Frameworks
Frameworks that make the human side of AI measurable.
Behavieural's frameworks turn behavioral science into constructs that can be measured, diagnosed and acted on. Each is grounded in named research and built to produce evidence, not just description.
The papers argue. The frameworks measure. A paper makes the case that AI adoption, oversight or governance fails for behavioral reasons. A framework is the instrument for finding out whether that is happening in a specific organization, and where.
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AI Trust Axis
A behavioral framework for the failure modes that stall AI adoption and weaken oversight.
The AI Trust Axis organizes the behavioral foundations of AI use into five dimensions, each reflecting a psychological process that shapes how people interpret, trust and integrate AI. It explains why technically strong systems still struggle under real operating conditions, and it turns that explanation into evidence. A purpose-built survey scale, structured interviews and workflow observation work together to show where trust is strained, why and how it appears in practice. The result is a Trust Integrity Score for deployed systems or a Trust Readiness Score ahead of deployment, tied to the specific failure modes present. The framework and its scale are under peer review, and the scoring methodology is proprietary.
Key highlights
Five dimensions of the human–AI relationship, measured with the AI Trust Axis Scale
Seven recurring failure modes: automation bias, algorithm aversion, resistance to workflow change, shadow AI use, escalation avoidance, performative human-in-the-loop review and local workarounds
Grounded in Mental Model Theory, Self-Determination Theory, Procedural Justice Theory, Cognitive Load Theory and Trust Violation and Repair Theory
Findings map to targeted changes in workflow design, trust architecture, behavioral change management, and governance and recovery
The same framework applies across sectors, with diagnostics and interventions tailored to each