The bias dilemma: the ethics of algorithmic bias in natural-language processing

Oisín Deery, Katherine Bailey

    Research output: Contribution to journalArticlepeer-review

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    Abstract

    Addressing biases in natural-language processing (NLP) systems presents an underappreciated ethical dilemma, which we think underlies recent debates about bias in NLP models. In brief, even if we could eliminate bias from language models or their outputs, we would thereby often withhold descriptively or ethically useful information, despite avoiding perpetuating or amplifying bias. Yet if we do not debias, we can perpetuate or amplify bias, even if we retain relevant descriptively or ethically useful information. Understanding this dilemma provides for a useful way of rethinking the ethics of algorithmic bias in NLP.
    Original languageEnglish
    Pages (from-to)1-28
    Number of pages28
    JournalFeminist Philosophy Quarterly
    Volume8
    Issue number3/4
    DOIs
    Publication statusPublished - 21 Dec 2022

    Bibliographical note

    Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • artificial intelligence
    • algorithms
    • bias

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