The critical writing on data science has taken the paradoxical position of insisting that normative issues pervade all work with data while leaving unaddressed the issue of data scientists’ ethical agency. Critics need to consider how data scientists learn to think about and handle these trade-offs, while practicing data scientists need to be more forthcoming about all of the small choices that shape their decisions and systems.
Technical actors are often far more sophisticated than critics at understanding the limits of their analysis. In many ways, the work of data scientists is a qualitative practice: they are called upon to parse an amorphous problem, wrangle a messy collection of data, and make it amenable to systematic analysis. To do this work well, they must constantly struggle to understand the contours and the limitations of both the data and their analysis. Practitioners want their analysis to be accurate and they are deeply troubled by the limits of tests of validity, the problems with reproducibility, and the shortcomings of their methods.
Many data scientists are also deeply disturbed by those who are coming into the field without rigorous training and those who are playing into the hype by promising analyses that are not technically or socially responsible. In this way, they should serve as allies with critics. Both see a need for nuances within the field. Unfortunately, universalizing critiques may undermine critics’ opportunities to work with data scientists to address meaningfully some of the most urgent problems.
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