Abstract
AbstractThe fifth and final stage in the prediction supply chain is learning from the aggregated data processed through optimized algorithms. Data-driven predictions are the result of a continuous cycle of finding digital sources, designing models, identifying patterns, designing for efficiency, and eventually generating insights. Insights represent new knowledge. The central question in this step is to think about who has the privilege to learn from aggregate data. This chapter draws attention to the connection between what is collected and what is learned. A logic of aggregation introduces three metaphors—autopsy, warden, and butler—to describe the relationship between the original data subjects and the new knowledge recipients. This chapter explains why some populations, but not others, benefit from what data science offers.
Publisher
Oxford University PressNew York
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