Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?
Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?
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DOI：
10.18653/v1/2021.acl-long.319
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Abhilasha Ravichander;A. Black;Thomas B. Norton;Shomir Wilson;N. Sadeh
中科院分区：
文献类型：
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作者：
Abhilasha Ravichander;A. Black;Thomas B. Norton;Shomir Wilson;N. Sadeh
隐私在维护民主理想和个人自主权方面发挥着至关重要的作用。在许多司法管辖区，隐私的主要法律方法是“通知和选择”范式，其中隐私政策是用于向用户传达信息的主要工具。然而，隐私政策是冗长而复杂的文档，用户很难阅读和理解。我们讨论了语言技术如何在解决这一信息鸿沟方面发挥重要作用，并报告了在帮助三种特定类别的利益相关者利用数字隐私政策方面的初步进展：消费者、企业和监管机构。我们的目标是为语言技术的开发和使用提供一个路线图，以使用户能够重新控制他们的隐私，限制隐私伤害，并从社区中聚集研究努力来解决具有重大社会影响的问题。我们强调了开发语言技术的许多剩余机会，这些技术在使用隐私政策文本的方式上更加精确或细致入微。
Privacy plays a crucial role in preserving democratic ideals and personal autonomy. The dominant legal approach to privacy in many jurisdictions is the “Notice and Choice” paradigm, where privacy policies are the primary instrument used to convey information to users. However, privacy policies are long and complex documents that are difficult for users to read and comprehend. We discuss how language technologies can play an important role in addressing this information gap, reporting on initial progress towards helping three specific categories of stakeholders take advantage of digital privacy policies: consumers, enterprises, and regulators. Our goal is to provide a roadmap for the development and use of language technologies to empower users to reclaim control over their privacy, limit privacy harms, and rally research efforts from the community towards addressing an issue with large social impact. We highlight many remaining opportunities to develop language technologies that are more precise or nuanced in the way in which they use the text of privacy policies.