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

• URL: http://arxiv.org/abs/2605.00582v1
• Date: Fri, 01 May 2026 11:42:40 GMT
• Title: AI Washing Inflates Expected Performance but Not Interaction Outcomes: An AI Placebo Study Using Fitts' Law
• Abstract summary: Expectations about the support of artificial intelligence may influence interaction outcomes similar to placebos.<n>Such expectations may result from AI washing, a practice of overstating a system's AI capabilities when actual functionality is limited.<n>This paper contributes evidence that AI washing inflates user expectations without altering actual interaction outcomes.
• Score: 11.815265443120987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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