Psychologically Safe AI Infrastructure
A psychologically safe, agency-building AI system is a governed behavioural change system that uses bounded, scientifically defensible measurement to recommend constraint-aware actions, with transparent explanations, human oversight, and evidence-weighted learning, so that people become more capable and more autonomous over time rather than more dependent on the system.
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About This ServicePsychologically Safe AI Infrastructure
This service addresses the foundational architecture that people-impact AI systems require but rarely receive. We work with system designers and product teams to ensure every AI that measures, predicts, or influences human outcomes protects the six trainable human capacities defined by the AI-IARA framework: Awareness, Interpretation, Intention, Action, Relational Agency, and Autonomy — with Integrity by design as the operational backbone.
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The AI-IARA Integrity Model
Six capacities describing what the system must protect and build, not what it must persuade users to feel.
Awareness
The system helps people notice when an algorithm is shaping attention, emotion, or judgment, rather than hiding that influence.
Interpretation
The system scaffolds user meaning-making and treats AI outputs as hypotheses, so the person does not outsource appraisal and sensemaking.
Intention
The system supports values-first goal clarity before optimization, so defaults and rankings do not silently replace what the person actually cares about.
Action
The system preserves productive friction and mastery experiences through graduated support that fades as competence grows, rather than automating effort away.
Relational Agency
The system supports human connection without substituting for it, and routes people toward real-world relationships and appropriate human support when needed.
Autonomy
The system enables people to consciously calibrate reliance, with clear controls to reduce delegation, override recommendations, and reset habits of dependence.
Design Principles and Evaluation Criteria
Eight pillars that define what a psychologically safe AI system must demonstrate.
Our Approach
A structured, step-by-step methodology tailored to every engagement.
Define Scope and Harm Model
We specify intended outcomes, excluded use cases, foreseeable harms, and escalation boundaries before building features.
Translate Agency to Requirements
Build Measurement Governance
Implement Safeguards
Evaluate and Monitor
| Step | Title | Description |
|---|---|---|
| 01 | Define Scope and Harm Model | We specify intended outcomes, excluded use cases, foreseeable harms, and escalation boundaries before building features. |
| 02 | Translate Agency to Requirements | We convert the AI-IARA capacities into design requirements and review criteria so psychological safety is inspectable during design reviews. |
| 03 | Build Measurement Governance | We define constructs, uncertainty, validity, and no-false-precision reporting, then justify every data element against purpose. |
| 04 | Implement Safeguards | We encode user control, explainability, opt-out pathways, and non-manipulative choice architecture, including protections against over-reliance and automation bias. |
| 05 | Evaluate and Monitor | We run pre-deployment evaluation for performance and harms, then post-deployment monitoring, incident response, and periodic re-validation as contexts shift. |
Who This Is For
This service is designed for organisations and teams navigating the intersection of AI, people, and accountability.
AI Product Teams
Teams building systems that assess or influence human psychological states who need safety-by-design.
Digital Health Companies
Companies developing AI-powered therapeutic or diagnostic tools that must protect vulnerable users.
Organisations Deploying People-Assessment AI
Organisations using AI in high-stakes contexts like hiring, performance, or welfare decisions.
Investors and Board Members
Stakeholders conducting due diligence on AI companies making claims about human outcomes.
Public Sector Bodies
Government bodies deploying AI in welfare, justice, education, or health where public trust is essential.
Frameworks & Standards
Every engagement is anchored to recognised standards and frameworks for accountability and rigour.
Engagement model: Typical engagement runs 6 to 12 weeks depending on system complexity. Work spans the full lifecycle from construct definition through deployment architecture to post-deployment monitoring.
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Let\u2019s discuss how this service can support your needs.