Human Cognitive Bias Mitigation Approaches to Fairness within the Machine Learning Value Chain: A Review and Research Agenda

Abstract

This systematic review examines the influence of human cognitive biases on machine learning (ML) systems across the 9 phases of the ML algorithmic value chain. Following the PRISMA guidelines, it synthesizes 19 studies on bias integration and management within ML, highlighting techniques to reduce bias and increase fairness. The review identifies key gaps: the unclear translation of human cognitive biases to ML biases, absence of metrics to measure biases, re-introduction of biases during debiasing, and the critical need for human intervention. These findings prompt several research themes spanning human cognition and algorithmic bias. The theoretical implications are three-fold: extending bias concepts to human cognition, creating an agenda to associate cognitive biases with ML outcomes, and assessing the need for a new or extended discipline. Practically, it raises awareness of human cognition in ML fairness, leading to improved methods for data handling