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

• 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

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...