Some Pitfalls in AI

The course covers common pitfalls in AI-powered applications, including bias, fairness, confounding variables, adversarial attacks, ethics, explainability, security, and privacy concerns. The learning outcomes include understanding the risks associated with AI decision-making and the importance of addressing these challenges. The course teaches skills such as recognizing bias in AI systems, mitigating adversarial attacks, and ensuring fairness in machine learning models. The teaching method involves a high-level overview of the pitfalls with real-world examples and discussions. The intended audience includes individuals interested in AI ethics, security, and privacy, as well as policymakers and developers working with AI technologies.