed to Betsy DiSalvo.

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Exploring the Interplay Between Voice, Personality, and Gender in Human-Agent Interactions

To foster effective human-agent interactions, designers must understand how vocal cues influence the perception of agent personality and the role of user-agent alignment in shaping these perceptions. In this work, we examine whether users can perceive extroversion in voice-only artificial agents and how perceived personality relates to user-agent synchrony. We conducted a study with 388 participants, who evaluated four synthetic voices derived from human recordings, varying by gender (male, female) and personality expression (introverted, extroverted). Our results show that participants were able to differentiate perceived extroversion in female agent voices, but not consistently in male voices. We also observed evidence of perceived personality synchrony, particularly in participants' evaluations of the first agent encountered, with this effect more pronounced among male participants and toward male agents. We discuss these findings in light of limitations in stimulus diversity and voice representation, and outline implications for the design of voice-based agents, particularly regarding the interaction between gender, personality perception, and initial user impressions. This paper contributes findings and insights to consider the interplay of user-agent personality and gender synchrony in the design of human-agent interactions.

cs.HC

The Problems with Proxies: Making Data Work Visible through Requester Practices

Fairness in AI and ML systems is increasingly linked to the proper treatment and recognition of data workers involved in training dataset development. Yet, those who collect and annotate the data, and thus have the most intimate knowledge of its development, are often excluded from critical discussions. This exclusion prevents data annotators, who are domain experts, from contributing effectively to dataset contextualization. Our investigation into the hiring and engagement practices of 52 data work requesters on platforms like Amazon Mechanical Turk reveals a gap: requesters frequently hold naive or unchallenged notions of worker identities and capabilities and rely on ad-hoc qualification tasks that fail to respect the workers' expertise. These practices not only undermine the quality of data but also the ethical standards of AI development. To rectify these issues, we advocate for policy changes to enhance how data annotation tasks are designed and managed and to ensure data workers are treated with the respect they deserve.

cs.HC