Subjects: Psychology >> Applied Psychology submitted time 2024-01-15
Abstract： As artificial intelligence continues to advance, algorithms find increasing applications across various domains, including education and transportation. In the realm of road traffic management, the escalating complexity of the traffic system poses challenges for traditional human monitoring, such as that carried out by traffic police. In light of this, there is a growing reliance on algorithmic monitoring, exemplified by electronic police systems. These systems offer extensive monitoring capabilities in terms of both time and space, providing an efficient means to uphold traffic order in the face of manual monitoring limitations. In order to enhance individuals’ inclination and adherence to traffic rules, algorithmic monitoring serves as a compelling alternative to address the shortcomings of manual monitoring, which often involves blind spots and high operational costs. Observations from daily life experiences suggest that the broad coverage of algorithmic monitoring has a mitigating effect on traffic rule violations. Despite this intuitive understanding, there exists a notable gap in empirical research to substantiate these observations. While numerous studies have delved into the impact of algorithms, yielding findings related to both algorithm appreciation and aversion, there remains a need for a focused investigation into the specific influence of algorithmic monitoring on compliance with traffic rules and an exploration of the underlying mechanisms.
This research aims to address this gap by providing a nuanced understanding of how algorithmic monitoring shapes individuals’ behavior in the context of obeying traffic rules. Drawing on construal-level theory, prior research has consistently shown that individuals tend to perceive humans at a high level of construal, while algorithms are typically construed at a low level. Moreover, traffic behaviors are also construed at different levels. Recognizing that the alignment of construal levels between the agent and behavior plays a pivotal role in influencing individuals, this paper posits a hypothesis: the effect of algorithmic monitoring on compliance with traffic rules hinges on the construal level of the traffic behavior, and the fit between the monitoring agent and monitored behavior acts as a mediating factor in this relationship. To address these considerations and test the proposed hypothesis, a preliminary investigation was conducted, selecting traffic behaviors with distinct construal levels (e.g., overspeed behavior as a low construal-level, and failure to give way to pedestrians as a high construal-level). In Study 1a, a situational test involving the de We found that for traffic behaviors with low construal levels, compared to human monitoring, people had a stronger sense of fit with algorithmic monitoring, thereby enhancing their intention to comply with traffic rules. Conversely, for traffic behaviors with high construal levels, there was a stronger sense of fit under human monitoring, leading to a greater compliance intention with traffic rules. In summary, the monitoring agent influences individuals’ intention to comply with traffic rules for behaviors at different construal levels, with the sense of fit playing an intermediate role. Further, after committing an error, the sense of fit induced by algorithmic monitoring decreased to a level comparable to human monitoring. Additionally, the positive effect on the intention to comply with traffic rules for behaviors with low construal levels disappeared. However, following an error in human monitoring, its monitoring effectiveness (i.e., compliance with traffic rules) for behaviors with high construal levels remained superior to that of algorithmic monitoring. Moreover, the mediating role of the sense of fit persisted. In essence, the monitoring effectiveness of algorithms is more significantly influenced by error information.
In summary, the enhancing effect of algorithmic monitoring on the intention to comply with traffic rules depends on the construal level of the observed traffic behavior, with the sense of fit playing a mediation role. Errors in algorithmic monitoring weaken its monitoring effectiveness for traffic behaviors with low construal levels. Hence, when the traffic management department chooses the monitoring agent, it should avoid indiscriminately using either algorithmic or human monitoring but consider the construal levels of traffic violations observed at the intersection.
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