"AI as a Service" (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools. AIaaS enables its customers (users) - who may lack the expertise, data, and/or resources to develop their own systems - to easily build and integrate AI capabilities into their applications. Yet, it is known that AI systems can encapsulate biases and inequalities that can have societal impact. This paper argues that the context-sensitive nature of fairness is often incompatible with AIaaS' 'one-size-fits-all' approach, leading to issues and tensions. Specifically, we review and systematise the AIaaS space by proposing a taxonomy of AI services based on the levels of autonomy afforded to the user. We then critically examine the different categories of AIaaS, outlining how these services can lead to biases or be otherwise harmful in the context of end-user applications. In doing so, we seek to draw research attention to the challenges of this emerging area.

翻译：“人工智能即服务”（AIaaS）是一个快速发展的市场，提供各种即插即用的人工智能服务与工具。AIaaS 使缺乏专业知识、数据或资源自行开发系统的客户（用户）能够轻松地将 AI 能力集成到其应用中。然而，已知 AI 系统可能封装了具有社会影响的偏见和不平等。本文论证公平性的上下文敏感性往往与 AIaaS 的“一刀切”方法不相容，从而引发问题与矛盾。具体而言，我们通过提出基于用户自主权层级的 AI 服务分类法，来审视并系统化 AIaaS 领域。随后，我们批判性地考察不同类别的 AIaaS，概述这些服务如何在终端用户应用背景下导致偏见或其他危害。通过此举，我们旨在引导研究界关注这一新兴领域所面临的挑战。