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Python SDK for the Aethelgard AI Compliance & Fairness Verification Engine — formal proofs, not statistical guesses.
Enterprise AI Compliance & Fairness Verification — Formal Proofs, Not Statistical Guesses.
Aethelgard is the only AI governance engine that uses Z3 SMT formal verification to mathematically prove fairness compliance — not merely estimate it statistically. This SDK provides a Python client for the Aethelgard Engine API.
| Capability | Fairlearn / AIF360 | Aethelgard |
|---|---|---|
| Evidence Type | Statistical p-values | Z3 formal proofs (deterministic) |
| Intersectional Analysis | Manual specification | Exhaustive automatic discovery |
| Regulations | None built-in | 6 frameworks (LL144, EU AI Act, SR 11-7, Colorado, FDA, Texas TRAIGA) |
| Confidence Intervals | Wilson CI only | Wilson CI + Z3 uncertainty-aware verification |
| Remediation | None | Automated minimum-cost remediation plans |
| Monitoring | None | Real-time drift detection with breach forecasting |
| Legal Protection | None | Attorney-client privilege safe harbor |
| Throughput | ~100 rec/s (Python) | 1.23 billion rec/s (Rust engine) |
pip install aethelgard-ai
For benchmarking utilities:
pip install aethelgard-ai[benchmarks]
from aethelgard import AethelgardClient
client = AethelgardClient(
base_url="http://localhost:8080",
api_key="your-api-key",
)
# Run a bias audit (NYC LL144 / EEOC Four-Fifths Rule)
result = client.audit_ll144(
model_name="hiring-model-v2",
groups=[
{"category": {"category_type": "Race", "value": "White"}, "total_candidates": 1000, "selected_candidates": 600},
{"category": {"category_type": "Race", "value": "Black"}, "total_candidates": 800, "selected_candidates": 440},
{"category": {"category_type": "Race", "value": "Hispanic"}, "total_candidates": 600, "selected_candidates": 300},
],
)
for group in result["group_results"]:
print(f"{group['group']:20s} IR={group['impact_ratio']:.4f} Z3={group['z3_verified']}")
| Regulation | Method | Jurisdiction |
|---|---|---|
| NYC Local Law 144 | client.audit_ll144() | New York City |
| EU AI Act (Art 9-15) | client.audit_eu_ai_act() | European Union |
| Federal Reserve SR 11-7 | client.audit_sr117() | US Banking |
| Colorado AI Act (SB 205) | client.audit_colorado() | Colorado |
| FDA 21 CFR 820 | client.audit_fda() | US Medical Devices |
| Texas TRAIGA (HB 149) | client.audit_texas_traiga() | Texas |
Every audit produces machine-checkable mathematical proofs — not statistical estimates.
Automatically discovers and tests all combinatorial demographic intersections.
Map compliance findings across frameworks (e.g., LL144 → EU AI Act → NIST AI RMF).
Prove fairness compliance without revealing model weights or training data.
Real-time drift detection with confidence-weighted alerts and breach forecasting.
See the full documentation.
Reproducible benchmarks comparing Aethelgard to Fairlearn, AIF360, and manual auditing across 10 real-world datasets (40K+ to 2.5M+ records):
pip install aethelgard-ai[benchmarks]
python -m aethelgard.benchmarks
@software{aethelgard2026,
author = {Ashuraliyev, Abduxoliq},
title = {Aethelgard: Formal Verification for AI Fairness Compliance},
year = {2026},
url = {https://github.com/ProgrmerJack/Aethelgard},
}
Apache License 2.0. See LICENSE.
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 1.2.0 | 17 Feb 2026 | 11 |
This describes our collected data; it is not a security assessment.
Observed since 20 Oct, 20:53 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:18043443a5f8c679acb3b41eae3289e71627696ba7b79b52b6d59354f7af5238