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Feature overview

Which guide do I need?

graph TB
    Start[What do you need?]
    Start --> Dev{Implementing?}

    Dev -->|Yes| DevType{Your focus?}
    DevType -->|CI/CD| MLEng[ML Engineer Guide]
    DevType -->|Notebooks| DataSci[Quickstart + Custom Data]
    DevType -->|Validation| Validator[Validator Guide]

    Dev -->|No| Role{Your role?}
    Role -->|Compliance| CompOff[Compliance Officer Guide]
    Role -->|Industry needs| Industry{Industry?}
    Industry -->|Banking| Banking[Banking + SR 11-7]
    Industry -->|Insurance| Insurance[Insurance Guide]
    Industry -->|Healthcare| Healthcare[Healthcare Guide]
    Industry -->|Fraud| Fraud[Fraud Detection]

    Role -->|Exploring| Explore[Start with Quickstart]

    style MLEng fill:#d4edda
    style DataSci fill:#d4edda
    style Validator fill:#d4edda
    style CompOff fill:#d4edda
    style Explore fill:#e1f5ff

Quick navigation:

Quick reference

GlassAlpha capabilities and where to learn more:

Feature Guide Reference Compliance
Group Fairness Configuration Fairness Metrics SR 11-7 §V
Intersectional Fairness Configuration Fairness Metrics SR 11-7 §V
Individual Fairness Configuration Fairness Metrics SR 11-7 §V
Dataset Bias Detection Guide - SR 11-7 §III.C.2
Calibration Configuration Calibration Reference SR 11-7 §III.B.2
Robustness Configuration Robustness Reference EU AI Act Art. 15
Shift Testing Shift Testing Guide - SR 11-7 §III.A.3
Reason Codes Reason Codes Guide - ECOA §701(d)
Preprocessing Preprocessing Guide - SR 11-7 §III.C.1

Why choose GlassAlpha?

How GlassAlpha compares

Feature GlassAlpha Fairlearn AIF360 Commercial Tools
Audit PDFs ✅ Professional, deterministic ❌ No reports ❌ No reports ✅ $$$
Custom Data in 5 min ✅ Yes ⚠️ Complex setup ⚠️ Complex setup ⚠️ Support needed
Built-in Datasets ✅ Multiple ready to use ❌ None ⚠️ Few ✅ Limited
Model Support ✅ XGBoost, LightGBM, sklearn ⚠️ sklearn only ⚠️ Limited ✅ Varies
Deterministic Results ✅ Byte-identical (same platform+Python) ⚠️ Partial ❌ No ⚠️ Varies
Offline/Air-gapped ✅ 100% offline ✅ Yes ✅ Yes ❌ Requires internet
Cost ✅ Free (Apache 2.0) ✅ Free (MIT) ✅ Free (Apache 2.0) 💰 \(5K-\)50K+
Regulatory Ready ✅ Audit trails + manifests ❌ No trails ❌ No trails ✅ $$$
Learning Curve ✅ Quick start available ⚠️ Steep ⚠️ Steep ⚠️ Training needed

Bottom line: GlassAlpha is the only OSS tool that combines professional audit PDFs, easy custom data support, and complete regulatory compliance.

Designed for regulatory compliance

  • Deterministic outputs - Byte-identical HTML reports on same platform+Python+config
  • Complete lineage - Git SHA, config hash, data hash, seeds recorded
  • Professional formatting - Publication-quality reports with visualizations
  • Audit trails - Immutable run manifests for regulatory submission

Note: HTML reports are byte-identical across platforms when using the same Python version and config. PDFs may have minor visual differences across platforms due to font rendering and layout engine variations, but are suitable for human review and regulatory submission. See determinism guide for details.

See compliance mapping →

On-premise first design

  • No external dependencies - Runs completely offline
  • File-based approach - No databases or complex infrastructure needed
  • Full reproducibility - Immutable run manifests for audit trails
  • Air-gapped compatible - Works without internet access

See trust & deployment details →

Simplicity as a core principle

  • Single command - glassalpha audit handles everything
  • YAML configuration - Policy-as-code for compliance requirements
  • Fast execution - 2-3 seconds for demo mode, 5-60 seconds for full audits
  • Clear errors - Actionable messages with fix suggestions

See configuration guide →

Supported models

Model Type Status Notes
XGBoost Production TreeSHAP integration optimized
LightGBM Production Native integration available
Logistic Regression Production Full scikit-learn compatibility

Additional model types available through extension framework.

See model selection guide →

Example configuration

Working configuration structure:

# Direct configuration example

data:
  path: data/german_credit_processed.csv
  target_column: credit_risk
  protected_attributes:
    - gender
    - age_group
    - foreign_worker

model:
  type: xgboost
  params:
    objective: binary:logistic
    n_estimators: 100
    max_depth: 5

explainers:
  strategy: first_compatible
  priority:
    - treeshap
    - kernelshap

metrics:
  performance:
    metrics:
      - accuracy
      - precision
      - recall
      - f1
      - auc_roc
  fairness:
    metrics:
      - demographic_parity
      - equal_opportunity

reproducibility:
  random_seed: 42

This configuration format supports deterministic, reproducible audits.

See full configuration reference →

Contributing

We welcome contributions to enhance GlassAlpha's capabilities:

Enhancement areas

  1. Additional models - Neural networks, time series, custom integrations
  2. Advanced explanations - Counterfactuals, gradient methods, interactive visuals
  3. Extended compliance - Additional frameworks, custom templates, industry metrics
  4. Performance - Large dataset optimization, parallel processing
  5. Documentation - Examples, tutorials, best practices

See contribution guidelines →