While voice agent product recommendations (VAPR) can be convenient for users, their underlying artificial intelligence (AI) components are subject to recommendation engine opacities and audio-based constraints, which limit users’ information level when conducting purchase decisions. As a result, users might feel as if they are being treated unfairly, which can lead to negative consequences for retailers. Drawing from the information processing and stimulus-organism-response theory, we investigate through two experimental between-subjects studies how process explanations and process visualizations—as additional information provision measures—affect users’ perceived fairness and behavioral responses to VAPRs. We find that process explanations have a positive effect on fairness perceptions, whereas process visualizations do not. Process explanations based on users’ profiles and their purchase behavior show the strongest effects in improving fairness perceptions. We contribute to the literature on fair and explainable AI by extending the rather algorithm-centered perspectives by considering audio-based VAPR constraints and directly linking them to users’ perceptions and responses. We inform practitioners how they can use information provision measures to avoid unjustified perceptions of unfairness and adverse behavioral responses.

虽然语音代理产品推荐 (VAPR) 可以为用户带来便利，但其底层人工智能 (AI) 组件受到推荐引擎不透明性和基于音频的限制，这限制了用户在做出购买决策时的信息水平。因此，用户可能会觉得自己受到了不公平的对待，这可能会给零售商带来负面后果。借鉴信息处理和刺激有机体反应理论，我们通过两个实验对象间研究来调查过程解释和过程可视化（作为额外的信息提供措施）如何影响用户对 VAPR 的感知公平性和行为反应。我们发现流程解释对公平感有积极影响，而流程可视化却没有。基于用户个人资料及其购买行为的流程解释在改善公平感方面表现出最强的效果。我们通过考虑基于音频的 VAPR 约束并将其直接与用户的感知和响应联系起来，扩展了以算法为中心的视角，为公平且可解释的人工智能文献做出了贡献。我们告知从业者如何使用信息提供措施来避免对不公平的不公正看法和不良行为反应。