PWR-197 · Human–AI Complementarity

AI-assisted decision calibration

Reliance on AI can be made more selective in some interfaces, but no explanation or warning guarantees good judgement.

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

One source of teaching truth

Canonical Power learning unit · TLU-PWR-197

The learner uses a known-quality advice deck to calculate appropriate acceptance, over-reliance, under-reliance and calibration by confidence band.

Canonical Power page: PWR-197 · AI-assisted decision calibration
Full tutorial: Open full tutorial
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: deep · 8 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 decision calibration in a declared context without inheriting broader claims.

What the current evidence supports

Reliance on AI can be made more selective in some interfaces, but no explanation or warning guarantees good judgement.

How to observe or measure it without overclaiming

Calibration, appropriate acceptance/rejection, verification rate, workload and harm-weighted errors across advice-quality strata. This measures the declared configured system and cannot by itself establish broad transfer, unaided ability, safety or legitimacy.

Myth

A confidence score makes AI safe to trust

Metric

Calibration, appropriate acceptance/rejection, verification rate, workload and harm-weighted errors across advice-quality strata

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

Incorrect AI reduced accuracy, explanations often failed to help, and acquired AI bias persisted into later unaided decisions.
No cited evidence supports perfect, universal or consequence-free performance.

Claims this evidence cannot support

A confidence score makes AI safe to trust
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 uses a known-quality advice deck to calculate appropriate acceptance, over-reliance, under-reliance and calibration by confidence band.

Get readyUse an answer-key task with deliberately mixed advice quality.
Learn the method8 concrete steps teach the permitted method from start to finish.
See right and wrong4 comparisons show correct or safer execution beside common wrong or riskier choices.
PractiseComplete one 20-case deck, then five no-AI items after 48 hours. Repeat monthly with changed reliability bands and version labels; discard the old rule after any task, model or distribution change.
Check progressBaseline fixture: A fictional travel-budget task has 20 answer-key cases. The AI advice is correct on 8 of 10 high-confidence messages but only 3 of 10 low-confidence messages; the learner does not know which individual messages are wrong. Enter success checks from “Define the decision and loss” and “Choose accept, reject or defer”. If “Read the explanation and treat it as verification.” occurs, apply its named fix; then score Calibration, appropriate acceptance/rejection, verification rate, workload and harm-weighted errors across advice-quality strata on the unused “Test persistence of bias” 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.

Open the full tutorialOpen My Power Path

Related Powers: PWR-193 · PWR-194 · PWR-195 · PWR-196

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.

Primary empirical supportLimiting / contrary
Humans inherit artificial intelligence biases
Lucía Vicente; Helena Matute · 2023 · Primary research
Primary empirical supportLimiting / contrary
Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task
Julia Cecil; Eva Lermer; Matthias F. C. Hudecek; Jan Sauer; Susanne Gaube · 2024 · Primary research
Primary empirical supportLimiting / contrary
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Gagan Bansal; Tongshuang Wu; Joyce Zhou; Raymond Fok; Besmira Nushi; Ece Kamar; Marco Tulio Ribeiro; Daniel S. Weld · 2021 · Primary research
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
Primary empirical supportLimiting / contrary
The impact of AI errors in a human-in-the-loop process
Ujué Agudo; Karlos G. Liberal; Miren Arrese; Helena Matute · 2024 · Primary research
Primary empirical supportLimiting / contrary
Effect of Uncertainty-Aware AI Models on Pharmacists' Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial
Corey Lester; Brigid Rowell; Yifan Zheng; Zoe Co; Vincent Marshall; Jin Yong Kim; Qiyuan Chen; Raed Kontar; X. Jessie Yang · 2025 · Primary research
Limiting / contraryOfficial boundary context
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Elham Tabassi; National Institute of Standards and Technology · 2023 · Official authority
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 Power-specific tutorial is available at /tutorials/pwr-197/. 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.