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.

通过重温 Nissenbaum 文章中关于计算机化对责任追究带来的挑战，结合最新的关于关系型责任追究框架的研究，探讨了数据驱动算法系统所面临的困难，并讨论了削弱这些困难的方法。

机器学习中的相互关系、责任和鲁棒性：算法社会中的问责

Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning

We increasingly depend on a variety of data-driven algorithmic systems to
assist us in many aspects of life. Search engines and recommender systems
amongst others are used as sources of information and to help us in making all
sort of decisions from selecting restaurants and books, to choosing friends and
careers. This has given rise to important concerns regarding the fairness of
such systems. In this work, we aim at presenting a toolkit of definitions,
models and methods used for ensuring fairness in rankings and recommendations.
Our objectives are three-fold: (a) to provide a solid framework on a novel,
quickly evolving, and impactful domain, (b) to present related methods and put
them into perspective, and (c) to highlight open challenges and research paths
for future work.

本文旨在介绍确保排名和推荐中的公平性所使用的定义、模型和方法的工具包，提供有关这一新颖、迅速发展和有影响力的领域的坚实框架，将相关方法并置并强调未来工作的开放挑战和研究路径。