Three Laws Blog: Law and Artificial Intelligence

Solove on A Regulatory Roadmap to AI and Privacy @DanielSolove @TeachPrivacy @gwlaw

Daniel J. Solove, George Washington University Law School, is publishing A Regulatory Roadmap to AI and Privacy as a GWU Law School Public Law Research Paper. Here is the abstract.

This short essay provides a regulatory roadmap to artificial intelligence and privacy. AI and privacy are deeply intertwined and have a complicated relationship with much overlap. AI can increase existing privacy problems, add dimensions and complexities to them, or remix them. Some attempts to regulate focus exclusively on AI, but doing this is like trying to remove weeds without digging up their roots. In stark ways, AI is showing the flaws in existing privacy laws. Instead of seeing AI as a unique situation, AI should be viewed as an opportunity to change course with privacy law generally. To address AI’s privacy problems, policymakers shouldn’t just pass new AI laws; they should also fix existing privacy laws. This essay briefly discusses the privacy problems raised by AI and how privacy law should be reformed to respond to these problems.

Download the essay from SSRN at the link.

Bartholomew on A Right to Be Left Dead @markbartholo @UBSchoolofLaw @CalifLRev

Mark Batholomew, SUNY Buffalo Law School, is publishing A Right to Be Left Dead in the California Law Review. Here is the abstract.

Technology forces us to contemplate a counterpart to the right of privacy—Brandeis and Warren’s “right to be left alone”—for the age of AI: the right to be left dead. Traditionally, it has been presumed that even if Brandeis and Warren’s right constitutes “the most comprehensive of rights and the right most valued by civilized men,” it does not apply to those no longer alive. The question is whether we need a new calculus at a time when anyone can be reanimated and made to behave in a manner indistinguishable from their living presence. This Article interrogates the need for a right to be left dead and takes some preliminary steps towards defining its contours, chief among them an awareness that an individual right to prevent unauthorized reanimations of the dead must look very different than the existing privacy, consumer protection, and property laws marshalled against unauthorized invocations of the living. A new right to be left dead can serve important social goals, but it must be narrowly construed, yielding to the critical interests of the living when necessary. To the extent legislatures and courts are beginning to grapple with the nascent problem of digital reanimation, they have failed to account for the most fundamental dividing line in existence—the line between life and death.

Download the article from SSRN at the link.

Warner and Sloan on How AI Unfairly Tilts the Playing Field: Privacy, Fairness and Risk Shadows @ChicagoKentLaw

Richard Warner, Chicago-Kent College of Law, and Robert H. Sloan, University of Illinois at Chicago, have published How AI Unfairly Tilts the Playing Field: Privacy, Fairness, and Risk Shadows. Here is the abstract.

Private sector applications of artificial intelligence (AI) raise related questions of informational privacy and fairness. Fairness requires that market competition occurs on a level playing field, and uses of AI unfairly tilt the field. Informational privacy concerns arise because AI tilts the playing field by taking information about activities in one area of one’s life and using it in ways that impose novel risks in areas not formerly associated with such risks. The loss of control over that information constitutes a loss of informational privacy. To illustrate both the fairness and privacy issues, imagine, for example, that Sally declares bankruptcy after defaulting on $50,000 of credit card debt. She incurred the debt by paying for lifesaving medical treatment for her eight-year-old daughter. Post-bankruptcy Sally is a good credit risk. Her daughter has recovered, and her sole-proprietor business is seeing increased sales. Given her bankruptcy, however, an AI credit scoring system predicts that she is a poor risk and assigns her a low score. That low credit score casts a shadow that falls on her when her auto insurance company, which uses credit scores in its AI system as a measure of the propensity to take risks, raises her premium. Is it fair that saving her daughter’s life should carry with it the risk—realized in this case—of a higher premium? The pattern is not confined to credit ratings and insurance premiums. AI routinely creates risk shadows. We address fairness questions in two steps. First, we turn to philosophical theories of fairness as equality of opportunity to spell out the content behind our metaphor of tilting the playing field. Second, we address the question of how, when confronted with a mathematically complex AI system, one can tell whether the system meets requirements of fairness. We answer by formulating three conditions whose violation makes a system presumptively unfair. The conditions provide a lens that reveals relevant features when policy makers and regulators investigate complex systems. Our goal is not to resolve fairness issues but to contribute to the creation of a forum in which legal regulators and affected parties can work to resolve them. The third of our three condition requires that systems incorporate contextual information about individual consumers, and we conclude by raising the question of whether our suggested approach to fairness significantly reduces informational privacy. We do not answer the question but emphasize that fairness and informational privacy questions can closely intertwine.

Download the article from SSRN at the link.

