Bias and Hallucinations

Explores how biases enter AI systems through data, algorithms, and human decisions, and presents practical methods (fairness metrics, debiasing techniques, and inclusive dataset curation) to detect and reduce discrimination across race, gender, age, and other protected characteristics. 

Covers the importance of making black-box models interpretable, showcasing techniques such as LIME, SHAP, counterfactual explanations, and model cards so stakeholders can understand, trust, and audit AI decisions. 

Examines privacy risks in machine learning (membership inference, model inversion, re-identification) and solutions including differential privacy, federated learning, synthetic data generation, and compliance with GDPR, CCPA, and emerging AI regulations. 

Accountability and Governance Frameworks, as outlined by the Institute for Ethical AI & Machine Learning, provide structured templates like the AI-RFX Procurement Framework to translate ethical principles into actionable checklists for evaluating AI systems during procurement and deployment. These frameworks stress assessing organizational maturity in processes and technical infrastructure via the Machine Learning Maturity Model, ensuring robust oversight to mitigate risks and promote responsible AI practices across the lifecycle.

Focuses on ensuring advanced AI systems behave as intended, even at superhuman levels, covering technical alignment research, scalable oversight, value learning, and the prevention of unintended or catastrophic outcomes.   

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