Alignment and Ethics

Chapter 30: Model Alignment Ethical outputs, human feedback-based alignment Applications: Safe LLMs [RLHF (Reinforcement Learning from Human Feedback), value alignment, red-teaming] References Chapter 31: Bias Mitigation Identifying biases in embeddings, outputs Debiasing techniques, fairness metrics [Adversarial debiasing, counterfactual fairness, disparate impact analysis] References Chapter 32: Explainable NLP Attention analysis, feature attribution Mechanistic interpretability, probing techniques [SHAP, LIME, circuit analysis, representation probing] References Chapter 33: Privacy in NLP Differential privacy, federated learning Applications: Secure NLP systems [DP-SGD (Differentially Private Stochastic Gradient Descent), PATE, secure aggregation] References Chapter 34: Generative AI Safety Risks: Misinformation, plagiarism, deepfakes Safety mechanisms: Watermarking, content moderation [Output filtering, provenance tracking, adversarial robustness] References