Hacker on Manipulation by Algorithms: Exploring the Triangle of Unfair Commercial Practice, Data Protection, and Privacy Law

Philipp Hacker, European University Viadrina Frankfurt (Oder), European New School of Digital Studies; Humboldt University of Berlin; is publishing Manipulation by Algorithms. Exploring the Triangle of Unfair Commercial Practice, Data Protection, and Privacy Law in the European Law Journal. Here is the abstract.

The optimization of sales practices in consumer markets through machine learning not only harbours the potential to better match consumer preferences with products, but also risks to facilitate the exploitation of consumer weaknesses discovered via data analysis. More specifically, recent technological advances have brought us to the edge of mind-reading technologies, which automatically analyse mental states and adapt offers accordingly, in potentially manipulative ways. This article shows that, in market contexts, the challenges of manipulation by algorithm necessitate an integrated understanding of unfair commercial practice, data protection, and privacy law. It maps the interactions between these contiguous yet distinct fields of law, and draws on economics and computer science to develop a novel framework to deal with algorithmic influence. In doing so, it also critically discusses the Commission proposals for the Digital Services Act and the Artificial Intelligence Act, and suggests to complement them with more broadly applicable measures to mitigate algorithmic manipulation.

Download the article from SSRN at the link.

Karen Hao on How We Lost Control of Our Faces

From MIT Technology Review:

Karen Hao, This Is How We Lost Control of Our Faces: The largest ever study of facial-recognition data shows how much the rise of deep learning has fueled a loss of privacy. 

 

Mitrou on Whether the General Data Protection Regulation Is AI-Proof @uaegean @AUEB

Lilian Mitrou, University of the Aegean; Athens University of Economics and Business, Department of Informatics, has published Data Protection, Artificial Intelligence and Cognitive Services: Is the General Data Protection Regulation (GDPR) ‘Artificial Intelligence-Proof’?. Here is the abstract.

AI - in its interplay with Big Data, ambient intelligence, ubiquitous computing and cloud computing - augments the existing major, qualitative and quantitative, shift with regard to the processing of personal information. The questions that arise are of crucial importance both for the development of AI and the efficiency of data protection arsenal: Is the current legal framework AI-proof ? Are the data protection and privacy rules and principles adequate to deal with the challenges of AI or do we need to elaborate new principles to work alongside the advances of AI technology? Our research focuses on the assessment of GDPR that, however, does not specifically address AI, as the regulatory choice consisted more in what we perceive as “technology – independent legislation. The paper will give a critical overview and assessment of the provisions of GDPR that are relevant for the AI-environment, i.e. the scope of application, the legal grounds with emphasis on consent, the reach and applicability of data protection principles and the new (accountability) tools to enhance and ensure compliance.

Download the article from SSRN at the link.

O'Callaghan on Inferential Privacy and AI

Joe O'Callaghan has published Inferential Privacy and Artificial Intelligence - A New Frontier? at 11 Journal of Law & Economic Regulation 72 (2018). Here is the abstract.

The growing use and sophistication of artificial intelligence permits the generation of increasing volumes of inferential data about individuals, including data that may be privacy invasive. The generation of such data poses challenges to our existing regulatory models, including under EU's General Data Protection Regulation. This article focuses on privacy and data protection law and associated guidance applicable in England and Wales. It considers how inferential data is generated and describes some relevant regulatory challenges this poses including in relation to the principles of fair and lawful processing, transparency, privacy by design and the purpose limitation. It considers relevant causes of action under the law of England and Wales where intrusive insights are generated and proposes that the field of "inferential privacy" is one that is ripe for further development from an academic and legal perspective.

Download the article from SSRN at the link.

Yanisky-Ravid and Hallisey on "Equality and Privacy By Design": Ensuring Artificial Intelligence (AI) Is Properly Trained and Fed @YaleLawSch @FordhamLawNYC

Shlomit Yanisky-Ravid, Yale Law School, and Sean Hallisey, Fordham University School of Law, have published ‘Equality and Privacy by Design’: Ensuring Artificial Intelligence (AI) Is Properly Trained & Fed: A New Model of AI Data Transparency & Certification As Safe Harbor Procedures. Here is the abstract.

