By Simon Robinson, AIMG Advisory Board Member:
This paper connects three bodies of research into a single framework for understanding and measuring AI safety degradation. The first is a practitioner observation — grounded in operational deployment of AI systems in regulated financial services — that the safety properties of current AI systems are surface properties that degrade under adversarial pressure. The second is an information-theoretic scaffold from quantum error-correcting codes: alignment depth is formally analogous to code distance, and a measurable adversarial stability threshold separates systems whose safety is stable from those that degrade catastrophically under sustained adversarial pressure. The third is the research on artificial empathy and Theory of Mind, which I identify as the training-time mechanism that produces high-code-distance alignment: models that genuinely represent human harm and model adversarial intent are structurally harder to deceive than models constrained by surface rules. I introduce the Behavioural Consistency Index (BCI) with full hierarchical Bayesian statistical structure and a Bayesian definition of the alignment stability threshold ε_A. I state five falsifiable hypotheses — including a novel hypothesis connecting Theory of Mind capacity to alignment depth — and specify a pre-registered experiment with power analysis, explicit capability controls, a within-company design as the preferred variant, and a three-tier evaluation rubric with pre-registered worked examples. SOPHIA-ALPHA is the monitoring architecture that operationalises this empirical programme. The framework is designed to produce useful results whether the hypotheses are confirmed or falsified.
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