Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning
This work addresses accountability issues in algorithmic systems for society, but it is incremental as it builds on existing philosophical and relational frameworks.
The paper revisits Nissenbaum's 1996 barriers to accountability in computerized systems, analyzing how data-driven algorithmic systems like machine learning present new challenges for accountability, and discusses ways to weaken these barriers to implement a unified moral, relational framework.
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.