arXiv cs.CY· Najeeb Gambo Abdulhamid, Stephanie Nyairo, Liz Ankrah, Jacki O'Neill·· 13 小时前AI 评分34
肯尼亚与尼日利亚中小企业如何让生成式 AI 真正可用：问责、本地化与代表性
Making Generative AI Workable at Work: Accountability, Localization, and Representation in Kenyan and Nigerian Small Businesses
AI 导读
对肯尼亚和尼日利亚 21 家中小企业 27 名员工的研究发现，生成式 AI 仅"可用"并不足以让参与变得可行，起草很少是难点，关键在于让 AI 草稿足够可靠以便发给客户。研究识别出五种交织实践：作为起草劳动力、通过迭代交互引导输出、跨工具切换比较、发布前的问责工作，以及修复本地需求不匹配的"语境错配"。作者将发布工作界定为一种面向边界的问责劳动，并据此提出四条职场生成式 AI 设计方向。
正文 · 原文
Abstract: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.
| Subjects: | Human-Computer Interaction (cs.HC); Computers and Society (cs.CY) |
| ACM classes: | H.5.3 |
| Cite as: | arXiv:2610.31344 [cs.HC] |
| (or arXiv:2610.31344v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31344 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Najeeb G Abdulhamid [view email]
[v1]
Thu, 22 Oct 2026 14:26:52 UTC (44 KB)
来源：arXiv cs.CY · arxiv.org