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Self-Disclosure to AI: The Paradox of Trust and Vulnerability in Human-Machine Interactions

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arxiv 2412.20564 v1 pith:YEJFNSTW submitted 2024-12-29 cs.HC cs.RO

classification cs.HCcs.RO
keywords trustinteractionsvulnerabilityhuman-machinemachinesparadoxpersonalprivacy
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we explore the paradox of trust and vulnerability in human-machine interactions, inspired by Alexander Reben's BlabDroid project. This project used small, unassuming robots that actively engaged with people, successfully eliciting personal thoughts or secrets from individuals, often more effectively than human counterparts. This phenomenon raises intriguing questions about how trust and self-disclosure operate in interactions with machines, even in their simplest forms. We study the change of trust in technology through analyzing the psychological processes behind such encounters. The analysis applies theories like Social Penetration Theory and Communication Privacy Management Theory to understand the balance between perceived security and the risk of exposure when personal information and secrets are shared with machines or AI. Additionally, we draw on philosophical perspectives, such as posthumanism and phenomenology, to engage with broader questions about trust, privacy, and vulnerability in the digital age. Rapid incorporation of AI into our most private areas challenges us to rethink and redefine our ethical responsibilities.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness

    cs.HC 2025-01 reject novelty 4.0 of 10

    Disclosing a game AI's superhuman ability can reduce suspicion and raise trust in novices, but also triggers overreliance and defeatism among experts and in cooperative settings.

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