Angèle Christin
Abstract
What claims are made about the objectivity of machines versus that of human experts? Whereas most current debates focus on the growing impact of algorithms in the age of Big Data, I argue here in favor of taking a longer historical perspective on these developments. Drawing on Daston and Galison's analysis of scientific production since the eighteenth century, I show that their distinction among three forms of objectivity ("truth-to-nature," "mechanical objectivity," and "trained judgment") sheds light on existing discussions about algorithmic objectivity and accountability in expert fields.
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