AI-assisted Decision-making and Consumers' Trust in AI Recommendations

Abstract

With the development of artificial intelligence (AI) in recent years, intelligent recommendation systems have become popular in daily life and are used to help people make decisions. AI recommendation systems will be of use only if consumers are willing to be guided by them. This paper studies consumers' trust in AI-generated recommendations under conditions of AI-assisted decision-making. Based on the literature of algorithm aversion, algorithm appreciation and trust in automation, this paper will present various psychological and cognitive reasons for changes in people's perceptions of algorithms. Research has shown that a sense of personalisation, relatively simple tasks and good explanations (explainable AI) can make people feel more trusted. Based on the above analysis, consumers are more likely to accept algorithms for objective and data-driven applications; however, they may be hesitant about algorithms used in cases of subjective judgment or high-stakes decisions, especially after learning of algorithmic errors. In addition, the above analysis also shows that emotional trust may be a mediator in the intention to delegate decision-making to AI agents. In short, this paper offers a theoretical discussion on algorithmic reliance and proposes strategies to build more transparent and trustworthy AI recommendation systems that can improve the user experience.