A Framework for Understanding Unintended Consequences of Machine Learning

This paper provides a framework that partitions sources of downstream harm in machine learning into six distinct categories spanning the data generation and machine learning pipeline, and describes how these issues arise, how they are relevant to particular applications, and how they motivate different solutions.