Recent years have witnessed an astonishing explosion in the evolution of
mobile applications powered by AI technologies. The rapid growth of AI
frameworks enables the transition of AI technologies to mobile devices,
significantly prompting the adoption of AI apps (i.e., apps that integrate AI
into their functions) among smartphone devices. In this paper, we conduct the
most extensive empirical study on 56,682 published AI apps from three
perspectives: dataset characteristics, development issues, and user feedback
and privacy. To this end, we build an automated AI app identification tool, AI
Discriminator, that detects eligible AI apps from 7,259,232 mobile apps. First,
we carry out a dataset analysis, where we explore the AndroZoo large repository
to identify AI apps and their core characteristics. Subsequently, we pinpoint
key issues in AI app development (e.g., model protection). Finally, we focus on
user reviews and user privacy protection. Our paper provides several notable
findings. Some essential ones involve revealing the issue of insufficient model
protection by presenting the lack of model encryption, and demonstrating the
risk of user privacy data being leaked. We published our large-scale AI app
datasets to inspire more future research.

本文通过数据集分析、关键问题定位和用户反馈与隐私保护等多方面对 56682 款 AI 应用进行了研究，揭示了其中的不足之处，如模型保护不足，用户隐私数据泄露等，还提供了数据集以供未来研究引用。