This resource explores the concept of algorithmic bias, which occurs when algorithms produce results that are systematically prejudiced due to flawed assumptions in the machine learning process. It discusses how bias enters algorithms through various sources such as biased training data, feature selection, proxy variables, and the lack of diverse perspectives in development teams. The document provides real-world examples of algorithmic bias, including issues in facial recognition, hiring algorithms, and criminal justice risk assessments. It highlights the social, economic, and ethical impacts of algorithmic bias, such as reinforcing stereotypes, unfair hiring practices, and violating fairness principles. Additionally, it outlines strategies to mitigate algorithmic bias, including ensuring diverse and representative data, promoting algorithmic transparency, fostering diverse development teams, and conducting regular audits and testing. The resource emphasizes the role of future computing professionals in creating fair and equitable algorithms.