Understanding the landscape of potential harms from algorithmic systems enables practitioners to better anticipate consequences of the systems they build. It also supports the prospect of incorporating controls to help minimize harms that emerge from the interplay of technologies and social and cultural dynamics. A growing body of scholarship has identified a wide range of harms across different algorithmic technologies. However, computing research and practitioners lack a high level and synthesized overview of harms from algorithmic systems. Based on a scoping review of computing research $(n=172)$, we present an applied taxonomy of sociotechnical harms to support a more systematic surfacing of potential harms in algorithmic systems. The final taxonomy builds on and refers to existing taxonomies, classifications, and terminologies. Five major themes related to sociotechnical harms - representational, allocative, quality-of-service, interpersonal harms, and social system/societal harms - and sub-themes are presented along with a description of these categories. We conclude with a discussion of challenges and opportunities for future research.

翻译：理解算法系统可能产生的危害全景，使从业者能够更有效地预判其构建系统的潜在后果。这也有助于通过实施控制措施，最大限度地减少技术与社会文化动态相互作用产生的危害。日益增长的研究成果已识别出不同算法技术中的广泛危害。然而，计算机领域的研究与实践仍缺乏对算法系统危害的高度概括性综合分析。基于对计算机研究文献的范围综述（样本量n=172），我们提出了一套应用型的社会技术危害分类体系，以支持更系统地揭示算法系统中的潜在危害。最终分类体系借鉴并引用了现有分类法、分类标准与术语体系。研究呈现了与社会技术危害相关的五大主题——表征性危害、分配性危害、服务质量危害、人际危害以及社会系统/社会危害，及相应子主题，并对各类别进行了描述。最后，我们讨论了未来研究面临的挑战与机遇。