Algorithmic bias refers to the systematic and discriminatory errors or inaccuracies that may arise in machine learning algorithms due to biased data or the design of the algorithm itself. This bias can result in unfair or discriminatory outcomes, such as biased hiring practices, racial profiling, or unequal access to services. Researchers in this area study ways to detect, mitigate, and prevent algorithmic bias to ensure fairness and equity in the use of artificial intelligence systems. Techniques used in this field include auditing algorithms for bias, developing fairness metrics, and incorporating ethical considerations into algorithm design.