AI Verification Behaviour Assessment
Measure whether employees take proportionate steps to check, challenge and corroborate AI-generated information before relying on it.
Explore the assessment
Recognising uncertainty is not enough — people must act on it
Employees may understand that AI can be wrong yet fail to carry out the checks needed before using an output in consequential work. Verification is visible in actions: checking a source, recomputing a figure, comparing evidence, or obtaining informed human review.
- Identify which claims require checking
- Select a verification method suited to the claim
- Use primary or authoritative evidence where available
- Act appropriately when verification fails
Higher vs lower verification capability
AI Verification Behaviour is the observable tendency to test, corroborate or validate material AI-generated information before relying upon it.
Higher capability
Targets checks at material claims and assumptions.
- Uses sources capable of genuinely confirming the claim
- Distinguishes independent evidence from repeated AI output
- Escalates or withholds reliance when verification fails
- Matches verification effort to potential consequences
Lower capability
Accepts plausible language as evidence.
- Asks the same AI system to confirm itself
- Checks trivial details but misses decisive claims
- Uses weak or derivative sources
- Continues to rely on an unverified conclusion
How the AI Verification Behaviour Assessment works
Structured simulations reveal behaviour under uncertainty and competing time pressure.
AI-generated claim
A workplace scenario includes a claim, figure or recommendation from an AI system.
Verification choice
The participant decides what to check, how, and using what evidence.
Behavioural evidence
Response choices reveal whether checks are targeted, proportionate and genuinely independent.
Verification profile
Reports identify strengths, superficial-checking risk and development priorities.
What the assessment can measure
The exact framework can be tailored to the target roles and AI use cases.
Claim Identification
Recognises which statements, calculations or assumptions are material enough to verify.
Source Checking
Uses relevant primary, authoritative or independent evidence.
Triangulation
Compares information across genuinely independent routes.
Calculation Checking
Recomputes or tests quantitative outputs where appropriate.
Contradiction Handling
Responds constructively when sources or outputs disagree.
Proportionate Assurance
Matches verification depth to uncertainty and consequence.
Verification within the RWA AI Judgement Framework
AI Verification Behaviour overlaps with two primary constructs — Information Credibility Evaluation and Human Oversight Behaviour — shown here alongside the full six-construct framework.
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.
Checking an AI-generated recommendation before presenting it
Example only — not a live scored item.
An analyst receives an AI-generated market comparison containing persuasive claims, several figures and references that appear credible. The analyst has limited time and must decide which elements to check, what evidence to use, and whether the recommendation is ready to share.
What the response reveals
- Prioritises decision-critical claims
- Uses genuinely independent evidence
- Checks referenced sources rather than trusting citation format
- Changes reliance when claims cannot be confirmed
Sample verification behaviour profile
Reports identify strengths, superficial-checking risk and development priorities.
Checks decision-critical claims well; verification of routine claims is sometimes superficial 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.
What the assessment is not designed to measure
Not general scepticism
Blanket distrust of AI is not stronger verification behaviour.
Not fact recall
The assessment does not depend mainly on knowing the correct answer in advance.
Not credibility evaluation alone
Judging that a claim is doubtful is distinct from carrying out an effective check.
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.
View →
ISO/IEC 42001:2023
The international AI management-system standard, covering governance, policies, accountability and risk management.
View →
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.
View →
Standards for Educational & Psychological Testing
Widely recognised guidance on validity, reliability, fairness, score interpretation and appropriate assessment use.
View →
Psychometric design before assessment technology
RWA develops bespoke psychometric assessments beginning with the construct and the decision it must support.
Construct definition first
Verification is specified and bounded from Credibility Evaluation and Oversight before content is built.
Bespoke scenario design
Scenarios reflect the organisation’s real evidence-checking demands.
Evidence-led positioning
Reported as a secondary construct until pilot evidence supports independent measurement.
Full development pathway
From construct definition through pilot, validation and reporting.
Explore related RWA AI assessment services
Develop an AI Verification Behaviour Assessment
RWA can develop a focused secondary-construct assessment or incorporate it into a broader AI judgement diagnostic.
Book a consultation
AI Verification Behaviour Assessment FAQs
What is an AI Verification Behaviour Assessment?
It assesses whether a person carries out appropriate checks before relying on material AI-generated information.
Which primary constructs does verification overlap with?
Information Credibility Evaluation and Human Oversight Behaviour.
How is verification different from credibility evaluation?
Credibility evaluation concerns judging whether information deserves belief; verification concerns the actions used to test or corroborate it.
Does stronger performance mean checking everything?
No. Strong verification is proportionate and targeted at claims that are material, uncertain or consequential.
Can the assessment be used independently?
Potentially, where unchecked AI information is a specific risk, subject to evidence supporting the intended use.
How do we begin?
RWA can discuss target roles and AI use cases and recommend the most appropriate assessment format.