responsible-ai-guide

Responsible AI Guide

Overview

A comprehensive collection of resources for building trustworthy, fair, and ethical AI systems. Covers fairness metrics, bias detection and mitigation, explainability methods, privacy-preserving techniques, robustness testing, and governance frameworks. Essential reading for researchers working on AI safety, alignment, and deploying models in high-stakes domains.

Topic Taxonomy

Responsible AI
├── Fairness
│   ├── Bias detection (data, model, outcome)
│   ├── Fairness metrics (demographic parity, equalized odds)
│   ├── Bias mitigation (pre/in/post-processing)
│   └── Intersectional fairness
├── Explainability
│   ├── Feature attribution (SHAP, LIME, IG)
│   ├── Concept-based (TCAV, concept bottleneck)
│   ├── Counterfactual explanations
│   └── Mechanistic interpretability
├── Privacy
│   ├── Differential privacy
│   ├── Federated learning
│   ├── Membership inference attacks
│   └── Machine unlearning
├── Robustness
│   ├── Adversarial attacks/defenses
│   ├── Distribution shift
│   ├── Uncertainty quantification
│   └── Out-of-distribution detection
├── Safety & Alignment
│   ├── RLHF and preference learning
│   ├── Constitutional AI
│   ├── Red teaming
│   └── Guardrails and filters
└── Governance
    ├── Model cards
    ├── Datasheets for datasets
    ├── AI impact assessments
    └── Regulatory compliance (EU AI Act)

Key Tools

Fairness Assessment

from fairlearn.metrics import MetricFrame
from sklearn.metrics import accuracy_score, recall_score

# Assess fairness across demographic groups
metrics = MetricFrame(
    metrics={
        "accuracy": accuracy_score,
        "recall": recall_score,
    },
    y_true=y_test,
    y_pred=y_pred,
    sensitive_features=demographics,
)

print("Overall:")
print(metrics.overall)
print("\nBy group:")
print(metrics.by_group)
print("\nDifference (max - min):")
print(metrics.difference())