Ethical AI and Bias

Artificial Intelligence is rapidly reshaping critical sectors of our lives—from hiring and healthcare to finance and criminal justice—offering transformative potential while simultaneously introducing profound ethical challenges. Central to these challenges is the critical issue of bias, where AI systems can systematically produce unfair or discriminatory outcomes. This course provides a comprehensive framework for understanding, identifying, and mitigating these risks, ensuring that AI development is guided by the core human values of fairness, transparency, and accountability.

You will begin by deconstructing the problem of Algorithmic Bias, exploring how AI models act as digital mirrors, reflecting and often amplifying existing societal prejudices embedded in their training data. The curriculum then introduces the technical and philosophical toolkit for building ethical systems, starting with Fairness and Discrimination, where you will learn to identify harmful outcomes like disparate impact. From there, you will confront the Black Box Problem and master the principles of Explainable AI (XAI) , learning to make opaque decisions transparent and understandable—a prerequisite for trust and due process.

The course culminates in a deep exploration of Accountability and Governance. You will navigate the complex question of liability across the AI lifecycle—from data curators to deploying companies—and examine emerging regulatory frameworks like the EU's AI Act. Finally, you will learn how to establish robust oversight through continuous Auditing and Responsible AI (RAI) practices, ensuring that AI systems remain fair, non-discriminatory, and aligned with ethical principles throughout their operational life. By the end of this course, you will be equipped not just to build powerful AI, but to build AI that is just, trustworthy, and serves all of humanity.