Fairness and Abstraction in Sociotechnical Systems
Fairness and Abstraction in Sociotechnical Systems
复制标题
DOI：
10.1145/3287560.3287598
复制
发表时间：
2019-01
期刊：
影响因子：
--
通讯作者：
Andrew D. Selbst;D. Boyd;Sorelle A. Friedler;Suresh Venkatasubramanian;J. Vertesi
中科院分区：
文献类型：
--
作者：
Andrew D. Selbst;D. Boyd;Sorelle A. Friedler;Suresh Venkatasubramanian;J. Vertesi
公平机器学习社区的一个关键目标是开发基于机器学习的系统，一旦引入社会环境，就可以实现社会和法律的结果，如公平，正义和正当程序。计算机科学中的基础概念-如抽象和模块化设计-被用来定义公平和歧视的概念，产生公平意识的学习算法，并在决策流程的不同阶段进行干预，以产生“公平”的结果。然而，在本文中，我们认为，这些概念使技术干预无效，不准确，有时甚至是危险的误导，当他们进入围绕决策系统的社会背景。我们用五个“陷阱”概述了这种不匹配，即使与传统数据科学相比，公平机器学习工作试图更具上下文感知能力，也可能陷入这些陷阱。我们利用科学技术研究中的社会技术系统的研究来解释为什么会出现这种陷阱以及如何避免它们。最后，我们提出了技术设计师可以通过重新关注设计过程而不是解决方案来减轻陷阱的方法，并通过绘制抽象边界来包括社会参与者而不是纯粹的技术参与者。
A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science---such as abstraction and modular design---are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones.