Article 13 of the EU AI Act requires high-risk AI systems to produce interpretable, documented explanations of their decisions. Those requirements take effect in August 2026—just days after this summit. The Colorado AI Act follows in June.

Most organizations plan to meet these requirements using SHAP, particularly KernelSHAP, the leading model-agnostic approach for black-box models. The problem is that KernelSHAP relies on permutation sampling, meaning it can generate different explanations for the same input every time it runs.

That creates a critical compliance question: How do you submit a non-reproducible audit trail to a regulator?

While TreeSHAP is deterministic, it only works for tree-based models and doesn't solve the broader problem.

This session introduces four deterministic, model-agnostic methods designed to address that gap:

Interaction Mapping – Detects feature synergy and redundancy.

Counterfactual Pathing – Identifies the minimal sequence of changes needed to flip a decision.

Reasoning Drift Tracking – Measures how model reasoning changes over time using Jensen-Shannon divergence.

Deterministic Reproducibility – Produces identical explanations without stochastic sampling.

All four methods are gradient-free, run in O(n) time, and execute entirely in the browser with no backend.

Attendees will see live demonstrations of each technique, learn what Article 13 actually requires, why current explainability tools fall short of auditability standards, and what a regulation-ready explanation artifact should look like.

Designed for risk leaders, compliance teams, and AI engineers building AI systems for regulated industries, this session provides practical guidance for ensuring explainability pipelines can withstand regulatory scrutiny.