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

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: Suresh, Harini; Guttag, John V.
Date: 2021
In publication: Equity and Access in Algorithms, Mechanisms, and Optimization
Pages: 1-9
Resource type: en Background/Context
Medium: en Print
Background/context type: en Conceptual
Open access/free-text available: en Yes
Peer reviewed: en No
DOI: 10.1145/3465416.3483305
URL: Open Access Paper (arXiv)
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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