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arXiv 2610.31344: Making Generative AI Workable at Work: Accountability, Localization, and Representation in Kenyan and Nigerian Small Businesses

Source: Metraipolis archive
Original: https://arxiv.org/abs/2610.31344
License (as archived): CC-BY-4.0
Archived: 2026-10-25T00:10:09.472Z
SHA-256 of the archived text: b9a415ab1afeb944b8fa6e98d1377f34ae8e46f1f21f1c098ae2912437126273
Stable URL of this page: https://metraipolis.com/documento/dc_e4b29ea649cd

How to cite

arXiv 2610.31344: Making Generative AI Workable at Work: Accountability, Localization, and Representation in Kenyan and Nigerian Small Businesses — Metraipolis archive, https://arxiv.org/abs/2610.31344 (CC-BY-4.0); archived copy: https://metraipolis.com/documento/dc_e4b29ea649cd (Metraipolis archive dc_e4b29ea649cd, sha256 b9a415ab1afeb944b8fa6e98d1377f34ae8e46f1f21f1c098ae2912437126273)

BibTeX

@misc{dce4b29ea649cd,
  title = {arXiv 2610.31344: Making Generative AI Workable at Work: Accountability, Localization, and Representation in Kenyan and Nigerian Small Businesses},
  howpublished = {Metraipolis archive},
  url = {https://arxiv.org/abs/2610.31344},
  note = {License CC-BY-4.0; archived copy https://metraipolis.com/documento/dc_e4b29ea649cd; sha256 b9a415ab1afeb944b8fa6e98d1377f34ae8e46f1f21f1c098ae2912437126273},
  year = {2026}
}

Archived text

Reproduced under CC-BY-4.0. Attribution: Metraipolis archive, https://arxiv.org/abs/2610.31344. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generative AI is widely framed as democratizing skilled work by lowering the barrier to producing professional output. Drawing on 21 semi-structured interviews with 27 workers across 21 small and medium-sized businesses (SMBs) in Kenya and Nigeria (July to October 2023), we find that availability alone did not make participation workable. In participants' accounts, drafting was rarely the hard part. The consequential work was making AI-assisted drafts defensible enough to circulate to clients. We identify five intertwined practices through which participants made generative AI workable: using it as draft labour, steering outputs through iterative interaction, switching and comparing across tools, performing release work to make outputs accountable before circulation, and repairing context misfit when outputs failed local requirements. We make two central contributions. First, we specify release work as a distinct, boundary-facing form of accountability labour, evidenced through verification gates, traceability reconstruction when sources fail, and non-delegation boundaries that decide what can ship. Second, we show how localization and representational failures, which we call context misfit, operate in everyday work as a recurring breakdown-and-repair class, including failures of local language, institutional knowledge, and the credible representation of African people and places, and we document the repair repertoire they demanded. Reframing democratization as workable participation, we argue that reported gains were conditional on this accountability and repair labour, and we translate the findings into four design directions for workplace generative AI. We treat Kenya and Nigeria as two situated cases, not as a representative sample of SMBs or the Global South.

Articolo: «Making Generative AI Workable at Work: Accountability, Localization, and Representation in Kenyan and Nigerian Small Businesses» — Abdulhamid, Najeeb Gambo; Nyairo, Stephanie; Ankrah, Liz; O'Neill, Jacki (2026-10-22), arXiv:2610.31344, categoria cs.CY (informatica e società).
Fonte: https://arxiv.org/abs/2610.31344
Licenza dell'articolo indicata su arXiv: CC-BY-4.0 — https://creativecommons.org/licenses/by/4.0. Qui solo titolo, autori e abstract (non modificato), con attribuzione; il testo integrale resta su arXiv.
Consultato il 2026-10-25T00:10:05Z dalla pagina pubblica dell'articolo (robots.txt rispettato). Depositato dalla Gilda dei Bibliotecari. Abstract in lingua originale.