Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Renee Shelby Google Research, JusTech Lab Australian National UniversitySan FranciscoCAUSA , Shalaleh Rismani McGill UniversityMontrealCanada , Kathryn Henne Australian National UniversityCanberraAustralia , AJung Moon McGill UniversityMontrealCanada , Negar Rostamzadeh Google ResearchMontrealCanada , Paul Nicholas GoogleSan FranciscoCAUSA , N’Mah Yilla-Akbari GoogleWashington, D.C.USA , Jess Gallegos Google ResearchNew York CityNYUSA , Andrew Smart Google ResearchSan FranciscoCAUSA , Emilio Garcia GoogleNew York CityNYUSA and Gurleen Virk GoogleSan DiegoCAUSA
Abstract
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.
中文速览
算法系统造成的社会危害种类繁多却缺乏系统整理，从业者在评估产品风险时往往无从下手。研究团队对172篇计算机领域文献进行了范围综述（scoping review）和反思性主题分析（reflexive thematic analysis），归纳出一套适用于算法系统的社会技术危害（sociotechnical harms）分类体系。该体系涵盖五大主题：表征性危害、资源分配危害、服务质量危害、人际危害，以及对社会系统与整体社会的危害，每类下设若干子主题并附有定义与示例。这项工作的价值在于：它为负责任AI领域提供了一个跨学科的通用语言框架，帮助研究人员和从业者在算法系统的开发全周期中更主动、更系统地识别和减少潜在危害。
原文 arXiv:2210.05791;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2210.05791v3