Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types
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DOI: 10.1371/journal.pone.0231189
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References listed on IDEAS
- Rachel Courtland, 2018. "Bias detectives: the researchers striving to make algorithms fair," Nature, Nature, vol. 558(7710), pages 357-360, June.
- Nikhil Garg & Londa Schiebinger & Dan Jurafsky & James Zou, 2018. "Word embeddings quantify 100 years of gender and ethnic stereotypes," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(16), pages 3635-3644, April.
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- Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
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