Technical Bias and Emergent Bias in Automated Decision-Making Systems
A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics
Machine learning is increasingly deployed in real world contexts, providing 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.We stress the need to reflect on epistemology, point to value-sensitive design methodologies to revisit the design and implementation process of automated decision-making systems, and stimulate debate on how to change machinelearning practice to effectively address these issues.
