Deterministic classical-statistics sensors applied to AI-system log data. No AI in the analytical pipeline -- every finding derives from peer-reviewed statistical methods that predate the AI era by 50 to 125 years. Independent-verifier methodology built to survive the higher Rule 702 admissibility bar that courts started enforcing after the 2025 AI-hallucinated-expert exclusions.
External validation of the “no AI in the analytical pipeline” discipline: Angela Shi’s July 2026 empirical catalog of embedding failure modes documents that stronger AI models do not fix the structural failures — they move the failure line. Her line: “the fix is not a stronger embedding model.” The Rule 702-defensible path is deterministic classical statistics applied to the AI’s output, not more AI applied to audit the AI.
Every sensor rests on the same analytical foundation: deterministic classical statistics applied to AI-decision output streams. The pipeline itself uses no AI. Every measurement uses peer-reviewed statistical primitives that predate the AI era by 50 to 125 years -- Kullback-Leibler divergence (1951), Fisher variance-ratio F-test (1920s), chi-square goodness-of-fit (Pearson 1900), Cohen's kappa (1960), signal-detection ROC analysis (1950s). The unifying pattern is predictor-corrector: a predictor produces the expected distribution (from disclosure, spec, baseline, or legally-mandated equal-treatment), KL divergence measures the departure of actual from predicted, and pre-registered thresholds trigger the corrector-need signal. Same shield across every sensor.
| Sensor / methodology | Best-fit case type |
|---|---|
| Distributional-shape sensor | Disparate-impact in AI-driven hiring, healthcare denial, insurance claims, lending, housing, criminal-justice |
| Retrieval-auditor | LLM copyright / training-data reproduction; RAG-grounded response integrity |
| Prompt-drift sensor | Product-liability where AI behavior changed silently; vendor breach-of-contract on performance; silent-divergence and silent-alignment findings |
| Tool-call-grader | Autonomous-agent tort (self-driving, robo-adviser, autonomous medical devices, trading bots) |
| Predictor-corrector | Fiduciary-breach and disclosure-alignment matters; disclosure-vs-actual departure quantification; automated-decision cases where the corrector layer failed |
| Router-drift sensor | Multi-model AI systems (fintech routing, healthcare triage cascades, agent-orchestration) |
| Trade-execution drift sensor | Robo-adviser fiduciary breach; PFOF matters; order-routing litigation; best-execution-obligation enforcement |
| Prior-authorization decision drift sensor | Healthcare AI (Medicare Advantage post-acute denial, nH Predict shape) |
| Underwriting drift sensor | Fair-lending disparate-impact; P&C insurance AI; AI-driven credit-scoring discrimination |
| Ranking / marketplace-suppression drift sensor | Antitrust, self-preferencing, marketplace-suppression securities matters |
| Ad-targeting-eligibility drift sensor | HUD-shape ad-discrimination; algorithmic-amplification MDL matters |
| Content-moderation drift sensor | Section 230 design-not-content challenges; viewpoint-neutrality claims; MDL 3047-adjacent matters |
| Independent-verifier declaration | Rule 702 admissibility protection across all of the above (load-bearing after 2025 Kohls-family exclusions) |
Additional sensor variants (cohort-eligibility drift, consent-drift, SLA-drift, retention-decision drift, access-control drift) are available on matter-specific inquiry.
Written analysis. Your System Cannot Testify About Itself — why “our system does not retain that” is a claim about the fielded system rather than the design, why verbose logging and redaction pull against each other, and what an organisation would have to hold in order to answer the question at all. Written for the people who build and operate these systems, not for one side of a dispute.
Mobley v. Workday: Inside the AI-Screening Merits Phase — case spotlight on the methodology problem, vendor-liability structure and Rule 702 gatekeeping in the leading AI-hiring discrimination matter.
Every published sample uses the same methodology structure that would apply to your matter's log data. Different verticals, identical detection shape.
Healthcare Snapshot sample SC P&C insurance Snapshot sample All 11 vertical samples
Bring the case name, docket number, defendants, side, and one paragraph on the AI system in question. I'll bring the arsenal-fit read and honest scoping.
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