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A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle

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arxiv 1901.10002 v5 pith:WWBC7OWL submitted 2019-01-28 cs.LG stat.ML

Harini Suresh , John V. Guttag This is my paper · ORCID
classification cs.LGstat.ML
keywords harmlearningmachineconsequencescycledownstreamframeworklife
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 7 Pith papers

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