The growing use of Machine Learning and Artificial Intelligence (AI), particularly Large Language Models (LLMs) like OpenAI's GPT series, leads to disruptive changes across organizations. At the same time, there is a growing concern about how organizations handle personal data. Thus, privacy policies are essential for transparency in data processing practices, enabling users to assess privacy risks. However, these policies are often long and complex. This might lead to user confusion and consent fatigue, where users accept data practices against their interests, and abusive or unfair practices might go unnoticed. LLMss can be used to assess privacy policies for users automatically. In this interdisciplinary work, we explore the challenges of this approach in three pillars, namely technical feasibility, ethical implications, and legal compatibility of using LLMs to assess privacy policies. Our findings aim to identify potential for future research, and to foster a discussion on the use of LLM technologies for enabling users to fulfil their important role as decision-makers in a constantly developing AI-driven digital economy.

翻译：随着机器学习与人工智能（AI），尤其是像OpenAI的GPT系列这样的大型语言模型（LLM）的日益广泛应用，各组织正经历着颠覆性的变革。与此同时，人们对组织如何处理个人数据的担忧也在不断加剧。因此，隐私政策对于数据处理实践的透明度至关重要，它使用户能够评估隐私风险。然而，这些政策通常冗长且复杂。这可能导致用户困惑和“同意疲劳”，即用户可能接受违背自身利益的数据实践，而一些滥用或不公平的实践也可能被忽视。LLMs可用于自动为用户评估隐私政策。在这项跨学科工作中，我们从三个支柱探讨了这种方法的挑战，即使用LLMs评估隐私政策的技术可行性、伦理影响以及法律兼容性。我们的研究结果旨在明确未来研究的潜力，并促进关于利用LLM技术使用户能够在不断发展的AI驱动数字经济中履行其作为决策者的重要角色的讨论。