Privacy and Artificial Intelligence

计算机科学 对抗制 领域(数学) 设计隐私 信息隐私 智能决策支持系统 计算机安全 人工智能 数据科学 风险分析(工程) 业务 数学 纯数学
作者
James Curzon,Tracy Ann Kosa,Rajen Akalu,Khalil El‐Khatib
出处
期刊:IEEE transactions on artificial intelligence [Institute of Electrical and Electronics Engineers]
卷期号:2 (2): 96-108 被引量:63
标识
DOI:10.1109/tai.2021.3088084
摘要

Artificial intelligence is a rapidly developing field of research with many practical applications. Congruent to advances in technologies that enable big data, deep learning, and neural networks to train, learn, and predict, artificial intelligence creates new risks that are difficult to predict and manage. Such risks include economic turmoil, existential crises, and the dissolution of individual privacy. If unchecked, the capabilities of artificially intelligent systems could pose a fundamental threat to privacy in their operation or these systems may leak information under adversarial conditions. In this article, we survey the literature and provide various scenarios for the use of artificial intelligence, highlighting potential risks to privacy and offering various mitigating strategies. For the purpose of this research, a North American perspective of privacy is adopted. Impact statement-While an appreciation of the privacy risks associated with artificial intelligence is important, a thorough understanding of the assortment of different technologies that comprise artificial intelligence better prepares those implementing such systems in assessing privacy impacts. This can be achieved through the independent consideration of each constituent of an artificially intelligent system and its interactions. Under individual consideration, privacy-enhancing tools can be applied in a targeted manner to reduce the risk associated with specific components of an artificially intelligent system. A generalized North American approach to assess privacy risks in such systems is proposed that will retain applicability as the field of research evolves and can be adapted to account for various sociopolitical influences. With such an approach, privacy risks in artificial intelligent systems can be well understood, measured, and reduced.
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