Ethics, Privacy, Fairness, and Bias in AI and Computing

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Multifaceted ethical considerations of artificial intelligence, focusing on privacy, bias, fairness, and transparency. Students engage in hands-on learning by examining practical tools and frameworks like AWS, Azure, and GCP, gaining insights into best practices for mitigating data privacy risks, detecting and managing bias, ensuring fairness, and monitoring model performance. Through case studies, discussions, and exercises, course addresses real-world challenges such as data poisoning, feature drift, and human oversight in decision-making processes. Explores key algorithms and methodologies for model explainability, including fairness and bias detection techniques, to develop a holistic understanding of responsible AI.