Explore the ethical implications of algorithmic monoculture in high-stakes decision-making processes. Delve into Kathleen Creel's lecture from Stanford University, which examines how using the same machine learning model across various settings can amplify biases and lead to consistent mistreatment of individuals. Learn about the formalization of outcome homogenization, experiments conducted on US census data, and the ethical arguments surrounding this phenomenon. Gain insights into the potential consequences for democracy, autonomy, and fairness, as well as the concept of fairness gerrymandering. Understand the risks associated with shared training data and foundation models, and consider the broader implications for algorithmic decision-making in society.