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

• URL: http://arxiv.org/abs/2202.05338v3
• Date: Fri, 13 May 2022 23:55:48 GMT
• Title: Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning
• Authors: A. Feder Cooper and Emanuel Moss and Benjamin Laufer and Helen Nissenbaum
• Abstract summary: In 1996, Nissenbaum issued a clarion call concerning the erosion of accountability in society due to the ubiquitous delegation of consequential functions to computerized systems. We revisit Nissenbaum's original paper in relation to the ascendance of data-driven algorithmic systems. We discuss how the barriers present difficulties for instantiating a unified moral, relational framework in practice for data-driven algorithmic systems.
• Score: 4.958893997693021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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