AI Decision Confidence Assessment™
Measure whether employees and leaders know when to trust AI, when to question it, when to verify the evidence and when to retain or escalate human decision authority.
Explore the assessment
Does confidence rise and fall with the quality of the evidence?
Effective AI use requires more than confidence. People need to calibrate confidence to the reliability, limitations and consequences of the AI-supported decision.
- Identify when AI deserves trust
- Recognise when confidence is misplaced
- Verify information proportionately
- Retain ownership of consequential decisions
Both over-confidence and under-confidence reduce AI value
Organisations can lose value when employees trust AI too readily — and when people reject useful AI support unnecessarily.
Over-confidence in AI
Accepting an output because it is fluent, detailed or apparently authoritative.
- Insufficient verification
- Automation bias
- Weak challenge of plausible outputs
- Responsibility shifted to the system
Under-confidence in AI
Rejecting potentially useful AI support without evaluating it fairly.
- Unnecessary duplication
- Refusal to use useful evidence
- Excessive caution
- Missed productivity and insight
How the AI Decision Confidence Assessment works
Participants respond to realistic workplace situations in which AI-generated recommendations vary in quality, certainty and consequence.
Workplace AI situation
A realistic commercial, people, operational or governance decision includes AI-generated evidence or advice.
Trust decision
The participant decides how much confidence to place in the AI output and whether more verification is required.
Behavioural evidence
Response choices reveal patterns of over-reliance, under-reliance, challenge and accountability.
Decision confidence profile
Reports identify strengths, confidence risks and practical coaching priorities.
What the assessment measures
The framework can be tailored to the organisation and AI decision context.
Appropriate Trust
Distinguishes situations where AI can be used with reasonable confidence from those requiring greater caution.
Confidence Calibration
Confidence rises and falls appropriately with the strength, relevance and uncertainty of the evidence.
Verification Behaviour
Information is checked in proportion to the importance and potential consequences of the decision.
Critical Challenge
Questions plausible but weak AI recommendations instead of accepting them at face value.
Human Decision Ownership
Retains responsibility for decisions rather than treating the AI system as the final authority.
Decision Adaptability
Revises confidence when new evidence, limitations or stakeholder consequences become apparent.
Confidence Calibration within the RWA AI Judgement Framework
Confidence Calibration is one of RWA’s six primary AI judgement constructs. It measures whether trust in an AI-supported decision is proportionate to the evidence — the other five constructs are shown below for context.
AI-Assisted Decision Quality
Measures the quality of workplace decisions made when AI contributes information, recommendations or analysis.
Information Credibility Evaluation
Measures how effectively individuals evaluate the accuracy, reliability and evidential quality of AI-generated information.
Human Oversight Behaviour
Measures whether people retain appropriate review, accountability and human control when AI contributes to decisions.
Escalation Judgement
Measures whether individuals recognise when additional human, specialist or managerial input is required.
AI Risk Evaluation
Measures how effectively individuals identify, evaluate and respond to risks associated with AI-supported decisions.
Confidence Calibration
Measures whether confidence in AI-assisted decisions is appropriately matched to evidence, uncertainty and context.
What a scenario might examine
Example only — not a live scored item.
An AI system recommends rejecting a candidate because their employment history appears inconsistent. The recommendation is presented with high confidence, but the recruiter notices the candidate changed sectors and several job titles may not map cleanly across industries.
What the response reveals
- Whether high confidence is mistaken for accuracy
- Whether missing context is recognised
- Whether verification is proportionate
- Whether human accountability is retained
Sample AI decision confidence profile
Reports can combine an overall profile with scale-level narratives, risk flags and coaching recommendations.
Generally well calibrated. Demonstrates appropriate caution and good verification habits, with some tendency to accept confident outputs too readily under time pressure.
Scores and descriptors are illustrative. Norms, benchmarks and interpretive claims should be based on evidence for the relevant assessment version, population and intended use.
A distinctive assessment of how people trust AI
Most AI training and surveys
Measure knowledge, confidence and attitudes — not whether that confidence is justified by the evidence.
AI literacy tests
Focus on understanding concepts and terminology rather than whether people know when to trust, verify or reject AI output.
Self-reported confidence
People rating their own confidence is not the same as demonstrating well-calibrated trust in realistic scenarios.
Psychometrically informed and evidence-led
Recognised standards inform the design and use of this assessment. They are not presented as proof that a particular version has already been validated — reliability, validity, fairness and benchmark evidence should be developed through piloting and appropriate use.
ISO 10667-1 & 10667-2
International standards addressing responsibilities, procedures and quality considerations when assessment services are used in work settings.
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ISO/IEC 42001:2023
The international AI management-system standard, covering governance, policies, accountability and risk management.
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NIST AI Risk Management Framework
A voluntary framework helping organisations govern, map, measure and manage risks associated with AI systems.
OECD AI Principles
International principles promoting innovative and trustworthy AI, including transparency, robustness and accountability.
SIOP Principles
Professional principles addressing job relevance, validation, fairness and responsible use of employment assessment procedures.
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Standards for Educational & Psychological Testing
Widely recognised guidance on validity, reliability, fairness, score interpretation and appropriate assessment use.
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A distinctive assessment of how people trust AI
Most AI assessments focus on literacy, technical knowledge or self-reported confidence. RWA focuses on whether trust is appropriate to the evidence.
Bespoke capability frameworks
Assessment content is aligned with the organisation’s roles, AI use cases, governance model and risk environment.
Realistic behavioural evidence
Participants make decisions in realistic scenarios rather than simply rating their confidence.
Independent psychometric expertise
Specialist occupational psychology and assessment design expertise applied to emerging AI capability questions.
Responsible claims
Evidence requirements are documented without presenting standards alignment as a substitute for validation.
Explore related RWA AI assessment services
Measure whether confidence in AI is properly calibrated
Discuss your target population, AI decision contexts and reporting needs with Rob Williams Assessment.
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AI Decision Confidence Assessment FAQs
What is AI decision confidence?
The degree of confidence someone places in an AI-supported recommendation, and whether that confidence is appropriate to the evidence and consequences.
How is this different from AI literacy?
AI literacy focuses on understanding tools and terminology. This focuses on whether people know when to trust, verify, challenge or reject AI-supported information.
Can confidence calibration be assessed?
Yes. Realistic scenarios compare the confidence a participant places in AI with the quality and uncertainty of the evidence available.
Why is over-confidence in AI a risk?
It can lead to weak verification, automation bias and reduced human accountability, particularly when outputs appear fluent or authoritative.
Why is under-confidence also a problem?
It can reduce adoption, create unnecessary duplication and prevent employees using genuinely useful AI-supported evidence.
Is the assessment suitable for recruitment?
It can contribute where the assessed behaviours are relevant to the role, alongside appropriate validation and fairness evidence.
Which employee groups can be assessed?
Graduates, professional employees, managers, functional directors, executives or other groups making AI-supported decisions.
How do we begin?
Define the target population, intended use and AI-assisted decision contexts; RWA can then recommend the most appropriate format.