A Sociotechnical View of Algorithmic Fairness

TL;DR

This paper argues that algorithmic fairness should be understood as a sociotechnical phenomenon, emphasizing the mutual influence of social and technical factors to develop more holistic solutions to systemic biases.

Contribution

It theorizes algorithmic fairness as a sociotechnical construct and provides a systematic analysis of existing literature to challenge current assumptions.

Findings

Problems with current fairness assumptions identified

Algorithmic fairness viewed as sociotechnical phenomenon

Guidelines for integrating social and technical insights

Abstract

Algorithmic fairness has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making, providing opportunities to improve fairness in information systems (IS). However, based on a state-of-the-art literature review, we argue that fairness is an inherently social concept and that technologies for algorithmic fairness should therefore be approached through a sociotechnical lens. We advance the discourse on algorithmic fairness as a sociotechnical phenomenon. Our research objective is to embed AF in the sociotechnical view of IS. Specifically, we elaborate on why outcomes of a system that uses algorithmic means to assure fairness depends on mutual influences between technical and social structures. This perspective can generate new insights that integrate knowledge from both technical fields and social studies. Further, it spurs new directions for IS debates. We contribute as follows: First, we problematize fundamental assumptions in the current discourse on algorithmic fairness based on a systematic analysis of 310 articles. Second, we respond to these assumptions by theorizing algorithmic fairness as a sociotechnical construct. Third, we propose directions for IS researchers to enhance their impacts by pursuing a unique understanding of sociotechnical algorithmic fairness. We call for and undertake a holistic approach to AF. A sociotechnical perspective on algorithmic fairness can yield holistic solutions to systemic biases and discrimination.