Bias, Explainability, Privacy, and Adversarial Attacks

In the previous chapter, we explored the topic of AI risk management framework and discussed its importance in mitigating the risks associated with AI systems. We covered the core concepts of what it is, the importance of identifying and assessing risks, and recommendations for managing those risks. In this chapter, we will take a more in-depth look at several specific risk topics and technical techniques for mitigations. We will explore the essential areas of bias, explainability, privacy, and adversarial attacks, and how they relate to AI systems. These are some of the most pertinent areas in responsible AI practices, and it is important for ML practitioners to develop a foundational understanding of these topics and the technical solutions. Specifically, we will examine how bias can lead to unfair and discriminatory outcomes, and how explainability can enhance the transparency and accountability of AI systems. We will also...