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.

The consent model of privacy protection assumes that individuals control their personal information and are able to assess the risks associated with data sharing. The model is attractive for policy-makers and automakers because it has the effect of glossing over the conceptual ambiguities that are latent in definitions of privacy. Instead of formulating a substantive and normative position on what constitutes a reasonable expectation of privacy in the circumstance, individuals are said to have control over their data. Organizations have obligations to respect rights to notice, access and consent regarding the collection, use and disclosure of personal data once that data has been shared. The policy goal becomes how to provide individuals with control over their personal data in the consent model of privacy protection. This paper argues that the privacy issues raised by vehicular ad hoc networks make this approach increasingly untenable. It is argued that substantive rules that establish a basic set of privacy norms regarding the collection, use and disclosure of data are necessary. This can be realized in part via a privacy code of practice for the connected vehicle. This paper first explores the relationship between privacy, consent and personal information in relation to the connected car. This is followed by a description of vehicular ad hoc networks and a survey of the technical proposals aimed at securing data. The privacy issues that will likely remain unsolved by enhancing individual consent are then discussed. The last section provides some direction on how a code of practice can assist in determining when individual consent will need to be enhanced and when alternatives to consent will need to be implemented.

Technology is rapidly advancing and along with these advancements come major privacy concerns for drivers. This is especially true for modern day connected cars equipped with infotainment and telematics systems that can collect substantial amounts of sensitive information. This paper provides an overview of how personal information flows through typical infotainment and telematics systems, identifies potential privacy implications for drivers and provides recommendations for reform moving forward.

A major component of modern vehicles is the infotainment system, which interfaces with its drivers and passengers. Other mobile devices, such as handheld phones and laptops, can relay information to the embedded infotainment system through Bluetooth and vehicle WiFi. The ability to extract information from these systems would help forensic analysts determine the general contents that is stored in an infotainment system. Based off the data that is extracted, this would help determine what stored information is relevant to law enforcement agencies and what information is non-essential when it comes to solving criminal activities relating to the vehicle itself. This would overall solidify the Intelligent Transport System and Vehicular Ad Hoc Network infrastructure in combating crime through the use of vehicle forensics. Additionally, determining the content of these systems will allow forensic analysts to know if they can determine anything about the end-user directly and/or indirectly.