用AI分析用户评价，发现心理健康类APP的伦理盲区。
Exploring the Ethical Concerns in User Reviews of Mental Health Apps using Topic Modeling and Sentiment Analysis
- 通过主题建模与情感分析，挖掘应用评论中的伦理议题。
- 发现现有伦理框架未覆盖新兴风险，如数据隐私与算法偏见。
- 适合研究AI伦理、数字健康或产品安全的从业者参考。
随着AI驱动的心理健康类移动应用迅速发展，其伦理问题和用户信任成为关注焦点。本研究提出一种基于自然语言处理（NLP）的框架，从Google Play与Apple App Store的用户生成评论中评估伦理维度。数据经收集与清洗后，采用主题建模识别潜在伦理主题，并将其映射至已有伦理框架；同时利用基于Transformer的零样本分类模型，以自下而上的方式挖掘评论中浮现的新伦理议题。随后通过情感分析捕捉用户对各项伦理议题的态度。结果表明，传统伦理原则不足以涵盖现代AI技术带来的挑战，且部分关键道德价值被忽视。该工作为构建持续评估系统提供支持，有助于提升AI心理聊天机器人的公平性、透明度与可信度。
原文摘要 · Abstract (English)
The rapid growth of AI-driven mental health mobile apps has raised concerns about their ethical considerations and user trust. This study proposed a natural language processing (NLP)-based framework to evaluate ethical aspects from user-generated reviews from the Google Play Store and Apple App Store. After gathering and cleaning the data, topic modeling was applied to identify latent themes in the context of ethics using topic words and then map them to well-recognized existing ethical principles described in different ethical frameworks; in addition to that, a bottom-up approach is applied to find any new and emergent ethics from the reviews using a transformer-based zero-shot classification model. Sentiment analysis was then used to capture how users feel about each ethical aspect. The obtained results reveal that well-known ethical considerations are not enough for the modern AI-based technologies and are missing emerging ethical challenges, showing how these apps either uphold or overlook key moral values. This work contributes to developing an ongoing evaluation system that can enhance the fairness, transparency, and trustworthiness of AI-powered mental health chatbots.
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