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

TL;DR

This paper examines the challenges of accountability in society due to the rise of data-driven machine learning systems, analyzing barriers and proposing ways to strengthen accountability frameworks.

Contribution

It revisits classic barriers to accountability in the context of modern AI, integrating moral philosophy with relational accountability frameworks to address new challenges.

Findings

Identifies four barriers to accountability in algorithmic systems.

Analyzes how these barriers hinder moral and relational accountability.

Proposes strategies to weaken barriers and improve accountability practices.

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.