Eliminate Model Blind Spots with Multi-Algo Consensus
Most explainability tools give you one answer. ClarifAI runs SHAP, LIME, and surrogate models simultaneously — then scores consensus across them. When all three agree, you can trust the explanation. When they diverge, you get flagged before regulators do.
The Trust Gap: Single-Model Fallibility
Enterprises deploying credit, fraud, and diagnostic AI models face a fundamental dilemma: regulators demand explanations, but any single explanation method can be gamed, cherry-picked, or simply wrong. A SHAP explanation that looks reasonable may contradict what LIME reveals — and neither tells you which one to believe. When a decision gets challenged in court or by an auditor, "our SHAP values looked fine" is not a defence.
Four-Layer Consensus Pipeline
Every prediction passes through four sequential stages before an explanation is certified as trustworthy.
Multi-Algorithm Parallelization
ClarifAI runs SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and a surrogate decision-tree model simultaneously on the same prediction. No single method is trusted in isolation.
Consensus Scoring
A proprietary consensus score (0–100) measures the agreement between all three explanation outputs. Scores above 85 are certified as trustworthy; scores below 60 trigger an automatic investigation flag and alert the model owner.
Surrogate Model Approximation
For high-stakes decisions, an interpretable surrogate model (decision tree or linear regression) is trained on local neighbourhood samples around the prediction point. This provides a human-readable decision path that auditors can verify without ML expertise.
SHAP & LIME Decomposition
Feature attributions from SHAP and LIME are decomposed into global (population-level) and local (instance-level) importance rankings. Any feature that appears in the top-3 for SHAP but not LIME is flagged as a potential proxy variable and routed to the fairness module.
Capabilities Built for Enterprise Explainability
Ensemble Validation
Run SHAP, LIME, and surrogate models simultaneously and receive a single consensus score per prediction. Eliminate the guesswork of choosing which explainability method to trust.
Global Explainability (SHAP)
Understand which features drive your model's decisions at the population level. Identify systemic biases and document model behaviour for regulatory submissions.
Local Interpretability (LIME)
Explain individual predictions with confidence. When a loan is denied or a fraud alert fires, LIME provides the neighbourhood-level reasoning that makes sense to compliance teams.
Surrogate Benchmarking
Automatically train a lightweight decision tree that mirrors your black-box model's behaviour. Audit-ready, human-readable, and deployable as a regulatory fallback.
Conflict Detection
When SHAP and LIME disagree on feature importance, ClarifAI surfaces the conflict in the dashboard and routes it to the RAI module for proxy variable analysis — before it becomes a compliance issue.
Regulatory Report Export
One-click export of explanation certificates in formats accepted by RBI FREE-AI, EU AI Act Article 13, and IRDAI guidelines. Each certificate is signed with a model version hash.
The Mathematics Behind Shapley Values
ClarifAI's SHAP implementation is grounded in cooperative game theory. Each feature's attribution is computed as its average marginal contribution across all possible feature coalitions — the only attribution method with guaranteed consistency, local accuracy, and missingness properties simultaneously.
Shapley Value Formula
φᵢ(v)Shapley value — the fair credit assigned to feature iSA subset of all features, not including feature inTotal number of input featuresv(S∪{i}) − v(S)Marginal contribution of feature i to subset SReady to Certify Your Model Explanations?
Schedule a 30-minute technical walkthrough and see consensus scoring on your own model within the first session.