English

A Sociotechnical View of Algorithmic Fairness

Computers and Society 2021-10-19 v1 Machine Learning Machine Learning

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.

Keywords

Cite

@article{arxiv.2110.09253,
  title  = {A Sociotechnical View of Algorithmic Fairness},
  author = {Mateusz Dolata and Stefan Feuerriegel and Gerhard Schwabe},
  journal= {arXiv preprint arXiv:2110.09253},
  year   = {2021}
}

Comments

Accepted at Information Systems Journal

R2 v1 2026-06-24T06:58:27.349Z