有问题的机器行为：算法审计的系统性文献综述
Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits
摘要：
虽然算法审计在通用性和公共重要性方面正在迅速增长，但相对较少的学术工作已经综合了以前的工作并为该领域的未来研究制定了战略。本系统性文献综述的目的就是为了做到这一点，遵循PRISMA指南，对500多篇英文文章进行了综述，产生了62项算法审计研究。这些研究主要按行为（歧视、扭曲、剥削和误判）进行综合和组织，还提供了域代码（例如搜索、视觉、广告等），组织（例如Google、Facebook、Amazon等），和审计方法（例如，傀儡、直接刮取、众包等）。该评论显示了以前的审计研究如何暴露出表现出问题行为的面向公众的算法，例如搜索算法应受到扭曲，广告算法应受到歧视。根据所审查的研究，它还提出了一些行为（例如基于交叉身份的歧视），领域（例如广告算法），方法（例如代码审计）和组织（例如Twitter，TikTok，LinkedIn），这些都需要未来的审计关注。最后，本文提供了成功审计的共同要素，并讨论了算法审计的背景下，更广泛的研究工作对算法的正义。
英文摘要：
While algorithm audits are growing rapidly in commonality and public importance, relatively little scholarly work has gone toward synthesizing prior work and strategizing future research in the area. This systematic literature review aims to do just that, following PRISMA guidelines in a review of over 500 English articles that yielded 62 algorithm audit studies. The studies are synthesized and organized primarily by behavior (discrimination, distortion, exploitation, and misjudgement), with codes also provided for domain (e.g. search, vision, advertising, etc.), organization (e.g. Google, Facebook, Amazon, etc.), and audit method (e.g. sock puppet, direct scrape, crowdsourcing, etc.). The review shows how previous audit studies have exposed public-facing algorithms exhibiting problematic behavior, such as search algorithms culpable of distortion and advertising algorithms culpable of discrimination. Based on the studies reviewed, it also suggests some behaviors (e.g. discrimination on the basis of intersectional identities), domains (e.g. advertising algorithms), methods (e.g. code auditing), and organizations (e.g. Twitter, TikTok, LinkedIn) that call for future audit attention. The paper concludes by offering the common ingredients of successful audits, and discussing algorithm auditing in the context of broader research working toward algorithmic justice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
