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Engineering has historically been positioned as “objective” and “neutral” (Cech, 2014), supporting a technical/social dualism in which “hard” technical skills are valued over “soft” social skills such as empathy and team management (Faulkner, 2007). Disrupting this dualism will require us to transform the way that engineering is taught, to include the social, economic, and political aspects of engineering throughout the curriculum. One promising approach to integrating social and technical is through developing students’ critical sociotechnical literacy, supporting students in coming to “understand the intrinsic and systemic sociotechnical relationship between people, communities, and the built environment” (McGowan & Bell, 2020, p. 981). This work-in-progress study is part of a larger NSF-funded research project that explores integrating sociotechnical topics with technical content knowledge in a first-year engineering computing course. This course has previously focused on teaching students how to code, the basics of data science, and some applications to engineering. The revised course engages students in a series of sociotechnical topics, such as analyzing and interpreting data-based evidence of environmental racism. Each week, students read short articles and write reflections to prepare for in-class small group discussions. Near the end of the semester, students examined the topic of racial bias in medical equipment. Students read two popular news articles that discussed differences in accuracies of pulse oximeter readings for patients with different skin tones. We analyze students’ reflection responses for evidence of their developing sociotechnical literacy along three dimensions: (1) bias, (2) differential impact, and (3) responsibility. This exploratory case study employs thematic analysis (Braun & Clarke, 2006) to analyze the students’ written reflections for this topic. Students reflected on evidence of racial bias and potential causes of bias in the device, how this bias is located in and furthers historical patterns of racism in medicine, and considered who or what might be responsible for either causing or fixing the now-known racial bias.