arXiv:2605.00582cs.HCcs.AI2026-05中稿 · the 2026 ACM Confe…

AI伪装夸大用户期待,但实际操作无变化

AI Washing Inflates Expected Performance but Not Interaction Outcomes: An AI Placebo Study Using Fitts' Law

论文配图:AI Washing Inflates Expected Performance but Not Interaction Outcomes: An AI Placebo Study Using Fitts' Law
图 1 · 摘自论文原文
  • 用假AI功能测试用户表现与预期差异
  • 期待提升但真实操作和感受无显著变化
  • 揭示AI营销误导性,适合关注人机交互者

人们对AI辅助的期望可能如同安慰剂效应。这种期望源于AI洗白——夸大系统实际能力,例如某些鼠标标榜‘AI辅助’却无核心AI功能。本研究采用被试内设计,28名参与者在三种条件下完成Fitts' Law任务:无支持、伪预测AI支持、伪生物信号增强AI支持。测量了客观性能指标、主观期望、感知工作量及可用性。相比基线,安慰剂条件下用户预期显著提升,但客观表现与主观感受均未产生差异。研究证明AI洗白会虚增用户期待,但不影响真实交互结果,凸显透明度问题。同时确立Fitts' Law作为审计带AI标签输入设备的有效方法。

原文摘要 · Abstract (English)

Expectations about the support of artificial intelligence (AI) may influence interaction outcomes similar to placebos. Such expectations may result from AI washing, a practice of overstating a system's AI capabilities when actual functionality is limited. For example, some computer mice are marketed as "AI-assisted" despite lacking AI in core functions. In a within-subjects study, 28 participants completed Fitts' Law tasks with a computer mouse under three conditions: no support, supposed predictive AI support, and supposed biosignal-enhanced AI support. Objective Fitts' Law performance indicators and subjective performance expectations, perceived workload, and perceived usability were measured. Compared to baseline, participants expected significantly improved performance in placebo conditions. However, these expectations did not translate into differences in objective or subjective assessments. This paper contributes evidence that AI washing inflates user expectations without altering actual interaction outcomes, highlighting a critical transparency issue. By exposing how deceptive AI marketing can shape user expectations, we underscore the need for accountability in AI product claims. Further, we establish Fitts' Law as a rigorous methodological lens for auditing AI-labelled input devices.

人机交互认知偏差产品可信度

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