Ethics in AI: Managing Bias and Accountability

As artificial intelligence becomes central to decision-making, ensuring these systems are fair, transparent, and accountable is more critical than ever. Understanding how to identify bias and manage ethical risks is a vital skill for anyone working with modern technology. This text-based course guides you through the core concepts of responsible AI, from foundational definitions of algorithmic fairness to the practical aspects of compliance and governance. You will learn to analyze ethical dilemmas, recognize human and systemic bias in data, and apply modern mitigation strategies to ensure your AI implementations are trustworthy. What you will learn: Understand the foundational principles of AI ethics, fairness, and accountability; Identify sources of bias in training data, algorithms, and modern generative models; Apply practical frameworks to mitigate bias and promote algorithmic fairness; Navigate the evolving global regulatory landscape and compliance standards; Analyze real-world ethical dilemmas through detailed case studies; Evaluate systems for transparency, explainability, and human-in-the-loop oversight. The course begins with essential terminology and the philosophical foundations of ethics in technology, before moving into practical methodologies for risk assessment and governance. You will progress through structured readings and conceptual exercises designed to build your ethical decision-making toolkit. This course is designed for beginners, business leaders, and aspiring developers who want to understand the ethical implications of AI. No prior programming or technical background is required. Start reading today to build a solid foundation in responsible and trustworthy AI.