A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics
This position paper highlights broader bias sources in ML systems for fairness researchers and practitioners, but is incremental as it builds on existing fairness debates.
The paper argues that automated decision-making systems suffer from technical and emergent biases beyond data biases, framing them as epistemological and dynamical feedback problems. It proposes value-sensitive design methodologies to address these issues in ML practice.
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.