PWR-196 · Human–AI Complementarity

AI-assisted perception

AI can assist selected perceptual judgements, but performance belongs to the versioned reader-tool-workflow configuration.

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Revision 7T · Full Tutorial Edition · Release manifest · Methodology · Corrections

One source of teaching truth

Canonical Power learning unit · TLU-PWR-196

The learner compares human-only, AI-only and combined labels against an answer key and identifies when incorrect advice changes a correct human judgement.

Canonical Power page
PWR-196 · AI-assisted perception
Full tutorial
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Practical authority
The tutorial teaches a low-risk method that may be practised inside its stated limits.
Current treatment
Full low-stakes tutorial
Research depth
focused · 3 bound sources
Risk framing
moderate to high governance
Capability self-practice
Permitted inside the tutorial's stated low-risk limits
Pathway membership
G-CUR-021

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Explanation is not permission.

This dossier explains the evidence and its limits. It is not a diagnosis, personal recommendation, assessment, clearance, performance promise or training programme. Actionable teaching appears only when the canonical Power record explicitly authorises it.

Complete bounded explanation

What this power means.

Capability to develop or express ai-assisted perception in a declared context without inheriting broader claims.

What the current evidence supports

AI can assist selected perceptual judgements, but performance belongs to the versioned reader-tool-workflow configuration.

How to observe or measure it without overclaiming

Sensitivity, specificity, calibration, time and severity-weighted misses versus expert-only, AI-only and combined conditions. This measures the declared configured system and cannot by itself establish broad transfer, unaided ability, safety or legitimacy.

Myth

AI gives superhuman sight

Metric

Sensitivity, specificity, calibration, time and severity-weighted misses versus expert-only, AI-only and combined conditions

Boundary

Report person, device/model/interface, task, assistance, failure state and comparator; the metric is not proof of unaided general capability.

Negative and limiting findings

  • Some combined teams did not exceed the best standalone component, and incorrect advice or explanations could induce overreliance.
  • No cited evidence supports perfect, universal or consequence-free performance.

Claims this evidence cannot support

  • AI gives superhuman sight
  • The tool or robot's output proves an unaided, universal or permanent human superpower
  • A successful laboratory task proves independent real-world or clinical capability

Individual tutorial · FULL SELF GUIDED TUTORIAL

How to learn this Power now.

The learner compares human-only, AI-only and combined labels against an answer key and identifies when incorrect advice changes a correct human judgement.

  1. Get readyUse synthetic, public or consented benign images with no biometric or sensitive content.
  2. Learn the method8 concrete steps teach the permitted method from start to finish.
  3. See right and wrong4 comparisons show correct or safer execution beside common wrong or riskier choices.
  4. PractiseUse one 30-image benign set weekly for three weeks, changing blur and class prevalence separately. Retest 15 new images after 48 hours; progress only when combined performance beats the better standalone component without more severe misses.
  5. Check progressBaseline fixture: A labelled set of 30 synthetic street-sign images contains 10 bicycles, 10 buses and 10 neither; five images in each class are blurred. The AI returns a label and confidence. Enter success checks from “Declare classes and error costs” and “Resolve disagreements”. If “Let the AI label determine where you look.” occurs, apply its named fix; then score Sensitivity, specificity, calibration, time and severity-weighted misses versus expert-only, AI-only and combined conditions on the unused “Test induced error” item.

Authority boundary: Titan teaches a concrete low-stakes method step by step while keeping evidence and transfer claims bounded. The method is Power-specific; its actionability follows this treatment.

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Related Powers: PWR-193 · PWR-194 · PWR-195 · PWR-197

Direct evidence register

Sources that support—and limit—the claim.

Citation roles are explicit. A source may support existence or trainability while simultaneously limiting transfer, certainty, generalisation or safety.

  1. Primary empirical supportLimiting / contrary
    Explainable AI improves task performance in human-AI collaboration

    Julian Senoner; Simon Schallmoser; Bernhard Kratzwald; Stefan Feuerriegel; Torbjørn Netland · 2024 · Primary research

  2. Primary empirical supportLimiting / contrary
    Improving the Performance of Radiologists Using Artificial Intelligence-Based Detection Support Software for Mammography: A Multi-Reader Study

    Jeong Hoon Lee; Ki Hwan Kim; Eun Hye Lee; Jong Seok Ahn; Jung Kyu Ryu; Young Mi Park; Gi Won Shin; Young Joong Kim; Hye Young Choi · 2022 · Primary research

  3. Limiting / contraryOfficial boundary context
    NIST Privacy Framework: A Tool for Improving Privacy Through Enterprise Risk Management, Version 1.0

    National Institute of Standards and Technology · 2020 · Official authority

Current teaching and evidence boundary

The next gate remains visible.

External confirmation is required before higher-authority use because: a clinical, high-risk or regulated configuration; a restricted safety or legitimacy boundary.

A Power-specific tutorial is available at /tutorials/pwr-196/. It contains a plain-English method, 8 ordered steps, a worked example, right-versus-wrong comparisons, specific mistakes and corrections, practice, measurement, accessibility, stopping rules and evidence. Its self guided mode remains controlling.