Nudges affect the perceived trustworthiness of algorithmic recommendations in public services: explaining by learning costs
研究比较了两种解释策略（显著解释和规范解释）如何通过降低学习成本来增强用户对政府使用的算法推荐系统的认知信任，发现规范解释对年轻用户更有效。
Purpose This study aims to identify the most effective explanatory strategies for building public trust in government-use AI-based algorithmic recommendations. Design/methodology/approach By comparing salient explanations and norm-based explanations across different age groups, we analyzed how these explanatory strategies reduce learning costs and enhance cognitive trust in the algorithm. Findings The study finds that both salient and norm-based explanations can reduce learning costs and enhance users’ cognitive trust in algorithms; however, norm-based explanations are particularly effective for younger users. Additionally, the study finds no significant interaction between the two types of explanations. Importantly, effective explanations can enhance both cognitive trust in the algorithm and affective trust in the government. Originality/value This research suggests that “nudges” in explanations can enhance citizens’ trust in algorithmic public services, which is significant for increasing acceptance of these algorithms.