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dc.contributor.authorBradley, Robin
dc.date.accessioned2020-05-19T00:03:28Z
dc.date.available2020-05-19T00:03:28Z
dc.date.issued2020-02-17
dc.identifier.citationBradley , R 2020 , ' How to Solve AI Bias ' , Paper presented at Women in Tech - Bias in AI , London , United Kingdom , 17/02/20 - 17/02/20 .
dc.identifier.citationworkshop
dc.identifier.urihttp://hdl.handle.net/2299/22716
dc.description© 2020 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.description.abstractBias in AI is a topic that impacts machine learning and artificial intelligence technology that learns from datasets and its training data. While gender discrimination and chatbots showing bias have recently caught people’s attention and imagination, the overall area of how to correct and manage bias is in its infancy for business use. Further, little is known about how to solve bias in AI and how there could potent for malicious misuse at large scale. We explore this area and propose solutions to this problem.en
dc.format.extent7
dc.format.extent252302
dc.language.isoeng
dc.subjectArtificial intelligence
dc.subjectAI artificial intelligence
dc.subjectbias
dc.subjectMachine Learning
dc.subjectAlgorithm and data structure visualization
dc.subjectAlgorithms
dc.subjectethics and politics
dc.subjectEthics Committees, Research
dc.subjectethics; socially responsible investment
dc.subjecttesting methodology
dc.subjectArtificial Intelligence
dc.subjectComputer Vision and Pattern Recognition
dc.titleHow to Solve AI Biasen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionECS Computer Science VLs
dc.description.statusNon peer reviewed
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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