Despite the importance of trust in human-AI interactions, researchers must adopt questionnaires from other disciplines that lack validation in the AI context. Motivated by the need for reliable and valid measures, we investigated the psychometric quality of two trust questionnaires, the Trust between People and Automation scale (TPA) by Jian et al. (2000) and the Trust Scale for the AI Context (TAI) by Hoffman et al. (2023). In a pre-registered online experiment (N = 1485), participants observed interactions with trustworthy and untrustworthy AI (autonomous vehicle and chatbot). Results support the psychometric quality of the TAI while revealing opportunities to improve the TPA, which we outline in our recommendations for using the two questionnaires. Furthermore, our findings provide additional empirical evidence of trust and distrust as two distinct constructs that may coexist independently. Building on our findings, we highlight the opportunities and added value of measuring both trust and distrust in human-AI research and advocate for further work on both constructs.

翻译：尽管信任在人机交互中至关重要，但研究者常需采用来自其他学科、缺乏人工智能情境效度验证的问卷。基于对可靠有效测量工具的需求，本研究考察了两份信任问卷的心理测量学质量：Jian等人（2000）开发的《人机自动化信任量表》（TPA）与Hoffman等人（2023）开发的《人工智能情境信任量表》（TAI）。通过预注册在线实验（N=1485），参与者观察了与可信赖及不可信赖人工智能（自动驾驶车辆与聊天机器人）的交互过程。结果支持TAI的心理测量学质量，同时揭示了改进TPA的潜在方向，并据此提出两份问卷的使用建议。此外，研究结果为信任与不信任作为可能独立共存的两种不同构念提供了额外实证证据。基于研究发现，我们强调了在人机交互研究中同时测量信任与不信任的机遇与增值价值，并倡导对这两个构念开展进一步研究。