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Shaping the Future of Generative AI for Black Communities: A Frame Analysis of Public Discourse and Empirical Scholarly Research — arXiv:2608.24767

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.24767
License (as archived): CC0-1.0
Archived: 2026-10-18T06:32:10.045Z
SHA-256 of the archived text: 7b358ad32b5dbb18895c052783c9a36ee00458302c50a8bbe38709861723cf65
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How to cite

Shaping the Future of Generative AI for Black Communities: A Frame Analysis of Public Discourse and Empirical Scholarly Research — arXiv:2608.24767 — arXiv (en), https://arxiv.org/abs/2608.24767 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d40636471bda (Metraipolis archive dc_d40636471bda, sha256 7b358ad32b5dbb18895c052783c9a36ee00458302c50a8bbe38709861723cf65)

BibTeX

@misc{dcd40636471bda,
  title = {Shaping the Future of Generative AI for Black Communities: A Frame Analysis of Public Discourse and Empirical Scholarly Research — arXiv:2608.24767},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.24767},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d40636471bda; sha256 7b358ad32b5dbb18895c052783c9a36ee00458302c50a8bbe38709861723cf65},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.24767. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As generative AI (genAI) systems become embedded in education, employment, healthcare, and creative industries, the impact and engagement among marginalized groups have become both a widespread discourse and a focus in scholarly research. As a starting point, we examine public discourse and empirical research to explore the impact of genAI systems on Black communities. We conducted a systematic literature review (SLR) of 91 empirical papers alongside a media discourse frame analysis of 28 public resources, applying Entman's framing theory to map how each corpus defines problems, attributes causes, and proposes treatments. Our SLR reveals that scholarly research concentrates heavily on technical bias detection, reducing Blackness to measurable variables rather than engaging with cultural practices, structural conditions, or Black knowledge systems. Our frame analysis reveals that public discourse attributes genAI-related harm to historical and systemic forces, while scholarly research stops its causal accounts at the dataset and its treatment recommendations at technical reform. We demonstrate that this misalignment is structurally produced: anti-Blackness operates simultaneously across both registers, generating a shared evacuation of Black epistemic agency. We argue for frame analysis as an AI ethics methodology capable of surfacing what technical evaluation forecloses.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-26 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2608.24767 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T06:28:16.182Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-08-26?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).