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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
| Tool | Category | Purpose |
|---|---|---|
| Fairlearn | Fairness | Bias assessment + mitigation |
| AI Fairness 360 | Fairness | IBM fairness toolkit |
| SHAP | Explainability | Shapley value explanations |
| Captum | Explainability | PyTorch interpretability |
| Opacus | Privacy | Differential privacy for PyTorch |
| ART | Robustness | Adversarial robustness toolbox |
| Alibi | Explainability | ML model explanations |
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())Reading Roadmap
### Foundations
1. "Fairness and Machine Learning" (Barocas, Hardt, Narayanan)
2. "Datasheets for Datasets" (Gebru et al., 2021)
3. "Model Cards for Model Reporting" (Mitchell et al., 2019)
### Fairness
4. "On Fairness and Calibration" (Pleiss et al., 2017)
5. "Fairness Through Awareness" (Dwork et al., 2012)
### Explainability
6. "A Unified Approach to Interpreting Model Predictions" (SHAP)
7. "Why Should I Trust You?" (LIME, Ribeiro et al., 2016)
### Safety
8. "Constitutional AI" (Bai et al., 2022)
9. "Red Teaming Language Models" (Perez et al., 2022)
10. "Scaling Monosemanticity" (Anthropic, 2024)Use Cases
- Bias auditing: Check models for demographic biases
- Compliance: EU AI Act and regulatory requirements
- Model documentation: Model cards and impact assessments
- Research ethics: Ethical considerations for AI research
- Course material: Teach responsible AI principles
