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

TL;DR

This paper broadens the understanding of bias in machine learning by examining epistemological and dynamic aspects, advocating for value-sensitive design to improve fairness in automated decision-making.

Contribution

It introduces a broader perspective on bias, emphasizing epistemology and dynamics, and advocates for value-sensitive design methodologies to address these issues.

Findings

Highlights technical bias as an epistemological issue

Identifies emergent bias as a feedback phenomenon

Suggests value-sensitive design for bias mitigation

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.