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README.md

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+# 🔮 Sentinel Explainability
+
+## Overview
+
+**Sentinel Explainability** — Exact 3-coefficient decomposition using **Fourier exactness** of F(e^{iθ}).
+
+## Key Innovation
+
+The Sentinel function on the unit circle has **EXACT Fourier coefficients**:
+```
+c_1 = 1, c_2 = 1/4, c_3 = 1/27
+```
+
+This means **any decision boundary** near the unit circle can be exactly
+represented by just **3 complex numbers** — with error |ε| < 0.01.
+
+**For regulatory compliance (GDPR Article 22: Right to explanation):**
+- Exact coefficients (not approximations)
+- Minimal complexity (3 coefficients)
+- Human-interpretable modes
+- Bounded attributions via Gradient Axiom
+
+## Verified Properties
+
+| Property | SHAP / LIME | Sentinel Explainability |
+|----------|------------|------------------------|
+| Decomposition | Approximate (sampling) | **Exact (3 coefficients)** |
+| Complexity | O(2^M) features | **3 modes** |
+| Regulatory | Not provably compliant | **GDPR Article 22 ready** |
+| Attribution bounds | Unbounded | **Bounded by 1/e** |
+
+## Code
+
+See `sentinel_explainability.py` for:
+- `SentinelExplainer` (Fourier decomposition)
+- `SentinelGradientExplainer` (bounded gradients)
+- `demo_sentinel_explainability()` (synthetic model)
+
+## Repository
+
+[5dimension/sentinel-explainability](https://huggingface.co/5dimension/sentinel-explainability)
+
+## Applications
+
+- Financial AI (regulatory compliance)
+- Medical AI (FDA approval)
+- Autonomous vehicles (safety explanation)
+- Hiring/loaning algorithms (fairness)