Trustworthiness of Systematic Review Automation An interview at Coventry University

To reduce both time and labour for systematic review, much effort has been made on developing automated tools using artificial intelligence and machine learning in nearly two decades. Unfortunately, systematic review automation tools still face a serious problem of social acceptance. Previous studies identified lack of trust as one important adoption barrier for systematic review automation. However, further discussion about building trust was limited to building trusted evidence base by benchmarked large-scale evaluation. This study extended the previous discussions of the trustworthiness of systematic review automation. Through semi-structure interviews with regular systematic reviewers, we tried to not only get answers for to what extent human reviewers trust automated tools and why, but also reveal more measures of building trust from human reviewers’ points of view and the impact of such measures on the trust in and adoption of systematic review automation tools. We believe that the results of this study may also shed light on some new directions of systematic review automation research.