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How AI hype impacts the LGBTQ+ community

McAra-Hunter, Dawn·2024·AI and Ethics
DOI 10.1007/s43681-024-00423-8
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Abstract

Hype around Artificial Intelligence (AI) has been a feature of this technology since its inception. However, the most recent wave of AI hype has been leveraged to encourage adoption of AI technologies that cause issues for marginalised communities. Hype is also a means to obfuscate real issues of bias, harm, and exploitation felt most sharply by marginalised communities when AI is implemented. This therefore raises the question of power imbalances as a feature of AI technologies as we currently know them. This paper will study the relationship of AI hype and marginalised communities, with particular emphasis on the LGBTQ + community, and look at the way that AI impacts on this community. This paper will pose two key questions: does hype affect marginalised communities, particularly hype around new technologies such as AI; and what impact does the LGBTQ + community experience as a result of hype. This paper will then move on to discuss areas that provide a focus for discourse of AI hype and the impact on the LGBTQ + community: policy and decision-making, the maintenance of the cisgender heteronormative (cishet) baseline, the ubiquity of a mythology of AI, and the role of market expansion.

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Metadata

Authors
McAra-Hunter, D.
ORCID
McAra-Hunter, Dawn
Venue
AI and Ethics
Publisher
Springer Science and Business Media LLC
Published
2024-02-14
Volume
4
Issue
3
Pages
771-790
ISSN
  • 2730-5953
  • 2730-5961
DOI
10.1007/s43681-024-00423-8
Cited by
14
References
14
Language
en
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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