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“Fairness for unobserved characteristics: insights from technological impacts on queer communities”.

Tomasev, Nenad, McKee, Kevin R., Kay, Jackie, Mohamed, Shakir·2021·Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
DOI 10.1145/3461702.3462540
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  • The same digital platforms that offer community, affirmation, and support can simultaneously expose LGBTQ+ young people to harassment, discrimination, and identity-based harm, sometimes through deliberate hostility, but often through algorithmic bias and exclusionary design.

    “The same digital platforms that offer community, affirmation, and support can simultaneously expose young people to harassment, discrimination, and identity-based harm, sometimes through deliberate hostility, but often through the quieter and more pervasive mechanisms of algorithmic bias and exclusionary design (Tomasev et al., 2021;McAra-Hunter, 2024).”

    From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Tomasev et al., 2021

  • Tomasev et al. (2021) documented how LGBTQ+ content is disproportionately flagged, restricted, or removed by automated moderation systems because algorithms trained on majority-group data treat LGBTQ+ expression as anomalous or inappropriate.

    “Tomasev et al. (2021) documented how LGBTQ+ content is disproportionately flagged, restricted, or removed by automated moderation systems, not because it is harmful, but because algorithms trained on majority-group data treat LGBTQ+ expression as anomalous or inappropriate.”

    From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Tomasev et al. (2021)

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Cited by

Sorted by citations

Metadata

Authors
Tomasev, N., McKee, K. R., Kay, J., & Mohamed, S.
Affiliations
  • DeepMind, London, United Kingdom
  • DeepMind & University College London, London, United Kingdom
Venue
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
Publisher
ACM
Published
2021-07-21
Pages
254-265
DOI
10.1145/3461702.3462540
Cited by
70
References
15
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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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