Responsible Artificial Intelligence

This course introduces the fundamental concepts, challenges, and methods of Responsible Artificial Intelligence (Responsible AI). The first part of the course focuses on the foundations of Responsible AI, including bias, fairness, explainability, robustness, reliability, and privacy in AI systems. The second part of the course focuses on methods and techniques for responsible AI design, with particular emphasis on bias and fairness in machine learning, explainable AI (XAI), and adversarial robustness. By the end of the course, students will have learned how to identify, analyze, and evaluate biases in AI systems, apply methods for explaining model behavior, and systematically incorporate fairness, explainability, and robustness aspects into the design and application of AI systems.

 

Learning objectives. By the end of the course, students will be able to:

  • Understand the core principles of Responsible AI
  • Analyze ethical, societal, and technical challenges in AI systems
  • Identify and evaluate bias and fairness issues in machine learning
  • Apply explainable AI (XAI) methods to analyze model behavior
  • Understand adversarial robustness challenges in AI systems
  • Incorporate fairness, explainability, and robustness aspects into AI/ML pipelines
 

Course content:

(Topics may be adjusted slightly during the trimester)

  • Foundations of Responsible/Trustworthy AI
  • Bias in AI Systems
  • Fairness-Aware Learning
  • Explainable AI (XAI)
  • Adversarial Robustness
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Literature: