Practitioners from diverse occupations and backgrounds are increasingly using machine learning (ML) methods. Nonetheless, studies on ML Practitioners typically draw populations from Big Tech and academia, as researchers have easier access to these communities. Through this selection bias, past research often excludes the broader, lesser-resourced ML community -- for example, practitioners working at startups, at non-tech companies, and in the public sector. These practitioners share many of the same ML development difficulties and ethical conundrums as their Big Tech counterparts; however, their experiences are subject to additional under-studied challenges stemming from deploying ML with limited resources, increased existential risk, and absent access to in-house research teams. We contribute a qualitative analysis of 17 interviews with stakeholders from organizations which are less represented in prior studies. We uncover a number of tensions which are introduced or exacerbated by these organizations' resource constraints -- tensions between privacy and ubiquity, resource management and performance optimization, and access and monopolization. Increased academic focus on these practitioners can facilitate a more holistic understanding of ML limitations, and so is useful for prescribing a research agenda to facilitate responsible ML development for all.

翻译：不同职业和背景的从业者越来越多地使用机器学习(ML)方法,然而,关于ML从业者的研究通常吸引来自大科技和学术界的人,因为研究人员较容易进入这些社区。通过这种选择偏见,过去的研究往往排除了较广泛、资源较少的ML社区 -- -- 例如,在初创企业、非技术公司和公共部门工作的从业者。这些从业者与大科技同行有着许多同样的ML发展困难和伦理难题;然而,由于在资源有限、存在风险增加和无法进入内部研究团队的情况下部署ML,他们的经历受到更多研究不足的挑战。我们协助对17次与以前研究中代表较少的组织利益攸关方的访谈进行了定性分析。我们发现,由于这些组织的资源限制,出现了一些紧张状况,即隐私和普遍性、资源管理和绩效优化、以及获取和垄断之间的紧张关系。对这些从业者更多的学术关注有助于更全面地了解ML的局限性,因此有助于制定研究议程,以促进所有人都能负责任地发展ML。