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

TL;DR

This study shows that AI washing inflates user expectations without improving actual interaction performance, highlighting transparency issues in AI marketing and demonstrating Fitts' Law as a tool for auditing AI-labelled devices.

Contribution

It provides empirical evidence that AI washing inflates expectations without affecting performance and introduces Fitts' Law as a method for auditing AI-labelled input devices.

Findings

Participants expected better performance with AI support, despite no actual improvement.

Objective and subjective measures showed no difference across conditions.

Fitts' Law effectively audits AI-labelled input devices.

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.