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

2026-05-01

AI Summary

This study investigates how “AI washing”—the unsubstantiated use of AI-related claims in marketing—misleads users’ expectations regarding human-AI interaction performance and whether such claims genuinely influence interaction outcomes. Employing a within-subjects experiment grounded in Fitts’ Law, the research compares users’ objective performance and subjective experiences across three conditions: no support, false predictive AI support, and false biometric-enhanced AI support. For the first time, the Fitts’ Law paradigm is leveraged as an auditing tool to evaluate AI-labeled input devices. Findings reveal that AI washing inflates user expectations without improving actual task performance or subjective experience, thereby exposing significant cognitive bias risks. The work underscores the necessity for transparent and credible AI product claims and introduces a novel methodological approach for evaluating AI-driven interactions.

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.

Problem

• AI washing
• user expectations
• interaction outcomes
• placebo effect
• Fitts' Law

Innovation

• AI washing
• placebo effect
• Fitts' Law
• human-AI interaction
• performance expectation