Recommendation algorithms are everywhere, from suggesting music to recommending potential romantic partners. They work by using data from past behavior to predict future behavior. The Netflix Prize competition in 2006 was a major milestone in the development of these algorithms, with the winning algorithm using a math technique called singular value decomposition (SVD) to find similarities among movies users liked. Recommendation algorithms have become more sophisticated in recent years, with TikTok’s algorithm being particularly powerful due to the vast amount of data it collects on user watch time. However, these algorithms can also have unintended real-world implications, such as promoting extremist content or suppressing content from marginalized groups.