Dynamic Selection of Reliance Calibration Cues With AI Reliance Model

Abstract

Understanding what an AI system can and cannot do is necessary for end-users to use the AI properly without being over- or under-reliant on it. Reliance calibration cues (RCCs) communicate an AI’s capability to users, resulting in optimizing their reliance on it. Previous studies have typically focused on continuously presenting RCCs, and although providing an excessive amount of RCCs is sometimes problematic, limited consideration has been given to the question of how an AI can selectively provide RCCs. This paper proposes vPred-RC, an algorithm in which an AI decides whether to provide an RCC and which RCC to provide. It evaluates the influence of an RCC on user reliance with a cognitive model that predicts whether a human will assign a task to an AI agent with or without an RCC. We tested vPred-RC in a human-AI collaborative task called the collaborative CAPTCHA (CC) task. First, our reliance prediction model was trained on a dataset of human task assignments for the CC task and found to achieve 83.5% accuracy. We further evaluated vPred-RC’s dynamic RCC selection in a user study. As a result, the RCCs selected by vPred-RC enabled participants to more accurately assign tasks to an AI when and only when the AI succeeded compared with randomly selected ones, suggesting that vPred-RC can successfully calibrate human reliance with a reduced number of RCCs. The selective presentation of RCCs has the potential to enhance the efficiency of collaboration between humans and AIs with fewer communication costs.

Keywords

• Reliance calibration
• reliance prediction
• reliance calibration cue
• trust calibration
• explainable AI
• human-AI interaction