Assessing behavioural risk in AI systems
Explore findings from a study of 314 practitioners who assessed the behavioural risks of AI systems using our assessment framework.
Discover where organisations report strengths in governance, where operational controls may be lacking, the human factors shaping AI risk, and opportunities to strengthen responsible AI adoption.
View ReportEvaluate the behavioural risks of your AI system.
Our free Behavioural AI Risk Assessment takes around 10 minutes and provides an immediate behavioural risk profile with practical insights to support responsible AI.
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How We Help
Behavioural Impact Assessment
Assess how people interact with your AI system and identify behavioural risks affecting trust, decision-making, adoption, and wellbeing.
Human-AI Interaction Design
Design AI and agentic experiences that are intuitive, trustworthy, and aligned with human needs and business objectives.
Safeguards, Governance & Readiness
Establish governance frameworks, product safeguards, and operational controls to support responsible, compliant AI deployment.
Responsible AI in child-focused edtech: lessons from Unomundi
Building trustworthy AI for children through developmental psychology, human–AI interaction, and ethical governance.
Read the Case StudyAssessing Behavioural Risk
We assess behavioural risk as part of the broader socio-technical system in which AI operates. Our approach evaluates not only the AI itself, but also the context in which it is deployed, its influence on human behaviour, the governance mechanisms surrounding it, and the controls needed to ensure safe, trustworthy outcomes.
Context
- •Use case & field of application
- •Audience (general public, enterprise, vulnerable people, minors, etc.)
- •Type of AI (predictive, conversational, agentic)
- •Risk tier & classification (e.g. EU AI Act prohibited / high / limited)
- •Decision stakes, autonomy level & reversibility
- •Data sources, provenance & lawful basis
- •User goals & tasks
- •Deployment environment
- •Criticality of decisions
Behavioural Impact
- •Trust & appropriate trust calibration
- •Adoption & acceptance
- •Risk of misinterpretation
- •Delegation of judgement
- •Automation bias & deskilling
- •Behavioural change
- •Manipulation or undue influence
- •Psychological harm
- •Disproportionate impact on different user groups
Governance
- •Clear accountability of AI actions
- •Escalation paths
- •Incident tracking
- •Change approval process
- •Policies
- •Data protection obligations
- •Alignment with broader & field-related regulatory frameworks
- •Bias & fairness audits
- •Documentation & regulatory evidence (DPIA, conformity assessments, model cards)
- •Defined roles & responsibilities + periodic review
- •Human accountability
- •AI literacy & staff training
Controls
- •Transparency & explainability (uncertainty, limitations, risks made clear)
- •Human oversight & intervention; high-risk actions are never fully automatic
- •Reversibility: easy to undo / correct actions, with safe fallback
- •Tested in real conditions, including red-teaming before launch
- •Ongoing testing and monitoring after launch
- •Contestability: users can challenge or appeal a decision
- •User feedback mechanisms
- •Logging & auditability
- •Fail-safe behaviour / graceful degradation
Building AI you can trust starts with understanding behavioural risk.
Work with us to assess your AI system, understand how people interact with it, strengthen governance, and design AI that people can trust.
Through the professional affiliations of its founder, HCRAI actively contributes to the international responsible AI community.