This in-depth Techellect research paper explores how artificial intelligence earns trust before it earns autonomy. Dr. James L. Norrie introduces the Triple III Model — Information, Influence, Intention — connecting ethics, psychology, and behavioural science to define measurable trust in machine intelligence.
Trust is not an algorithmic feature — it is a relational one. Each paper explores a different layer of human–machine alignment, from context and character to accountability and measurable evidence.
Autonomy should never be a default setting. It is a privilege earned through transparency, fit, and ethical restraint.
The Triple III Model outlines three essential dimensions that govern how AI earns trust:
- Information — Accuracy, explainability, and data integrity are the foundation of credible AI. Transparency in how models are trained and validated establishes the baseline for trust.
- Influence — Behavioural and psychological alignment between human and machine. Systems must adapt to human expectations, not the other way around.
- Intention — The ethical and contextual purpose behind AI design. Decisions must be auditable, reversible, and accountable to human oversight.
Together, these layers create a measurable framework for trustworthy autonomy — ensuring AI systems remain tools of human empowerment, not instruments of disengagement.
Trust is earned, not coded. It emerges when human users consistently experience AI behaviour as reliable, ethical, and comprehensible.
Where traditional governance models treat compliance as static, trustworthy AI demands continuous, relational validation — between machine intent and human impact.
“Through the Triple III Model,” Dr. Norrie argues, “AI must amplify human judgement rather than replace it — earning credibility through evidence, alignment, and ethical restraint.”
Implementing the Triple III Model requires a shift from procedural compliance to principled autonomy.
- Embed transparency into model design so decision pathways can be reviewed and justified.
- Define human-in-the-loop checkpoints for reversibility and accountability.
- Balance capability with restraint — understanding that just because AI can act, doesn’t mean it should.
This approach ensures that autonomy remains earned, not assumed — and that each iteration of AI design strengthens, rather than erodes, digital trust.
Dr. James L. Norrie is the Founder of Techellect.com and CyberconIQ.com, and serves as Professor of Law and Cybersecurity and Founding Dean of the Graham School of Business at York College of Pennsylvania. His research bridges behavioural science and emerging technology, exploring how risk perception shapes digital trust.
