Responsible AI is not just ethics - it is risk management and regulatory compliance. Risk classification framework: Critical risk (autonomous decisions affecting human rights, safety, credit, employment) requires human oversight, bias auditing, explainability, regulatory approval. High risk (medical devices, hiring tools, credit scoring) requires documentation, testing, monitoring. Medium risk (chatbots, recommendation systems) requires transparency disclosure and basic safety measures. Low risk (spam filters, search ranking) requires general security practices. Bias auditing process: 1) Define protected attributes relevant to your use case (gender, race, age, disability). 2) Collect disaggregated evaluation data with demographic labels. 3) Measure fairness metrics: demographic parity (equal selection rates), equal opportunity (equal TPR across groups), equalized odds. 4) Investigate root causes: training data underrepresentation, proxy features, feedback loop amplification. 5) Mitigation: resampling, reweighting, adversarial debiasing, post-processing threshold adjustment. Explainability by risk level: Critical - must explain individual decisions (LIME, SHAP). High risk - explain model behavior statistically. Lower risk - global model explanations sufficient. Documentation: Model cards (Mitchell et al.) for model characteristics, intended use, performance breakdowns, known limitations. Datasheets (Gebru et al.) for training datasets.