[论文解读] Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits

Jack Bandy|arXiv (Cornell University)|Feb 3, 2021

Ethics and Social Impacts of AI参考文献 12被引用 32

一句话总结

该论文对500多篇论文进行PRISMA引导的范围性综述，将62项算法审计研究整合成四种行为分类（歧视、扭曲、利用、误判），并概述未来的审计方向。

ABSTRACT

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.

研究动机与目标

• 映射现有算法审计研究，识别常见的危害。
• 建立有问题的机器行为分类体系。
• 分析审计中涉及的领域、组织与审计方法。
• 识别研究空缺并提出面向算法公正性的未来审计方向。

提出的方法

• 使用 PRISMA 指南筛选 500 多篇论文以识别算法审计。
• 进行主题分析，按行为、领域、组织和审计方法进行编码。
• 使用两名编码者的归纳性主题分析，并计算科恩的凯泽系数的逐步进展。
• 定义四种有问题的行为：歧视、扭曲、利用、误判。
• 以关键词驱动的识别为补充，结合 Scopus 与其他来源以降低新近性偏差。

实验结果

研究问题

• RQ1 RQ1：先前的算法审计暴露了哪些类型的有问题的机器行为？
• RQ2 RQ2：未来的算法审计应研究算法在社会中行使权力的哪些有问题的方式？

主要发现

• 62项研究；大多数聚焦于歧视（21项）或扭曲（29项）。
• 审计涵盖搜索（25项）、广告（12项）和推荐（8项）。
• 识别出四种主要行为：歧视、扭曲、利用、误判。
• 广告与视觉领域显示出显著的歧视与扭曲模式。
• 代码审计研究尚未充分探索；对 Twitter、LinkedIn、TikTok 等平台的覆盖有限。
• 呼吁对交叉性歧视进行审计，并在更广泛的算法公正框架中定位审计。