A growing body of literature has proposed formal approaches to audit algorithmic systems for biased and harmful behaviors. While formal auditing approaches have been greatly impactful, they often suffer major blindspots, with critical issues surfacing only in the context of everyday use once systems are deployed. Recent years have seen many cases in which everyday users of algorithmic systems detect and raise awareness about harmful behaviors that they encounter in the course of their everyday interactions with these systems. However, to date little academic attention has been granted to these bottom-up, user-driven auditing processes. In this paper, we propose and explore the concept of everyday algorithm auditing, a process in which users detect, understand, and interrogate problematic machine behaviors via their day-to-day interactions with algorithmic systems. We argue that everyday users are powerful in surfacing problematic machine behaviors that may elude detection via more centrally-organized forms of auditing, regardless of users' knowledge about the underlying algorithms. We analyze several real-world cases of everyday algorithm auditing, drawing lessons from these cases for the design of future platforms and tools that facilitate such auditing behaviors. Finally, we discuss work that lies ahead, toward bridging the gaps between formal auditing approaches and the organic auditing behaviors that emerge in everyday use of algorithmic systems.

翻译：越来越多的文献提出了审计偏向和有害行为的算法系统的正式方法。虽然正式审计方法产生了很大的影响,但它们往往会遭遇重大的盲点,在系统部署后,关键的问题只在日常使用的背景下才会显现。近年来,在很多情况下,算法系统日常用户在与这些系统的日常互动过程中发现并提高对有害行为的认识。然而,迄今为止,这些自下而上、用户驱动的审计程序很少引起学术关注。在本文件中,我们提出并探索日常算法审计的概念,用户通过日常与算法系统的互动来发现、理解和调查有问题的机器行为。我们争论说,日常用户在通过更集中化的审计形式冲洗有问题的机器行为方面力量很大,而这种行为可能无法通过更集中化的审计形式被察觉。我们分析了几个真实世界的日常算法审计案例,从中吸取了这些案例的经验教训,用于设计有助于此类审计行为的未来平台和工具。最后,我们讨论了如何利用日常审计方法来弥补有机审计系统之间出现的风险。