[论文解读] What is the Point of Fairness? Disability, AI and The Complexity of Justice

Cynthia L. Bennett, Os Keyes|arXiv (Cornell University)|Aug 2, 2019

Neuroethics, Human Enhancement, Biomedical Innovations参考文献 28被引用 66

一句话总结

本文批评了AI在残疾议题上的狭隘公平框架，提出两个计算机视觉案例研究，以主张更广泛的正义概念来解决结构性不公。

ABSTRACT

Work integrating conversations around AI and Disability is vital and valued, particularly when done through a lens of fairness. Yet at the same time, analyzing the ethical implications of AI for disabled people solely through the lens of a singular idea of "fairness" risks reinforcing existing power dynamics, either through reinforcing the position of existing medical gatekeepers, or promoting tools and techniques that benefit otherwise-privileged disabled people while harming those who are rendered outliers in multiple ways. In this paper we present two case studies from within computer vision - a subdiscipline of AI focused on training algorithms that can "see" - of technologies putatively intended to help disabled people but, through failures to consider structural injustices in their design, are likely to result in harms not addressed by a "fairness" framing of ethics. Drawing on disability studies and critical data science, we call on researchers into AI ethics and disability to move beyond simplistic notions of fairness, and towards notions of justice.

研究动机与目标

• 推动包容残疾的AI研究，超越单一公平概念。
• 突出仅以公平为导向可能强化现有权力动态在针对残疾人士的AI设计中的风险。
• 利用残疾研究与批判性数据科学分析现实世界案例中的AI伦理。

提出的方法

• 呈现两项涉及旨在帮助残疾人的技术的计算机视觉案例研究。
• 分析设计决策与对结构性不公关注不足如何在公平框架下仍然造成伤害。
• 借助残疾研究和批判性数据科学，批评当前的伦理方法。

实验结果

研究问题

• RQ1 以公平为焦点的AI伦理如何未能解决影响残疾人士的结构性不公？
• RQ2 哪些替代的正义框架能更好地捕捉AI系统在残疾情境中产生的伤害与不平等？
• RQ3 当前AI公平做法与准入壁垒的动态在何种方式再现权力失衡？

主要发现

• 仅仅的公平可能强化残疾情境中现有的医疗把关者与特权地位。
• 在设计中忽视结构性不公时，旨在帮助残疾人的AI系统可能造成伤害。
• 残疾研究和批判性数据科学倡导超越公平，走向更强健的正义概念。
• 案例研究说明视觉AI的失败未能考虑更广泛的正义考量。