[论文解读] Understanding bias in facial recognition technologies

David Leslie|arXiv (Cornell University)|Sep 26, 2020

Ethics and Social Impacts of AISocial Sciences被引用 21

一句话总结

本文探讨了历史歧视模式如何将偏见嵌入人脸识别技术（FDRTs）中，导致分配不公与识别不公。文章分析了FDRTs中偏见的社会技术成因，评估了普遍监控的伦理影响，并提出了更负责任地开发和部署这些系统治理框架。

ABSTRACT

Over the past couple of years, the growing debate around automated facial recognition has reached a boiling point. As developers have continued to swiftly expand the scope of these kinds of technologies into an almost unbounded range of applications, an increasingly strident chorus of critical voices has sounded concerns about the injurious effects of the proliferation of such systems. Opponents argue that the irresponsible design and use of facial detection and recognition technologies (FDRTs) threatens to violate civil liberties, infringe on basic human rights and further entrench structural racism and systemic marginalisation. They also caution that the gradual creep of face surveillance infrastructures into every domain of lived experience may eventually eradicate the modern democratic forms of life that have long provided cherished means to individual flourishing, social solidarity and human self-creation. Defenders, by contrast, emphasise the gains in public safety, security and efficiency that digitally streamlined capacities for facial identification, identity verification and trait characterisation may bring. In this explainer, I focus on one central aspect of this debate: the role that dynamics of bias and discrimination play in the development and deployment of FDRTs. I examine how historical patterns of discrimination have made inroads into the design and implementation of FDRTs from their very earliest moments. And, I explain the ways in which the use of biased FDRTs can lead distributional and recognitional injustices. The explainer concludes with an exploration of broader ethical questions around the potential proliferation of pervasive face-based surveillance infrastructures and makes some recommendations for cultivating more responsible approaches to the development and governance of these technologies.

研究动机与目标

• 调查系统性种族主义和历史歧视如何影响人脸识别技术的设计与部署。
• 分析有偏见的FDRTs在分配不公与识别不公方面的后果。
• 审视在民主社会中扩大面部监控基础设施的伦理影响。
• 提出可操作的建议，以实现人脸识别技术的负责任开发与治理。

提出的方法

• 对人脸识别系统进行批判性的社会技术分析，追溯偏见源于历史与结构性不平等的根源。
• 分析FDRTs的技术流程，从数据收集到模型部署，识别偏见引入的环节。
• 运用伦理框架评估有偏见系统对边缘化群体和公民自由的影响。
• 评估人脸识别技术被滥用的真实案例，以说明系统性风险。
• 提出伦理治理原则，包括透明度、问责制和监督机制。
• 整合计算机科学、社会学与伦理学的洞见，倡导在FDRTs中采用以人为本的设计。

实验结果

研究问题

• RQ1 历史歧视模式如何影响人脸识别技术的发展？
• RQ2 有偏见的FDRTs以何种方式导致分配不公与识别不公？
• RQ3 扩大普遍性基于面部的监控基础设施会带来哪些社会风险？
• RQ4 如何设计伦理治理框架以减轻FDRT部署中的伤害？
• RQ5 数据收集实践与算法设计在何种程度上加剧了偏见？

主要发现

• 人脸识别技术中的偏见并非偶然，而是根植于历史与结构性不平等，尤其体现在数据收集与标注过程中。
• FDRTs对女性和人种少数群体的误识率更高，导致实际伤害，如错误逮捕与监控过度。
• 面部监控的扩展威胁到民主规范，因其可能引发不受约束的监控，损害隐私与自主权。
• 当个体因算法偏见被错误分类或拒绝承认时，便发生识别不公，影响其获取服务与权利。
• 当前系统往往缺乏透明度与问责制，使得难以挑战或纠正有偏见的结果。
• 伦理治理需要跨学科协作、包容性设计以及可执行的监督机制，以预防伤害并确保公平。