Explainable AI (XAI) for Enterprise: SHAP, LIME and Regulated Deployment
Explainable AI is the practice of producing, storing and presenting evidence about why a model produced a given output, in a form the accountable human can act on. Techniques like SHAP and LIME answer the question; logging, versioning and human oversight make the answer auditable.
This guide covers global, local and counterfactual explanations, how SHAP and LIME differ in practice, and what a reference architecture looks like for regulated deployments in healthcare and fintech.
What we deliver
SHAP For Regulated Scoring
Consistent, additive feature contributions; TreeSHAP is fast enough for real-time tabular scoring and maps cleanly to reason codes.
LIME For Investigation
Model-agnostic local surrogates that suit text, images and ad-hoc case review, with sampling variance that rules it out as filed evidence.
Healthcare Deployment
Clinical vocabulary, cohort calibration, subgroup fairness checks and a recorded override path for the clinician.
Fintech Deployment
Adverse action reason codes from SHAP contributions, independent validation, challenger models and monitored thresholds.
Audit-Ready Architecture
Feature lineage, versioned model registry, separate explanation service, immutable decision log and attribution drift monitoring.
LLM And Agent Systems
Retrieval citations, structured rationale fields, full prompt logging and guardrails replace feature attribution for generative systems.
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