Human Judgment in the Age of AI & Algorithms

Artificial Intelligence and algorithms now influence decisions in nearly every industry: from hiring and finance to healthcare, marketing, and public policy. Yet while AI systems are powerful tools for analyzing data and predicting outcomes, they do not replace the need for human judgment. In fact, the rise of AI makes human decision-making more important than ever. This course explores how humans and algorithms can work together effectively. You will learn how AI systems make predictions, where they excel, and where they can fail. You will also explore the most common risks in algorithmic decision-making, including biased training data, automation bias, algorithm aversion, and the unintended consequences of poorly designed optimization goals. Through real-world examples and practical frameworks, the course will teach you how to critically evaluate algorithmic recommendations rather than blindly trusting or dismissing them. You will learn how to interpret probabilistic predictions, assess the assumptions behind machine learning models, and understand when human judgment should override automated decisions. The course also introduces tools for better decision-making in uncertain environments, including probabilistic thinking, scenario planning, and decision trees. These techniques help professionals navigate complex situations where both human reasoning and algorithmic insights are valuable. Finally, you will learn how organizations design effective human-AI collaboration systems. Topics include human-in-the-loop decision models, explainability, escalation procedures, accountability structures, and governance practices that ensure AI systems are used responsibly. By the end of this course, you will have a clear understanding of how to combine the analytical power of AI with the contextual reasoning and ethical judgment that only humans can provide. Whether you are a manager, analyst, entrepreneur, or professional working alongside AI tools, this course will help you make better decisions in an increasingly algorithm-driven world.