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

arXiv: https://arxiv.org/abs/2605.00582 · HTML: https://mlsanity.com/paper/2605.00582/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 52 references that are in the map.
- First posted on arXiv 1 May 2026.

## Builds on (most cited first, 4 of 4)

- ["Look! It's a Computer Program! It's an Algorithm! It's AI!": Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?](https://mlsanity.com/paper/2108.11486/) (2021)
- ["AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AI](https://mlsanity.com/paper/2309.16606/) (2023)
- [The Placebo Effect of Artificial Intelligence in Human-Computer Interaction](https://mlsanity.com/paper/2204.04935/) (2022)
- [The Illusion of Performance: The Effect of Phantom Display Refresh Rates on User Expectations and Reaction Times](https://mlsanity.com/paper/2401.17706/) (2024)

## Abstract (arXiv)

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.