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Ethical Implications of Bias in Machine Learning

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dc.contributor.author Yapo, Adrienne
dc.contributor.author Weiss, Joseph
dc.date.accessioned 2017-12-28T02:15:53Z
dc.date.available 2017-12-28T02:15:53Z
dc.date.issued 2018-01-03
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50557
dc.description.abstract Biases in AI and machine learning algorithms are presented and analyzed through two issues management frameworks with the aim of showing how ethical problems and dilemmas can evolve. While "the singularity" concept in AI is presently more predictive than actual, both benefits and damage that can result by failure to consider biases in the design and development of AI. Inclusivity and stakeholder awareness regarding potential ethical risks and issues need to be identified during the design of AI algorithms to ensure that the most vulnerable in societies are protected from harm.
dc.format.extent 8 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Topics in Organizational Systems and Technology
dc.subject AI and Ethics, Machine Learning and Ethics
dc.title Ethical Implications of Bias in Machine Learning
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2018.668
Appears in Collections: Topics in Organizational Systems and Technology


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