Literature Review and Research Prospects on Automation Bias in the Use of Artificial Intelligence
Artificial intelligence(AI)-assisted decision-making significantly enhances managerial effectiveness.However,when AI makes errors,uncritical adoption of their suggestions by decision-makers can lead to severe consequences.This paper systematically reviews the concept,behavioral patterns,mechanisms,and influencing factors of automation bias in the context of AI utilization,using a comprehensive literature review approach.The findings highlight a growing trend of cross-domain development in automation bias,characterized by excessive reliance on AI,reduced vigilance,neglect of verification,and individual differences.The key factors influencing automation bias can be grouped into four dimensions:system,environment,organization,and individual.Specific elements include the systems'ability to provide predictive information and immediate error feedback,the design of decision support systems,task complexity,workload,time constraints,accountability for overall performance or decision accuracy,trust and confidence,knowledge level,and capabilities.Future research should focus on empirical studies exploring the effects of AI automation bias on corporate managers,incorporating system,environment,organization,and individual factors to uncover its underlying mechanisms.Moreover,the development of effective debiasing strategies is essential to help decision-makers utilize AI responsibly and accurately in decision-making processes.
artificial intelligenceautomation biasautomated systemdecision supportliterature reviewresearch statusresearch outlook
万方数据
维普
