论文标题
算法社会的问责制：机器学习中的关系，责任和鲁棒性
Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning
论文作者
论文摘要
1996年，计算机社会的问责制[95]发出了一个关于社会侵蚀的克拉里昂呼吁，该责任是由于无处不在的相应功能授予计算机系统的。 Nissenbaum [95]描述了计算机化提出的问责制的四个障碍，我们将其与数据驱动的算法系统的升级有关 - 即机器学习或人工智能 - 揭示这些系统所存在的责任感的新挑战。尼森鲍姆（Nissenbaum）的原始论文对道德哲学的障碍进行了讨论；我们将这一分析与有关关系问责制框架的最新奖学金结合在一起，并讨论障碍如何在实践中实例化数据驱动算法系统实例化统一的道德，关系框架。我们通过讨论削弱障碍的方法来结束。
In 1996, Accountability in a Computerized Society [95] issued a clarion call concerning the erosion of accountability in society due to the ubiquitous delegation of consequential functions to computerized systems. Nissenbaum [95] described four barriers to accountability that computerization presented, which we revisit in relation to the ascendance of data-driven algorithmic systems--i.e., machine learning or artificial intelligence--to uncover new challenges for accountability that these systems present. Nissenbaum's original paper grounded discussion of the barriers in moral philosophy; we bring this analysis together with recent scholarship on relational accountability frameworks and discuss how the barriers present difficulties for instantiating a unified moral, relational framework in practice for data-driven algorithmic systems. We conclude by discussing ways of weakening the barriers in order to do so.
