2023/11/06 by Wen, Wen, Yang, Shuo, MOSHE ZUKERMAN +1
Psychology · #Behavioral Economics #Cognition and Perception #Economics #FOS: Psychology #Psychology #Public Administration #Public Affairs #Public Policy and Public Administration #Science and Technology Studies #Social and Behavioral Sciences #Transportation #acceptance of AI #algorithmic transparency #autonomous vehicle #survey experiment #trust in AI
paper · doi:10.17605/osf.io/wu4yd
This study aims to analyze the impact of algorithm transparency on public trust and acceptance for autonomous vehicles. To achieve this, we will employ a 2×2×2 experimental design that encompasses the following key elements: (i) We will investigate how two facets of algorithm transparency, namely algorithm accessibility and algorithm explainability, influence trust in autonomous algorithmic systems and vehicles. To gauge their effects, we will introduce two hypothetical accident scenarios, “route planning error” and “collision accident”, and assess whether these transparency components can enhance trust in AI system and trust in autonomous vehicles within these scenarios. (ii) We will introduce a set of questions to measure public acceptance of autonomous vehicles. This will enable us to investigate the diverse effects of algorithm transparency on both trust and acceptance of autonomous vehicles in various scenarios, capturing the disparity between public trust in autonomous vehicles and their actual adoption behaviors. (iii) We will investigate how the severity of accident scenarios moderates the influence of algorithm transparency on trust and acceptance of autonomous vehicles.