An Investigation of Human Annotators' AI Teammate Selection and Compliance Behaviours

Abstract

Human-artificial intelligence (AI) collaborative annotation has gained increasing prominence as a result of its enormous potential to complement human and AI strengths as well as AI's recent development. However, it is not straightforward to form suitable human-AI teams and design human-AI interaction mechanisms for effective collaborative annotation. Through an exploratory study, this thesis investigated a diverse set of factors that may influence humans' AI teammate selection and compliance behaviours in a collaborative annotation context wherein AI agents serve as suggesters to humans. The study results indicate that multiple factors influenced which AI agents the participants chose to receive suggestions from, such as the AI agents' recent and overall accuracies as well as the participants' suggestion compliance records. We also discovered that the participants' AI compliance decisions were swayed by factors including whether the AI agents' suggestions aligned with the participants' top choices and whether such suggestions provided novel perspectives to the participants. Moreover, it was found that most of the participants constructed narratives to interpret the differences in various AI teammates' behaviours based on limited evidence. This thesis also contributes by presenting MIA, a versatile web platform for mixed-initiative annotation. Based on the weaknesses of MIA's current designs, as informed by empirical results of the aforementioned exploratory study and another human-AI collaborative annotation study, as well as the goals to improve MIA's scalability and adaptability, this thesis proposes design changes to MIA; these design changes also apply to other mixed-initiative annotation platforms.