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Recent advances in artificial speech synthesis and machine learning equip AI-powered conversational agents, from voice assistants to social robots, with the ability to mimic human emotional expression during their interactions with users. One unexplored development is the ability to design machine-generated voices that induce varying levels of “shakiness” (i.e., trembling) in the agents’ voices. In the current work, we examine how the trembling voice of a conversational AI impacts users’ perceptions, affective experiences, and their subsequent behavior. Across three studies, we demonstrate that a trembling voice enhances the perceived psychological vulnerability of the agent, followed by a heightened sense of empathic concern, ultimately increasing people's willingness to donate in a prosocial charity context. We provide further evidence from a large-scale field experiment that conversational agents with a trembling voice lead to increased click-through rates and decreased costs-per-impression in an online charity advertising setting. These findings deepen our understanding of the nuanced impact of intentionally designed voices of conversational AI agents on humans and highlight the ethical and societal challenges that arise. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and related technologies are transforming many consumption activities, powering breakthroughs that expand the human experience by enhancing human capabilities, performance, and creativity. While this explains the consumer enthusiasm and rapid adoption of these technologies, AI systems can also have the opposite effect: reducing and constraining the range of experiences that are available to consumers. This article examines the mechanisms through which AI can constrain the human experience, considering individual, interpersonal, and societal processes. Our analysis uncovers a complex interplay between the advantages of AI and its inadvertent negative repercussions, which potentially restrict human autonomy, self-identity, relational dynamics, and social behavior. In this article, we propose three different mechanisms at the core of these constraining forces: parametric reductionism, agency transference, and regulated expression. Our exploration of these mechanisms highlights the risks connected to system design and points to questions and implications for future researchers and policymakers. [ABSTRACT FROM AUTHOR]