A Sociotechnical View of Algorithmic Fairness

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.

Accepted at Information Systems Journal

References in corpus (20)

• Equality of Opportunity in Supervised Learning
• Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
• Towards a Critical Race Methodology in Algorithmic Fairness
• On Fairness and Calibration
• Flexibly Fair Representation Learning by Disentanglement
• Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities
• From Parity to Preference-based Notions of Fairness in Classification
• Clustering without Over-Representation
• What's Sex Got To Do With Fair Machine Learning?
• Scalable Fair Clustering
• Mediating Community-AI Interaction through Situated Explanation: The Case of AI-Led Moderation
• Guarantees for Spectral Clustering with Fairness Constraints
• PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
• Fairwashing Explanations with Off-Manifold Detergent
• Exploring Algorithmic Fairness in Robust Graph Covering Problems
• On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
• Fair Regression with Wasserstein Barycenters
• Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards
• Learning Fairness in Multi-Agent Systems
• A Pairwise Fair and Community-preserving Approach to k-Center Clustering