A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Item
Title
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Abstract/Description
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.
Author/creator
Date
Pages
1-9
Resource type
Background/Context
Medium
Print
Background/context type
Conceptual
Open access/free-text available
Yes
Peer reviewed
No
Citation
Suresh, H., & Guttag, J. V. (2021). A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. Equity and Access in Algorithms, Mechanisms, and Optimization, 1–9. https://doi.org/10.1145/3465416.3483305
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