Model(ing) Privacy: Empirical Approaches to Privacy Law & Governance

Lindsey Barrett
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引用次数: 3

Abstract

Privacy can be difficult for people to conceptualize, including for the policymakers charged with designing, interpreting, and enforcing privacy law. In both consumer privacy law and Fourth Amendment jurisprudence, the privacy protections afforded to individuals are shaped by the ability of governmental decision-makers to assess privacy preferences, expectations and behaviors, which they are rarely in a position to do accurately. But while policymakers can have a hard time understanding the subtle factors influencing privacy decision-making or parsing seemingly contradictory privacy incentives, it is an area where new empirical approaches have begun to excel. Researchers have used empirical techniques like machine learning, natural language processing, and crowdsourcing to explain the complexities of privacy decision-making, and to illustrate the nuances of privacy preferences, expectations, and behaviors that many opinion surveys often fail to grasp. Recent work has focused on eliciting privacy norms through the use of crowdsourcing; modeling individual privacy preferences and expectations using machine learning; extracting necessary information from privacy policies through the use of natural language processing; modeling AI assistants based on context and user preferences to predict (or nudge) future user decisions; and creative combinations thereof. Modeling privacy preferences, expectations and behavior can provide judges, regulators, and legislators with a more accurate and nuanced sense of privacy norms for future cases and policy discussions. Encouraging the implementation of proactive privacy tools, such as automated annotation of privacy policies and nudging assistants, can help bridge the gap separating user expectations, user behavior, and how both are understood under existing laws. While the use of this research in privacy law and policy cannot fundamentally transform the structural flaws that skew regulators’ perceptions of societal norms, it can at least correct the worst of those excesses, and facilitate policy that reflects how people actually think about privacy in the modern age.
隐私模型:隐私法与治理的实证方法
人们很难将隐私概念化,包括负责设计、解释和执行隐私法的政策制定者。在消费者隐私法和第四修正案的判例中,对个人隐私的保护是由政府决策者评估隐私偏好、期望和行为的能力决定的,而他们很少能够准确地做到这一点。但是,尽管政策制定者可能很难理解影响隐私决策的微妙因素,或分析看似矛盾的隐私激励措施,但这是一个新的实证方法开始脱颖而出的领域。研究人员使用机器学习、自然语言处理和众包等经验技术来解释隐私决策的复杂性,并说明许多民意调查经常无法掌握的隐私偏好、期望和行为的细微差别。最近的工作重点是通过使用众包来引出隐私规范;使用机器学习建模个人隐私偏好和期望;通过使用自然语言处理从隐私政策中提取必要的信息;基于上下文和用户偏好为AI助手建模,以预测(或推动)未来的用户决策;以及它们的创造性组合。对隐私偏好、期望和行为进行建模可以为法官、监管机构和立法者提供更准确、更细致的隐私规范感,以应对未来的案件和政策讨论。鼓励实施主动隐私工具,例如隐私政策的自动注释和助推助手,可以帮助弥合用户期望、用户行为以及在现有法律下如何理解两者之间的差距。虽然在隐私法律和政策中使用这项研究不能从根本上改变扭曲监管者对社会规范看法的结构性缺陷,但它至少可以纠正这些过度行为中最糟糕的部分,并促进反映现代人们对隐私的实际看法的政策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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