Artificial Intelligence systems (“AI”) are often described as a technological breakthrough that will completely transform our society and economy. AI systems have been implemented in all facets of the economy, from medicine to transportation, finance, art, legal, social, and weapons; making decisions previously determined by humans. While this article recognizes that AI systems promise benefits, it also identifies urgent challenges to our everyday life. Just as the technology has become prolific, so has the literature concerning its legal implications. However, the literature suffers from a lack of solutions that address the legal and engineering perspectives. This leaves technology firms without guidelines and increases the risk of societal harm. Policymakers, including judges, operate without a regulatory regime to turn to when addressing these novel and unpredictable outcomes. This article tries to fill the void by focusing on the use of data by these systems, rather than on the software and software programmers. It suggests a new Model that stems from a recognition of the significant role that the data plays in the development and functioning of AI systems. One of the most important phases of teaching AI systems to operate starts with a preexisting massive dataset that the data providers use to train the system. The data providers are programmers, trainers; the stakeholders who enable access to data or the systems’ users. In this article, we analyze and discuss the threats the use of data by AI systems pose in terms of producing discriminatory outcomes as well as violations of privacy. The data can be illegal, discriminatory, manufacture, unreliable, or simply incomplete. The more data that AI systems “swallow,” the likelihood increases that AI systems could produce biased, discriminatory decisions and/or violate privacy. The article discusses how discrimination can arise, even inadvertently, from the operation of “trusted” and "objective" AI systems. The article addresses, on the one hand, the hurdles and challenges behind the use of big data by AI systems, and on the other, suggests a possible, new solution. We propose a new AI data transparency Model that focuses on disclosure of the data being used by AI systems, when necessary. To perfect the Model we recommend an auditing regime and a certification program, either by governmental body or, in the absence of such entity, by private institutes. This Model will encourage the industry to take steps, proactively, to ensure that the dataset is trustworthy and then, to publicly exhibit the quality of the data (that their AI systems rely on). By receiving and publicizing a quality “stamp” the firms will fairly build their competitive reputation and will strengthen the public control of the systems. We envision that the implementation of this Model will help firms and individuals become educated about the potential issues concerning AI, discrimination and the continued weakening of societal expectations of privacy. In this sense, the AI data transparency Model operates as a safe harbor mechanism that incentivizes the industry, from the first steps of developing and training AI systems, to the actual operation of the AI systems, to implement effective standards, that we coin Equality and Privacy by Design. The suggested AI Transparency Model functions as a safe harbor, even without massive regulatory steps. From an engineering point of view, not only does the model recognize the data providers and the big data as the most important components in the process of creating, training and operating AI systems, but the AI Data Transparency Model is also technologically feasible as data can be easily absorbed and kept by a technological tool. This Model is feasible from a practical perspective, as it follows already existing legal frameworks of data transparency, such as the ones being implemented by the FDA and SEC. We argue that improving transparency in data systems should result in less harmful AI systems, better protect societal rights and norms, and produce improved outcomes in this emerging field, specifically for minority communities, who often lack resources or representation to combat the use of AI systems. We assert that improvements in transparency regarding the data used while developing, training or operating AI systems could mitigate and reduce these harms. We recommend critical evaluations and audits of data used to train AI systems to identify such risks, and propose a certification system whereby AI systems can publicize good faith efforts to reduce the possibility of discriminatory outcomes and privacy violations. We do not purport to solve the riddle of every possible negative outcome created by AI systems; instead, we are trying to incentivize the creation of new standards that the industry could implement, from day one of developing AI systems that addresses the possibility of harm, rather than post-hoc assignments of liability.

Download the article from SSRN at the link.

Is Alexa Listening? Another Murder Case Involves Echo Recordings

A New Hampshire trial court judge has told Amazon that it must turn over recordings that an Echo installed in the home of two murder victims made that might constitute auditory evidence of the homicides. The victims, Christine Sullivan and Jenna Pellegrini, died in January 2017. Timothy Verrill is on trial for their deaths. More about the case here.

A similar case popped up in Arkansas last year. Prosecutors sought recordings they thought might exist in a November 2015 murder case. Amazon refused to comply with the request, but the defendant agreed to provide the information. Eventually, the lead prosecutor dropped the case saying that the available evidence supported "more than one 'reasonable explanation' " for the victim's death.  More here from CNN.

Lamo and Calo on Regulating Bot Speech @madeline_lamo @rcalo

Madeline Lamo and Ryan Calo, both of the University of Washington School of Law, have published Regulating Bot Speech. Here is the abstract.

We live in a world of artificial speakers with real impact. Chat bots befriend children in order to acquire marketing data. Robotic telemarketers laugh at the suggestion that they are not real. Russian social media bots foment sufficient political strife to merit a spotlight in Congressional hearings. Concerns over bot speech have led prominent figures in technology to call for regulation. Legislators have begun to heed these calls, drafting laws that would require online bots to clearly indicate that they are not human. This essay is the first to consider how efforts to regulate bot speech might fare under the First Amendment. At first blush, requiring a bot to self-disclose raises little in the way of free speech concerns — it does not censor speech per se, nor does it unmask the identity of the person behind the automated account. A deeper analysis, however, reveals several areas of First Amendment tension that any bot disclosure law would need to address. These include a poor fit between the disclosure requirement and the harms such a law would aim to address, the potential for unmasking anonymous speakers in the enforcement process, and the creation of a scaffolding for censorship by private actors and other governments. We offer recommendations for legislators who seek respond to the real risks autonomous speakers pose while avoiding these pitfalls.

Download the article from SSRN at the link.