PWR-122 · Metacognition & Epistemic Resilience

Error detection

Error detection improves when people and interfaces are designed together—but rare mistakes remain easy to miss.

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

One source of teaching truth

Canonical Power learning unit · TLU-PWR-122

The learner applies a four-pass checklist to three synthetic reports and reduces high-cost misses without increasing false alarms beyond the declared limit.

Canonical Power page
PWR-122 · Error detection
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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
detailed · 4 bound sources
Risk framing
moderate to high
Capability self-practice
Permitted inside the tutorial's stated low-risk limits
Pathway membership
G-CUR-012

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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 error detection in a declared context without inheriting broader claims.

What the current evidence supports

Selected interfaces can reduce specific human–AI and rare-target errors, but this is configured system performance rather than a universal human faculty.

How to observe or measure it without overclaiming

Declare error taxonomy, prevalence, base rate, false alarms, misses, correction path, model/version, workload and consequences; high accuracy can hide rare catastrophic misses.

Myth

Spot every hidden error

Metric

Miss rate, false-alarm rate, time to correction and harm under declared prevalence

Boundary

One detection task cannot establish universal vigilance, honesty detection or safety.

Negative and limiting findings

  • Very rare targets produced high miss rates despite extensive task exposure.
  • Cognitive forcing reduced overreliance but lowered user ratings and varied across people.

Claims this evidence cannot support

  • spot every mistake
  • human lie detector
  • AI-proof your mind
  • error score identifies careless people

Individual tutorial · FULL SELF GUIDED TUTORIAL

How to learn this Power now.

The learner applies a four-pass checklist to three synthetic reports and reduces high-cost misses without increasing false alarms beyond the declared limit.

  1. Get readyDefine the four error classes and examples before inspection.
  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 three-report set weekly for four weeks, with new synthetic content and predeclared error prevalence. Keep time fixed. After week two, include a set with very rare targets to test vigilance without increasing stakes.
  5. Check progressFor each class, count hits, misses, false alarms and correct rejections. Calculate sensitivity as hits/(hits+misses) when at least one keyed error exists; report false-alarm count and eligible clean checks, time and burden, then compare matched baseline and checklist sets without merging classes.

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-121 · PWR-123 · PWR-124 · PWR-125

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
    Rare Targets Are Rarely Missed in Correctable Search

    Mathias S. Fleck; Stephen R. Mitroff · 2007 · Primary research

  2. Primary empirical supportLimiting / contrary
    To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making

    Zana Buçinca; Maja Barbara Malaya; Krzysztof Z. Gajos · 2021 · Primary research

  3. Primary empirical supportLimiting / contrary
    The Ultra-Rare-Item Effect

    Stephen R. Mitroff; Adam T. Biggs · 2013 · Primary research

  4. Limiting / contraryOfficial boundary context
    Artificial Intelligence Risk Management Framework (AI RMF 1.0)

    National Institute of Standards and Technology · 2023 · 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 Power-specific tutorial is available at /tutorials/pwr-122/. 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.