Tools & Frameworks

A comprehensive collection of open-source tools, libraries, and frameworks for implementing trustworthy machine learning systems.

Fairness & Bias Mitigation

Comprehensive Toolkits

AI Fairness 360 (AIF360)
IBM Research | Python, R | ⭐⭐⭐⭐⭐

pip install aif360

- Features: 70+ fairness metrics, 10+ bias mitigation algorithms - Best for: Research, comprehensive bias analysis, enterprise applications - Highlights: Web interface, extensive documentation, industry-tested - Example: Credit scoring, hiring decisions, criminal justice

Fairlearn
Microsoft | Python | ⭐⭐⭐⭐

pip install fairlearn

- Features: Scikit-learn integration, dashboard visualization, constraint-based optimization - Best for: Quick prototyping, ML practitioners familiar with sklearn - Highlights: User-friendly API, interactive dashboards, Azure ML integration

Specialized Libraries

Themis
UMass Amherst | Python | ⭐⭐⭐

pip install themis-ml

- Focus: Fairness testing and debugging - Features: Automated bias discovery, causal fairness analysis - Best for: Testing existing models for hidden biases

FairML
Academic | Python | ⭐⭐⭐

pip install fairml

- Focus: Auditing black-box models - Features: Input influence ranking, bias detection without model access - Best for: Third-party model auditing

Robustness & Adversarial Defense

Attack Libraries

Adversarial Robustness Toolbox (ART)
IBM Research | Python | ⭐⭐⭐⭐⭐

pip install adversarial-robustness-toolbox

- Features: 20+ attacks, 15+ defenses, multiple ML frameworks - Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost - Best for: Comprehensive adversarial ML research and testing

Foolbox
University of Tübingen | Python | ⭐⭐⭐⭐

pip install foolbox

- Features: 30+ gradient-based and black-box attacks - Frameworks: PyTorch, TensorFlow, JAX, NumPy - Best for: Quick adversarial example generation, benchmarking

CleverHans
Google Brain | Python | ⭐⭐⭐

pip install cleverhans

- Features: Classic attacks (FGSM, PGD, C&W), TensorFlow focus - Best for: Educational purposes, reproducing classic papers

Defense Frameworks

CROWN
UCLA | Python | ⭐⭐⭐⭐

git clone https://github.com/huanzhang12/CROWN-IBP

- Focus: Certified robustness via interval bound propagation - Features: Formal verification, scalable certified training - Best for: Safety-critical applications requiring guarantees

Auto-Attack
EPFL | Python | ⭐⭐⭐⭐

pip install autoattack

- Focus: Robust evaluation standard - Features: Ensemble of complementary attacks, parameter-free - Best for: Standardized robustness evaluation

Interpretability & Explainability

Model-Agnostic Tools

SHAP
Microsoft Research | Python | ⭐⭐⭐⭐⭐

pip install shap

- Theory: Shapley values from cooperative game theory - Features: Global/local explanations, 15+ explainer types - Visualization: Interactive plots, force plots, dependence plots - Best for: Production explanations, business stakeholders

LIME
University of Washington | Python | ⭐⭐⭐⭐

pip install lime

- Theory: Local linear approximation - Features: Text, image, tabular data support - Best for: Quick local explanations, diverse data types

Deep Learning Specific

Captum
PyTorch Team | Python | ⭐⭐⭐⭐⭐

pip install captum

- Framework: Native PyTorch integration - Features: 15+ attribution algorithms, neuron/layer analysis - Visualization: Built-in visualization utilities - Best for: Deep learning research, PyTorch users

Alibi
Seldon | Python | ⭐⭐⭐⭐

pip install alibi

- Features: Counterfactual explanations, anchor explanations - Focus: Production-ready explanations for ML deployment - Best for: Model serving, real-time explanations

InterpretML
Microsoft Research | Python | ⭐⭐⭐⭐

pip install interpret

- Features: Glass-box models (EBM), model-agnostic explanations - Visualization: Unified dashboard for multiple explanation types - Best for: Regulated industries, healthcare, finance

Privacy-Preserving ML

Differential Privacy

Opacus
PyTorch Team | Python | ⭐⭐⭐⭐⭐

pip install opacus

- Framework: PyTorch-native differential privacy - Features: DP-SGD, privacy accounting, gradient clipping - Best for: Deep learning with formal privacy guarantees

TensorFlow Privacy
Google | Python | ⭐⭐⭐⭐

pip install tensorflow-privacy

- Framework: TensorFlow integration - Features: DP optimizers, privacy analysis, membership inference - Best for: Large-scale DP training, Google Cloud integration

Diffprivlib
IBM Research | Python | ⭐⭐⭐⭐

pip install diffprivlib

- Framework: Scikit-learn compatible DP algorithms - Features: DP versions of common ML algorithms - Best for: Traditional ML with differential privacy

