Work

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

Year: 2021

Type: conference-paper

Abstract: 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.

Source: ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO)

DOI: 10.1145/3465416.3483305

Authors: Harini Suresh, John Guttag

Institution: Massachusetts Institute of Technology

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W3172100757

FWCI: 52.18

Cites: 72

Cited by: 1,017

Related to: 10