1 + 1 > 2? Information, Humans, and Machines

Abstract

Our study, conducted through a field experiment with a major Asian microloan company, examines the interaction between information complexity and machine explanations in human–machine collaboration. We find that human evaluators’ loan approval decision-making outcomes are significantly enhanced when they are equipped with both large information volumes and machine-generated explanations, underscoring the limitations of relying solely on human intuition or machine analysis. This blend fosters deep human engagement and rethinking, effectively reducing gender biases and increasing prediction accuracy by identifying overlooked data correlations. Our findings stress the crucial role of combining human discernment with artificial intelligence to improve decision-making efficiency and fairness. We offer specific training and system design strategies to bolster human–machine collaboration, advocating for a balanced integration of technological and human insights to navigate intricate decision-making scenarios efficiently. Specifically, the study suggests that, whereas machines manage borderline cases, humans can significantly contribute by reevaluating and correcting machine errors in random cases (i.e., those without explicitly congruent feature patterns) through stimulated active rethinking triggered by strategic information prompts. This approach not only amplifies the strengths of both humans and machines, but also ensures more accurate and fair decision-making processes.