Existing approaches to AI ethics certification rely on static audits, fairness metrics, and human oversight committees, lacking mechanisms for real-time, objective monitoring of emerging ethical risk. We introduce a dynamic framework for ethical stability assessment grounded in the Karimov–Alekberli (KA) thermodynamic framework. The central contribution is the Ethical Stability Metric (ESM), a multi-channel causal entropic response that measures deviations in decision-relevant entropy from an adaptive baseline, augmented by a cross-channel coupling term that captures structural divergence between protected groups. We position our contribution precisely: we do not formalize ethics itself; we formalize the loss of ethical stability. Proposition 1 (proof sketch) states that ethical violations are preceded by measurable destabilization in multi-channel decision entropy—a testable, falsifiable claim supported empirically; full formal proof is deferred to future work. Validation across three settings demonstrates ESM’s leading-indicator capability: a synthetic credit scoring study (ESM leads demographic parity by 12 steps; median 5 steps across 50 Monte Carlo trials); a COMPAS-reconstruction retrospective (ESM detects structural bias escalation within 2 months of deployment; ADWIN and KSWIN: zero detections); and a German Credit age-bias drift study (9-month lead time). A regulatory mapping to EU AI Act Articles 9, 13, 15, and 72 proposes a certification workflow from static compliance to dynamic, continuous ethical monitoring.
Karimov et al. (Mon,) studied this question.