Machine Learning Bias and Fairness: The COMPAS Case Study

Algorithmic decision-making plays a critical role in society, but without careful design, machine learning models can perpetuate and amplify systemic biases. Understanding how these biases manifest in real-world systems, such as the COMPAS risk assessment tool, is essential for anyone building or evaluating modern AI technologies.\n\nIn this text-based course, you will transition from a beginner to an ethically conscious practitioner capable of identifying, measuring, and mitigating algorithmic bias. You will explore how data collection and model design can inadvertently lead to discriminatory outcomes, and learn how to apply modern fairness frameworks to your work.\n\nWhat you'll learn:\n- Understand the core concepts of algorithmic bias, fairness, and ethical machine learning.\n- Analyze the COMPAS case study to see how risk assessment tools can perpetuate racial discrimination.\n- Evaluate models using key fairness metrics such as demographic parity and equalized odds.\n- Identify sources of bias in training data and machine learning pipelines.\n- Practice applying modern bias mitigation techniques to improve model equity.\n- Explore contemporary ethical AI frameworks and emerging regulatory standards.\n\nThe course begins with foundational definitions of algorithmic fairness and a deep dive into the COMPAS case study. From there, you will progress to practical methodologies for detecting bias and implementing mitigation strategies in your own data workflows.\n\nThis course is designed for aspiring data scientists, policy analysts, and technology enthusiasts who want to understand AI ethics. No prior machine learning or programming experience is required.\n\nStart reading today to build a solid foundation in ethical artificial intelligence and fair machine learning practices.