Three Laws Blog: Law and Artificial Intelligence

Lee on Synthetic Data and the Future of AI @UCDavisLaw

Peter Lee, University of California, Davis, School of Law, is publishing Synthetic Data and the Future of AI in volume 110 of the Cornell Law Review. Here is the abstract.

The future of artificial intelligence (AI) is synthetic. Several of the most prominent technical and legal challenges of AI derive from the need to amass huge amounts of real-world data to train machine learning (ML) models. Collecting such real-world data can be highly difficult and can threaten privacy, introduce bias in automated decision making, and infringe copyrights on a massive scale. This Article explores the emergence of a seemingly paradoxical technical creation that can mitigate—though not completely eliminate—these concerns: synthetic data. Increasingly, data scientists are using simulated driving environments, fabricated medical records, fake images, and other forms of synthetic data to train ML models. Artificial data, in other words, is being used to train artificial intelligence. Synthetic data offers a host of technical and legal benefits; it promises to radically decrease the cost of obtaining data, sidestep privacy issues, reduce automated discrimination, and avoid copyright infringement. Alongside such promise, however, synthetic data offers perils as well. Deficiencies in the development and deployment of synthetic data can exacerbate the dangers of AI and cause significant social harm. In light of the enormous value and importance of synthetic data, this Article sketches the contours of an innovation ecosystem to promote its robust and responsible development. It identifies three objectives that should guide legal and policy measures shaping the creation of synthetic data: provisioning, disclosure, and democratization. Ideally, such an ecosystem should incentivize the generation of high-quality synthetic data, encourage disclosure of both synthetic data and processes for generating it, and promote multiple sources of innovation. This Article then examines a suite of “innovation mechanisms” that can advance these objectives, ranging from open source production to proprietary approaches based on patents, trade secrets, and copyrights. Throughout, it suggests policy and doctrinal reforms to enhance innovation, transparency, and democratic access to synthetic data. Just as AI will have enormous legal implications, law and policy can play a central role in shaping the future of AI.

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.