  As machine learning (ML) increasingly affects people and society, awareness
of its potential unwanted consequences has also grown. To anticipate, prevent,
and mitigate undesirable downstream consequences, it is critical that we
understand when and how harm might be introduced throughout the ML life cycle.
In this paper, we provide a framework that identifies seven distinct potential
sources of downstream harm in machine learning, spanning data collection,
development, and deployment. In doing so, we aim to facilitate more productive
and precise communication around these issues, as well as more direct,
application-grounded ways to mitigate them.
