Informational Friction as a Lens for Studying Algorithmic Aspects of Privacy
Informational Friction as a Lens for Studying Algorithmic Aspects of Privacy
复制标题
DOI：
10.1145/3415172
复制
发表时间：
2020-10
影响因子：
--
通讯作者：
P. Skeba;E. Baumer
中科院分区：
文献类型：
--
作者：
P. Skeba;E. Baumer
本文讨论了算法系统所带来的隐私概念化的挑战，这些算法系统可以从看似无害的数据中推断出敏感信息。由于机器学习和人工智能系统在几乎每个行业的快速采用，这种类型的隐私迫在眉睫。在本文中，我们建议信息摩擦，从弗洛里迪的信息伦理的概念，作为一个有价值的概念透镜研究算法方面的隐私。信息摩擦描述了一个代理访问或更改另一个代理的信息所需的工作量。通过关注工作量，而不是信息的类型或收集信息的方式，信息摩擦可以帮助解释为什么自动化分析应该独立于与数据收集相关的隐私问题。作为一个示范，本文分析了执法使用面部识别，和Facebook的有针对性的广告模式，使用信息摩擦，并展示了这些系统所固有的风险，这是不完全确定的另一个流行的框架，尼森鲍姆的上下文Integrity.The论文的结论与更广泛的影响，无论是对隐私研究和隐私监管的讨论。
This paper addresses challenges in conceptualizing privacy posed by algorithmic systems that can infer sensitive information from seemingly innocuous data. This type of privacy is of imminent concern due to the rapid adoption of machine learning and artificial intelligence systems in virtually every industry. In this paper, we suggest informational friction, a concept from Floridi's ethics of information, as a valuable conceptual lens for studying algorithmic aspects of privacy. Informational friction describes the amount of work required for one agent to access or alter the information of another. By focusing on amount of work, rather than the type of information or manner in which it is collected, informational friction can help to explain why automated analyses should raise privacy concerns independently of, and in addition to, those associated with data collection. As a demonstration, this paper analyze law enforcement use of facial recognition, andFacebook's targeted advertising model using informational friction and demonstrate risks inherent to these systems which are not completely identified in another popular framework, Nissenbaum's Contextual Integrity.The paper concludes with a discussion of broader implications, both for privacy research and for privacy regulation.