The Role of Data—Representation and Bias

Somendra Narayan ()
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Somendra Narayan: University of Amsterdam

Chapter Chapter 4 in The Bridgerton Paradox in Artificial Intelligence, 2025, pp 37-46 from Springer

Abstract: Abstract Focusing on the technical core of how AI learns, this chapter details how data serves as both a mirror and amplifier of social inequities. It classifies common types of bias—selection, representation, and measurement—and shows how these distortions arise from flawed archives, incomplete datasets, and cultural blind spots. The chapter explores data curation strategies (proactive auditing, balanced sampling) and post hoc techniques (algorithmic reweighting, fairness constraints), underscoring that addressing bias is as much an ethical and philosophical challenge as a technical one.

Keywords: Data bias; Representation bias; Data curation; Post hoc mitigation; Ethical data practices (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-99493-7_4

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DOI: 10.1007/978-3-031-99493-7_4

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