Risk-sensitive learning is a framework in machine learning that focuses on making decisions while considering the potential risks and uncertainties involved. Unlike traditional approaches that may prioritize maximizing rewards, this method evaluates the consequences of actions, aiming to minimize negative outcomes. It is particularly useful in environments where the stakes are high, such as finance or healthcare. This approach often employs techniques from reinforcement learning and probability theory to assess the likelihood of various outcomes. By incorporating risk preferences, models can be tailored to align with specific goals, allowing for more robust decision-making in uncertain situations.