Detecting and Overcoming Bias in AI Systems

As artificial intelligence becomes deeply integrated into everyday decision-making, ensuring these systems are fair and unbiased is more critical than ever. Unchecked algorithmic bias can lead to discriminatory outcomes, legal liabilities, and eroded public trust. This text-based course equips you with the foundational knowledge to spot bias in training data, evaluate model fairness, and implement modern mitigation techniques. What you'll learn: - Understand the core concepts of algorithmic fairness and how bias enters AI systems - Identify different types of bias, including historical, representation, and measurement bias - Apply quantitative metrics to evaluate fairness in datasets and machine learning predictions - Explore modern mitigation strategies such as pre-processing, in-processing, and post-processing techniques - Examine contemporary challenges in large language models, including alignment and toxic outputs - Navigate ethical guidelines and emerging frameworks for responsible AI deployment You will begin by learning key terminology and foundational definitions before moving on to practical evaluations of fairness and real-world mitigation strategies. This course is designed for beginners, requiring no prior background in advanced mathematics or programming. Start reading today to build fairer, more ethical artificial intelligence.