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Machine learning (ML) is increasingly deployed in real world contexts,supplying actionable insights and forming the basis of automateddecision-making systems. While issues resulting from biases pre-existing intraining data have been at the center of the fairness debate, these systems arealso affected by technical and emergent biases, which often arise ascontext-specific artifacts of implementation. This position paper interpretstechnical bias as an epistemological problem and emergent bias as a dynamicalfeedback phenomenon. In order to stimulate debate on how to change machinelearning practice to effectively address these issues, we explore this broaderview on bias, stress the need to reflect on epistemology, and point tovalue-sensitive design methodologies to revisit the design and implementationprocess of automated decision-making systems.

A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics