Title: A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics,
Abstract: Machine learning (ML) is increasingly deployed in real world contexts,
supplying actionable insights and forming the basis of automated
decision-making systems. While issues resulting from biases pre-existing in
training data have been at the center of the fairness debate, these systems are
also affected by technical and emergent biases, which often arise as
context-specific artifacts of implementation. This position paper interprets
technical bias as an epistemological problem and emergent bias as a dynamical
feedback phenomenon. In order to stimulate debate on how to change machine
learning practice to effectively address these issues, we explore this broader
view on bias, stress the need to reflect on epistemology, and point to
value-sensitive design methodologies to revisit the design and implementation
process of automated decision-making systems.