Federated Learning

PySyft
OpenMined | Python | ⭐⭐⭐⭐

pip install syft

- Features: Federated learning, secure multi-party computation - Frameworks: PyTorch, TensorFlow support - Best for: Research, privacy-preserving collaborations

Flower (flwr)
Flower Labs | Python | ⭐⭐⭐⭐⭐

pip install flwr

- Features: Framework-agnostic federated learning - Deployment: Easy client-server architecture - Best for: Production federated learning, cross-platform deployment

FedML
FedML Inc | Python | ⭐⭐⭐⭐

pip install fedml

- Features: MLOps for federated learning, mobile deployment - Platform: Cloud platform + open source library - Best for: End-to-end federated ML solutions

Evaluation & Benchmarking

Robustness Benchmarks

RobustBench
Community | Python | ⭐⭐⭐⭐⭐

pip install robustbench

- Features: Standardized robustness evaluation, model zoo - Datasets: CIFAR-10/100, ImageNet, common corruptions - Best for: Comparing robustness methods, reproducible evaluation

Fairness Benchmarks

Folktables
Stanford | Python | ⭐⭐⭐⭐

pip install folktables

- Features: Real-world fairness benchmarks from US Census data - Tasks: Income prediction, employment, health insurance - Best for: Realistic fairness evaluation, policy research

Development & Deployment

MLOps for Trustworthy ML

Evidently
Evidently AI | Python | ⭐⭐⭐⭐

pip install evidently

- Features: ML monitoring, drift detection, bias monitoring - Deployment: Dashboard, reports, real-time monitoring - Best for: Production ML monitoring, continuous auditing

Great Expectations
Superconductive | Python | ⭐⭐⭐⭐

pip install great-expectations

- Features: Data validation, pipeline testing, documentation - Integration: Airflow, dbt, cloud platforms - Best for: Data quality assurance, ML pipeline validation

Model Cards & Documentation

Model Card Toolkit
Google | Python | ⭐⭐⭐

pip install model-card-toolkit

- Features: Automated model card generation, templates - Integration: TensorFlow Model Analysis integration - Best for: Model documentation, regulatory compliance

Quick Start Guides

Fairness Analysis Workflow

# Using AIF360 for comprehensive bias analysis
from aif360.datasets import AdultDataset
from aif360.metrics import BinaryLabelDatasetMetric
from aif360.algorithms.preprocessing import Reweighing

# Load data and compute bias metrics
dataset = AdultDataset()
metric = BinaryLabelDatasetMetric(dataset)
print(f"Disparate Impact: {metric.disparate_impact()}")

# Apply bias mitigation
rw = Reweighing(unprivileged_groups=[{'sex': 0}],
                privileged_groups=[{'sex': 1}])
dataset_transf = rw.fit_transform(dataset)

Adversarial Robustness Testing

# Using ART for adversarial evaluation
from art.attacks.evasion import FastGradientMethod
from art.estimators.classification import KerasClassifier

# Wrap your model
classifier = KerasClassifier(model=model)

# Generate adversarial examples
attack = FastGradientMethod(estimator=classifier, eps=0.1)
x_test_adv = attack.generate(x=x_test)

# Evaluate robustness
accuracy_clean = classifier.predict(x_test).argmax(axis=1) == y_test
accuracy_adv = classifier.predict(x_test_adv).argmax(axis=1) == y_test
print(f"Clean accuracy: {accuracy_clean.mean():.2f}")
print(f"Adversarial accuracy: {accuracy_adv.mean():.2f}")

Privacy-Preserving Training

# Using Opacus for differential privacy
from opacus import PrivacyEngine

# Attach privacy engine to optimizer
privacy_engine = PrivacyEngine()
model, optimizer, data_loader = privacy_engine.make_private(
    module=model,
    optimizer=optimizer,
    data_loader=data_loader,
    noise_multiplier=1.0,
    max_grad_norm=1.0,
)

# Train with privacy guarantees
for epoch in range(epochs):
    for batch in data_loader:
        # Standard PyTorch training loop
        optimizer.zero_grad()
        loss = criterion(model(batch[0]), batch[1])
        loss.backward()
        optimizer.step()

    # Check privacy budget
    epsilon = privacy_engine.get_epsilon(delta=1e-5)
    print(f"Epoch {epoch}, ε = {epsilon:.2f}")

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Tool Selection Guide

• Research: Start with comprehensive toolkits (AIF360, ART, SHAP)
• Production: Focus on framework-specific tools (Fairlearn for sklearn, Captum for PyTorch)
• Evaluation: Use standardized benchmarks (RobustBench, Folktables)
• Deployment: Implement monitoring (Evidently, Great Expectations)

Last updated: December 2024