Aethelgard Python SDK
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.
Why Aethelgard?
| 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) |
Installation
pip install aethelgard-ai
For benchmarking utilities:
pip install aethelgard-ai[benchmarks]
Quick Start
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']}")
Supported Regulations
| 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 |
Key Features
Formal Verification (Z3 SMT Proofs)
Every audit produces machine-checkable mathematical proofs — not statistical estimates.
Intersectional Analysis
Automatically discovers and tests all combinatorial demographic intersections.
Multi-Regulation Crosswalk
Map compliance findings across frameworks (e.g., LL144 → EU AI Act → NIST AI RMF).
Zero-Knowledge Proofs
Prove fairness compliance without revealing model weights or training data.
Continuous Monitoring
Real-time drift detection with confidence-weighted alerts and breach forecasting.
API Reference
See the full documentation.
Benchmarks
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
Citation
@software{aethelgard2026,
author = {Ashuraliyev, Abduxoliq},
title = {Aethelgard: Formal Verification for AI Fairness Compliance},
year = {2026},
url = {https://github.com/ProgrmerJack/Aethelgard},
}
License
Apache License 2.0. See LICENSE.
Metadata
Release files for aethelgard-ai 1.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| aethelgard_ai-1.2.0.tar.gz | 23.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| aethelgard_ai-1.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 50.2 kB
Release files / aethelgard_ai-1.2.0.tar.gz
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|---|---|
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| Tags | Source |
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