Impact of Voice Fidelity on Decision Making: A Potential Dark Pattern?
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Abstract (arXiv)
Manipulative design in user interfaces (conceptualized as dark patterns) has emerged as a significant impediment to the ethical design of technology and a threat to user agency and freedom of choice. While previous research focused on exploring these patterns in the context of graphical user interfaces, the impact of speech has largely been overlooked. We conducted a listening test (N = 50) to elicit participants' preferences regarding different synthetic voices that varied in terms of synthesis method (concatenative vs. neural) and prosodic qualities (speech pace and pitch variance), and then evaluated their impact in an online decision-making study (N = 101). Our results indicate a significant effect of voice qualities on the participant's choices, independently from the content of the available options. Our results also indicate that the voice's perceived engagement, ease of understanding, and domain fit directly translate to its impact on participants' behaviour in decision-making tasks.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2024.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 5.
8 papers.
- 2024GPT-4o System Cardcited by 4,467
- 2025Toward Metaphor-Fluid Conversation Design for Voice User Interfacescited by 5
- 2026Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Modelscited by 3
- 2024The Voice: Lessons on Trustworthy Conversational Agents from "Dune"cited by 1
- 2026RealityTest: How People Probe AI Identity and Whether Models Disclose Itcited by 1
- 2026Predicting Biased Human Decision-Making with Large Language Models in Conversational Settingscited by 0
- 2026Emulating Aggregate Human Choice Behavior and Biases with GPT Conversational Agentscited by 0
- 2026A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracingcited by 0
- 2026A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing3 Jun 2026
- 2026RealityTest: How People Probe AI Identity and Whether Models Disclose It29 May 2026
- 2026Emulating Aggregate Human Choice Behavior and Biases with GPT Conversational Agents5 Feb 2026
- 2026Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Models27 Jan 2026
- 2026Predicting Biased Human Decision-Making with Large Language Models in Conversational Settings16 Jan 2026
- 2025Toward Metaphor-Fluid Conversation Design for Voice User Interfaces17 Feb 2025
- 2024GPT-4o System Card25 Oct 2024
- 2024The Voice: Lessons on Trustworthy Conversational Agents from "Dune"10 Jul 2024