Evolution and impact of bias in human and machine learning algorithm interaction

Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc. The result is that algorithms are subject to bias that is born fro...

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Bibliographic Details
Published in:	PloS one Vol. 15; no. 8; p. e0235502
Main Authors:	Sun, Wenlong, Nasraoui, Olfa, Shafto, Patrick
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 13.08.2020 Public Library of Science (PLoS)
Subjects:	Algorithms Annotations Bias Biology and Life Sciences Cognitive biases Computer and Information Sciences Computer engineering Computer science Computer simulation Decision making Evolution Human influences Human performance Human-computer interaction Humans Hypotheses Information retrieval Information storage and retrieval systems Interfaces Investigations Iterative methods Labels Language Learning Learning algorithms Machine learning Machine Learning - standards Physical Sciences Psychological aspects Psychological research Recommender systems Relevance Research and Analysis Methods Social Sciences Statistical analysis Statistical methods User interfaces United States Louisville Kentucky United States > US
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
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