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arxiv: 2605.00582 · v1 · submitted 2026-05-01 · 💻 cs.HC · cs.AI

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

Nick von Felten , Luisa Ella M\"uller , Johannes Sch\"oning This is my paper

Pith reviewed 2026-05-09 18:41 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords AI washingplacebo effectFitts' Lawuser expectationshuman-computer interactionAI marketing transparencyinput device performance
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0 comments X

The pith

AI-labeled mice raise user expectations of better performance but leave actual Fitts' Law task outcomes unchanged.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether labeling a standard computer mouse as AI-supported creates placebo expectations that improve pointing-task results. Twenty-eight participants performed Fitts' Law tasks under three within-subjects conditions: plain mouse, mouse labeled as predictive AI, and mouse labeled as biosignal-enhanced AI. Users reported significantly higher expected performance under the two placebo labels, yet objective measures of throughput and error rate plus subjective ratings of workload and usability showed no differences. The work establishes that AI washing can shape hopes about interaction without altering measurable behavior or experience.

Core claim

Participants expected significantly improved performance in the two placebo AI conditions compared to baseline, but these expectations did not translate into differences in objective Fitts' Law performance indicators or in subjective assessments of workload and usability.

What carries the argument

Within-subjects Fitts' Law experiment that compares a standard mouse against two placebo-labeled AI-supported versions while recording both expected and actual task performance.

If this is right

  • Overstated AI claims in product marketing can raise user expectations without delivering measurable interaction benefits.
  • Actual task performance and perceived workload remain the same across labeled and unlabeled conditions.
  • Fitts' Law tasks offer a repeatable method for auditing performance claims made about input devices.
  • Transparency requirements for AI-labeled consumer products are needed to prevent misleading expectations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same expectation-inflation pattern may appear with other everyday AI-labeled devices such as keyboards or touchscreens.
  • Empirical tests like this could help regulators define what counts as a deceptive AI claim.
  • Varying how believable the AI label is could quantify how strongly expectations must be held before they affect behavior.

Load-bearing premise

Participants fully believed the placebo AI labels were genuine and that the Fitts' Law task was sensitive enough to detect any small real performance differences if they existed.

What would settle it

A follow-up study in which participants are told the labels are fake yet still show no change in performance, or one in which genuine AI support is added and performance improves while expectations remain matched.

Figures

Figures reproduced from arXiv: 2605.00582 by Johannes Sch\"oning, Luisa Ella M\"uller, Nick von Felten.

Figure 1
Figure 1. Figure 1: Visualisation of the study procedure. The process started with a briefing explaining the conditions and tasks. Then view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the Fitts’ Law clicking task and the different interface and hardware elements we used to achieve the view at source ↗
Figure 3
Figure 3. Figure 3: Overview of subjective measure experimental outcomes. Although participants expected higher performance with view at source ↗
Figure 4
Figure 4. Figure 4: Visualisation of the one-dimensional Fitts’ Law task in which participants alternated clicks between left and right view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of Fitts’ Law regression models between view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reports a within-subjects experiment with 28 participants performing Fitts' Law pointing tasks on a computer mouse under three conditions: baseline (no support), placebo predictive AI support, and placebo biosignal-enhanced AI support. Participants showed significantly higher performance expectations in the AI-labeled conditions, but no differences emerged in objective Fitts' Law metrics (throughput, movement time, error rate) or subjective measures of workload and usability. The authors conclude that AI washing inflates expectations without altering actual interaction outcomes and position Fitts' Law as a rigorous method for auditing AI-labeled input devices.

Significance. If the null results on performance hold after addressing power and sensitivity concerns, the work provides controlled empirical evidence that deceptive AI marketing creates placebo expectations without corresponding performance gains. This is relevant to HCI for highlighting transparency issues in AI product claims. The use of objective, standardized Fitts' Law metrics is a methodological strength that supports falsifiable claims about interaction outcomes rather than relying solely on subjective reports.

major comments (2)
  1. [Methods] Methods section: No power analysis, minimum detectable effect size, or sensitivity discussion is reported for the primary Fitts' Law dependent variables (throughput, movement time, error rate). With n=28 in a within-subjects design across three conditions, the null performance result central to the claim ('does not alter actual interaction outcomes') cannot be confidently distinguished from low statistical power to detect small genuine placebo effects.
  2. [Abstract] Abstract and Methods: The abstract and methods description omit key Fitts' Law task parameters (target widths/distances, number of trials, index of difficulty range). Without these, it is impossible to evaluate whether the task had adequate sensitivity to detect the magnitude of performance change one might expect from even a weak placebo effect, directly affecting interpretation of the null result.
minor comments (2)
  1. [Results] Results: Report effect sizes (e.g., partial eta-squared or Cohen's d) for the significant expectation differences and for the non-significant performance comparisons to aid interpretation of the null findings.
  2. [Discussion] Discussion: Clarify whether the Fitts' Law task was chosen because it is insensitive to placebo effects or because it provides an objective benchmark; this would strengthen the methodological contribution claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important aspects of methodological transparency. We will revise the manuscript to address the concerns about statistical power and task parameter reporting. Our responses to the major comments are provided below.

read point-by-point responses

  1. Referee: [Methods] Methods section: No power analysis, minimum detectable effect size, or sensitivity discussion is reported for the primary Fitts' Law dependent variables (throughput, movement time, error rate). With n=28 in a within-subjects design across three conditions, the null performance result central to the claim ('does not alter actual interaction outcomes') cannot be confidently distinguished from low statistical power to detect small genuine placebo effects.

    Authors: We agree that a formal power or sensitivity analysis strengthens interpretation of null results. In the revision, we will add a post-hoc sensitivity analysis (e.g., using G*Power for repeated-measures ANOVA) to report the minimum detectable effect size for throughput, movement time, and error rate given n=28, alpha=0.05, and 80% power. This will clarify whether small placebo effects were detectable. We maintain that the standardized Fitts' Law paradigm is sensitive to performance differences, but explicit reporting addresses the concern directly. revision: yes

  2. Referee: [Abstract] Abstract and Methods: The abstract and methods description omit key Fitts' Law task parameters (target widths/distances, number of trials, index of difficulty range). Without these, it is impossible to evaluate whether the task had adequate sensitivity to detect the magnitude of performance change one might expect from even a weak placebo effect, directly affecting interpretation of the null result.

    Authors: We acknowledge the omission of these details. The revised Methods section will include the exact Fitts' Law parameters used in the study (target widths, distances, trial counts per condition, and index of difficulty range). We will also ensure the abstract references the standardized task setup to support evaluation of sensitivity. These additions will allow readers to assess whether the task could detect small effects. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical measurements with no derivations or self-referential claims

full rationale

The paper reports a within-subjects experiment measuring objective Fitts' Law metrics (throughput, movement time, error rate) and subjective ratings under three mouse conditions. No equations, fitted parameters, predictions derived from prior results, or self-citations are used to support the central claim. The null performance result and expectation inflation are direct outcomes of the collected data rather than any reduction to inputs by construction. The use of Fitts' Law is as a standard measurement tool, not a derived or renamed result. This is a self-contained empirical study with no load-bearing theoretical steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is an empirical user study with no mathematical derivations, new theoretical entities, or fitted parameters beyond standard statistical thresholds.

axioms (1)
  • standard math Standard assumptions underlying the statistical tests used to detect significant differences in expectations versus performance measures
    Implicit when reporting 'significantly improved performance expectations' without detailing test choices or checks.

pith-pipeline@v0.9.0 · 5501 in / 1158 out tokens · 34860 ms · 2026-05-09T18:41:15.586377+00:00 · methodology

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Reference graph

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