In this chapter, I develop what I call the Gender Shades Case Study (GSCS), which focuses on Joy Buolamwini’s master’s research on racial and gender bias in facial recognition and classification systems. I situate that research in its demographic and cultural context and use it to demonstrate the significance of diversity in computing and technology, arguing, as Buolamwini puts it, that “[w]ho codes matters” (2017). Buolamwini’s research is relevant to many topics in Philosophy and AI, including discussions of algorithmic fairness, privacy, and surveillance. However, I leave those important discussions for other scholars to investigate. In this chapter, I develop this case to provide multifaceted evidence of the impact of diversity in computer science and technology, including AI, mainly as it plays out concerning biases and values around gender and race. I develop this case an example of situated knowledge and Helen Longino’s Critical Contextual Empiricism.