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Computing ethics

Engaging the Ethics of Data Science in Practice

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Critical commentary on data science has converged on a worrisome idea: that data scientists do not recognize their power and, thus, wield it carelessly. These criticisms channel legitimate concerns about data science into doubts about the ethical awareness of its practitioners. For these critics, carelessness and indifference explains much of the problem—to which only they can offer a solution.

Such a critique is not new. In the 1990s, Science and Technology Studies (STS) scholars challenged efforts by AI researchers to replicate human behaviors and organizational functions in software (for example, Collins3). The scholarship from the time was damning: expert systems routinely failed, critical researchers argued, because developers had impoverished understandings of the social worlds into which they intended to introduce their tools.6 At the end of the decade, however, Mark Ackerman reframed this as a social-technical gap between "what we know we must support socially and what we can support technically."1 He argued that Al's deficiencies did not reflect a lack of care on the part of researchers, but a profound challenge of dealing with the full complexity of the social world. Yet here we are again.


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