Algorithmic Bias and AI Ethics: Preventing Prejudice in Technology

Are you aware of how easily human prejudices can be encoded into artificial intelligence? As AI systems increasingly shape critical decisions in hiring, finance, healthcare, and criminal justice, understanding algorithmic bias has become an essential skill for modern developers, engineers, and technology professionals. In this text-only course, you will explore how machine learning models inherit and amplify societal biases. You will study key historical and contemporary case studies of algorithmic discrimination, analyze why these failures occur, and learn how to apply modern ethical frameworks to build fairer, more transparent systems. What you'll learn: - Understand the core concepts of algorithmic bias, prejudice, and fairness in machine learning. - Analyze real-world case studies where predictive systems demonstrated discriminatory behavior. - Identify the sources of bias in training data, feature selection, and model deployment. - Apply modern mathematical fairness metrics to evaluate predictive models. - Explore ethical mitigation strategies for large language models and modern generative AI. - Design auditing workflows to detect and address prejudice before software is released. You will start with the foundational definitions of algorithmic fairness and data ethics, progressing through detailed written case studies and conceptual exercises. By the end of this reading-based program, you will have a practical, conceptual framework for identifying and mitigating bias in your own engineering projects. This course is designed for beginner developers, data enthusiasts, and engineering students who want to build responsible technology. No prior background in advanced mathematics or machine learning is required. Begin your journey toward building more equitable and ethical artificial intelligence today.