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- Title
Assembled Bias: Beyond Transparent Algorithmic Bias.
- Authors
Waller, Robyn Repko; Waller, Russell L.
- Abstract
In this paper we make the case for the emergence of novel kind of bias with the use of algorithmic decision-making systems. We argue that the distinctive generative process of feature creation, characteristic of machine learning (ML), contorts feature parameters in ways that can lead to emerging feature spaces that encode novel algorithmic bias involving already marginalized groups. We term this bias assembled bias. Moreover, assembled biases are distinct from the much-discussed algorithmic bias, both in source (training data versus feature creation) and in content (mimics of extant societal bias versus reconfigured categories). As such, this problem is distinct from issues arising from bias-encoding training feature sets or proxy features. Assembled bias is not epistemically transparent in source or content. Hence, when these ML models are used as a basis for decision-making in social contexts, algorithmic fairness concerns are compounded.
- Subjects
ALGORITHMIC bias; MACHINE learning; SOCIAL context; FAIRNESS; DECISION making
- Publication
Minds & Machines, 2022, Vol 32, Issue 3, p533
- ISSN
0924-6495
- Publication type
Article
- DOI
10.1007/s11023-022-09605-x