SLR on Algorithmic Decision Making

Abstract

With the continuing application of artificial intelligence (AI) technologies into decision-making, algorithmic decision-making is becoming more efficient, even often outperforming human counterpart. Despite this superior performance, people often consciously or unconsciously display reluctance to rely on algorithms, a phenomenon known as algorithm aversion. Viewed as a behavioral anomaly, algorithm aversion has recently attracted much scholarly attention. With a view to synthesize the findings of this literature, we systematically review 80 empirical studies identified through searching in seven academic databases and performing citation chaining. We map the emergent themes following grounded theory and categorize the influencing factors of algorithm aversion under four main themes: algorithm, individual, task, and high-level. Our analysis reveals that although algorithm and individual factors have been investigated extensively, very little effort has been given to explore the task and high-level factors. We contribute to algorithm aversion literature by proposing a comprehensive framework, highlighting open issues in existing studies, and outlining several research avenues that could be handled in future research. Implications for research and practitioners about the findings of the study are discussed.