Ethical Implications of Bias in Machine Learning

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dc.contributor.authorYapo, Adrienne
dc.contributor.authorWeiss, Joseph
dc.date.accessioned2017-12-28T02:15:53Z
dc.date.available2017-12-28T02:15:53Z
dc.date.issued2018-01-03
dc.description.abstractBiases 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.extent8 pages
dc.identifier.doi10.24251/HICSS.2018.668
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50557
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTopics in Organizational Systems and Technology
dc.subjectAI and Ethics, Machine Learning and Ethics
dc.titleEthical Implications of Bias in Machine Learning
dc.typeConference Paper
dc.type.dcmiText

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