Three Laws Blog: Law and Artificial Intelligence

Kerr on Schroedinger's Robot: Privacy in Uncertain States @ianrkerr

Ian R. Kerr, University of Ottawa Faculty of Law, Common Law Section, is publishing Schrödinger's Robot: Privacy in Uncertain States in volume 20 of Theoretical Inquiries L. (2019). Here is the abstract.

Can robots or AIs operating independent of human intervention or oversight diminish our privacy? There are two equal and opposite reactions to this issue. On the robot side, machines are starting to outperform human experts in an increasing array of narrow tasks, including driving, surgery, and medical diagnostics. This is fueling a growing optimism that robots and AIs will exceed humans more generally and spectacularly; some think, to the point where we will have to consider their moral and legal status. On the privacy side, one sees the very opposite: robots and AIs are, in a legal sense, nothing. Judge Posner, for example, has famously opined that they do not invade privacy because they are not sentient beings. Indeed, the received view is that since robots and AIs are neither sentient nor capable of human-level cognition, they are of no consequence to privacy law. This article argues that robots and AI operating independently of human intervention can and, in some cases, already do diminish our privacy. Rejecting the all-or-nothing account of robots and privacy described above, I seek to identify the conditions that give rise to diminished privacy in order to see whether robots and AI can meet those conditions. To do so, I borrow from epistemic privacy — a theory that understands a subject’s state of privacy as a function of another’s state of cognizance regarding the subject’s personal facts. Epistemic privacy offers a useful analytic framework for understanding the kind of cognizance that gives rise to diminished privacy. I demonstrate that current robots and AIs are capable of developing truth-promoting beliefs and observational knowledge about people without any human intervention, oversight, knowledge, or awareness. Because machines can actuate on the basis of the beliefs they form in ways that affect people’s life chances and opportunities, I argue that they demonstrate the kind of cognizance that definitively implicates privacy. Consequently, I conclude that legal theory and doctrine will have to expand their understanding of privacy relationships to include robots and AIs that meet these epistemic conditions. An increasing number of machines possess epistemic qualities that force us to rethink our understanding of privacy relationships with robots and AIs.

Download the article from SSRN at the link.

Newly Published: Data Protection and Privacy: The Age of Intelligent Machines @hartpublishing

For your bookshelf: Data Protection and Privacy: The Age of Intelligent Machines (Ronald Leenes, Rosamunde van Brakel, Serge Gutwirth, and Paul De Hert, eds., Hart Publishing, 2017).

The subjects of Privacy and Data Protection are more relevant than ever with the European General Data Protection Regulation (GDPR) becoming enforceable in May 2018. This volume brings together papers that offer conceptual analyses, highlight issues, propose solutions, and discuss practices regarding privacy and data protection. It is one of the results of the tenth annual International Conference on Computers, Privacy and Data Protection, CPDP 2017, held in Brussels in January 2017. The book explores Directive 95/46/EU and the GDPR moving from a market framing to a 'treaty-base games frame', the GDPR requirements regarding machine learning, the need for transparency in automated decision-making systems to warrant against wrong decisions and protect privacy, the riskrevolution in EU data protection law, data security challenges of Industry 4.0, (new) types of data introduced in the GDPR, privacy design implications of conversational agents, and reasonable expectations of data protection in Intelligent Orthoses. This interdisciplinary book was written while the implications of the General Data Protection Regulation 2016/679 were beginning to become clear. It discusses open issues, and daring and prospective approaches. It will serve as an insightful resource for readers with an interest in computers, privacy and data protection.

Endo on Technological Opacity & Procedural Injustice @nyuniversity

Seth Katsuya Endo, New York University, is publishing Technological Opacity & Procedural Injustice in the B.C.L. Rev. (2018). Here is the abstract.

From Google’s auto-correction of spelling errors to Netflix’s movie suggestions, machine-learning systems are a part of our everyday life. Both private and state actors increasingly employ such systems to make decisions that implicate individuals’ substantive rights, such as with credit scoring, government-benefit eligibility decisions, national security screening, and criminal sentencing. In turn, the rising use of machine-learning systems has led to questioning about whether they are sufficiently accurate, fair, and transparent. This Article builds on that work, focusing on how opaque technologies can subtly erode the due-process norm of participation. To illuminate this issue, this Article examines the use of predictive coding — a form of technology-assisted review in which supervised machine-learning software is taught to predict the relevance of collected documents for discovery productions. The use of predictive coding in civil discovery highlights the new challenge to the participation norm because the processes generally do not provide any explanations for the outputs, much less non-technological accounts that are tied to the underlying substantive legal issues. Thus, even if predictive coding results in reasonably complete, accurate, and cost-efficient productions, the “black-box” nature of the process may harm the element of legitimacy that comes from litigants understanding and being able to more fully participate in judicial processes. This harm, however, has not been addressed by the developing jurisprudence, probably because most of the early cases involved high-stakes litigation between sophisticated parties who could afford computer experts. But the participation issue — and related equality concerns — will become increasing problematic as the technology’s use expands beyond this privileged posture. In response to these issues, this Article proposes a reinvigorated Mathews framework that explicitly weighs predictive coding’s impact on the participation norm to better future proof the doctrine.

Download the article from SSRN at the link.

Hacker on Personal Data, Exploitative Contracts, and Algothrithmic Fairness: Autonomous Vehicles Meet the Internet of Things @EuropeanUni

Philipp Hacker, European University Institute; Humboldt University of Berlin, is publishing Personal Data, Exploitative Contracts, and Algorithmic Fairness: Autonomous Vehicles Meet the Internet of Things in International Data Privacy Law. Here is the abstract.

Personal data harvested in the Internet of Things not only promises to be particularly valuable, but also particularly privacy-sensitive. Analysed with the power of specialized Artificial Intelligence, such data allows for potentially beneficial personalization of goods and services; however, it also facilitates data-driven exploitative contracting. For example, autonomous and connected vehicles (ACVs), epitomizing the merger of Artificial Intelligence with the Internet of Things, are collecting ever increasing amounts of personal data. From an economic perspective, the processing of this data by ACV operators is ambivalent, enabling innovative driving and safety features in a personalized version of autonomous driving, but also exploitative contracts tailored to profit from the vulnerabilities of data subjects. A key regulatory conundrum at the intersection of Artificial Intelligence with the Internet of Things therefore consists in facilitating socially beneficial innovation while reining in exploitative contracts. Current EU data protection, contract and tort law, however, arguably fail to rein in data-driven exploitative contracts. Therefore, three novel interventions aiming to infuse greater fairness into the code of the digital economy are discussed: (a) mandatory “data safe” alternatives; (b) personalized data protection; and (c) procedural rules on algorithmic fairness.

Download the article from SSRN at the link.