A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics
arXiv:1807.00553
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.
Presented at the 2018 Workshop on Fairness, Accountability and Transparency in Machine Learning during ICML 2018, Stockholm, Sweden
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- Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing
- Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
- How Different Groups Prioritize Ethical Values for Responsible AI
- Pretrained AI Models: Performativity, Mobility, and Change
- An Introduction to Algorithmic Fairness