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In this regard, D'Ignazio and Klein join data studies scholar Yanni Loukissas who argues for a shift from datasets to data settings in order to capture 'both the technical and the human processes that affect what information is captured in the data collection process and how the data are then structured' (Loukissas, 2019, as cited in D'Ignazio & Klein, p. 132). With that, the authors come a full circle to their original motivation to demonstrate that in data science "power imbalances are everywhere" (p. 201) and to encourage data scientists to proceed by the alternative route to justice outlined by data feminism. Chapter Five applies the lens of I pluralism i to the process of knowledge-making and reiterates the idea that clean data hide diversity and that data messiness may be an indicator of rich information about the circumstances under which those data were collected. [Extracted from the article]