Edited by Mireille Hildebrandt and Kieron O’Hara
Chapter 3: Data-driven agency and knowledge
If data-driven agency is a form of agency based on what machines have learned, it seems important to understand the nature and limit of the type of knowledge that can be mechanically obtained from digital data. After reviewing some of the popular claims made about big data this chapter explores some of the differences in the use of big data and machine science in the natural sciences and in the social domain. It insists in particular on the fact that in the natural sciences what constitutes data and how it should be interpreted are under the collective jurisdiction of specialists of the domain whose authority is recognized by governments, funding agencies and the general public, while in the social domain the data is often claimed to be simply ‘found’ though it is explicitly sought for a variety of reasons. It is not however ‘crafted’ in the sense of being validated and authenticated by the community of concerned researchers. In consequence, anyone who has the necessary technical competence gains the authority to interpret the data and declare what the data proves. Finally, the chapters analyzes some aspects of machine learning and science that tend to encourage the faulty interpretation that ‘data is enough’.
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