Three Laws Blog: Law and Artificial Intelligence

Brennan on Proposals for the Rights and Liabilities of Artificial Intelligence through Electronic Personhood: Conscious Machines, Hermit Crabs, and the Distant Past

Dan Brennan has published Proposals for the Rights and Liabilities of Artificial Intelligence through Electronic Personhood: Conscious Machines, Hermit Crabs, and the Distant Past. Here is the abstract.

Examines the complexities of contemporary AI regulation, chiefly in the technology's unique capacity for autonomous decision making and independent learning. The paper highlights the potential of granting legal personhood to sophisticated AI systems as an alternative to current regulatory schemes. Three categories of personhood are considered: the ontological model, corporate frameworks, and the ancient Roman concept of peculium. Fundamentally, each model allows AI to hold property and be responsible for its actions, notably by indemnifying any legal persons it may harm. That said, each model has unique benefits and limitations which are discussed in turn. Chapter 6 concludes that neither one of these models can be exclusively relied upon. Instead, a new form of 'electronic personhood' should adopt elements from all the regulatory methods discussed herein, including methods of current regulation and the EU's AI Act.

Download the article from SSRN at the link.

Deeks on Delegating War Initiation to Machines @UVALaw @AustJIA

Ashley Deeks, University of Virginia School of Law, has published Delegating War Initiation to Machines at 78 Australian Journal of International Affairs 148 (2024). Here is the abstract.

The use of autonomy to initiate force, which states may begin to view as necessary to protect against hypersonic attacks and other forms of "hyperwar," may effectively constitute a delegation of war-initiation decision making to a machine. Yet legal questions about whether and when the leader of a country may delegate their decision making to others - and normative questions about whether he should do so - can be complicated. Any state that intends to introduce significant autonomy into such systems should assess whether and how the use of autonomy in war initiation comports with its domestic laws and norms that govern the delegation of the use of force.

Download the article from SSRN at the link.

Haneman on The Law of Digital Resurrection @TaxLawProf @BCLawReview @CreightonLaw

Victoria J. Haneman, Creighton University School of Law, is publishing The Law of Digital Resurrection in the B. C. L. Rev. (2025). Here is the abstract.

The digital right to be dead has yet to be recognized as an important legal right. Artificial intelligence, augmented reality, and nanotechnology have progressed to the point that personal data can be used to resurrect the deceased in digital form with appearance, voice, emotion, and memory recreated to allow interaction with a digital app, chat bot, or avatar that may be indistinguishable from that with a living person. Users may now have a completely immersive experience simply by loading the personal data of the deceased into a neural network to create a chatbot that inherits features and idiosyncrasies of the deceased and dynamically learns with increased communication. There is no legal or regulatory landscape against which to estate plan to protect those who would avoid digital resurrection, and few privacy rights for the deceased. This is an intersection of death, technology, and privacy law that has remained relatively ignored until recently. This Article is the first to respect death as an important and distinguishing part of the conversation about regulating digital resurrection. Death has long had a strained relationship with the law, giving rise to dramatically different needs and idiosyncratic legal rules. The law of the dead reflects the careful balance between the power of the state and an individual's wishes, and it may be the only doctrinal space in which we legally protect remembrance. This Article frames the importance of almost half of a millennium of policy undergirding the law of the deceased, and proposes a paradigm focused upon a right of deletion for the deceased over source material (data), rather than testamentary control over the outcome (digital resurrection), with the suggestion that existing protections are likely sufficient to protect against unauthorized commercial resurrections.

Download the article from SSRN at the link.

Shaheen on Section 230's Immunity for Generative Artifiicial Intelligence

Louis Shaheen, Ohio State University, has published Section 230's Immunity for Generative Artificial Intelligence. Here is the abstract.

Section 230 of the Communications Decency Act of 1996 immunizes online platforms from liability resulting from the content that third parties post on their platforms. Congress passed Section 230 in response to two civil cases from New York that split on whether online platforms were immune from liability that third parties posted on their sites. Congress was concerned that, without immunity from third-party content posted on their websites, online platforms would not survive. Moreover, Congress believed that immunity was necessary for the internet's development. Congress was right. Commentators have hailed Section 230 as the backbone of the internet. But as time has passed, Section 230 has been repeatedly criticized for the broad immunity it has given platforms––immunity for conduct outside the bounds of what its drafters anticipated. With the emergence of generative artificial intelligence, Section 230 is back in the news. Congress, President Joe Biden, law firms, and law professors have begun discussing in executive orders, hearings, and law reviews whether generative artificial intelligence has Section 230 immunity. They are all concerned about the impact that artificial intelligence will have on our society. Circuit courts have not had the chance to explicitly make clear whether Section 230 immunizes generative artificial intelligence.

Download the article from SSRN at the link.

Grieman on Law, Death, and Robots: The Regulation of Artificial Intelligence in High Risk Civil Applications @hartpublishing

Forthcoming from Hart Publishing:

Keri Grieman, Law, Death, and Robots: The Regulation of Artificial Intelligence in High Risk Civil Applications (October 2024). Here from the publisher's website is a description of the book's contents.

 

Can the law keep up with AI? This book examines liability and regulation for artificial intelligence causing serious physical harm, both now and in the future. While AI moves quickly, regulation follows more slowly – an increasing problem for an evolutionary, fast-paced emerging technology. AI has the potential to save lives, but in doing so will have the potential to take them as well. How do we future-proof law and regulation to incentivise life-saving innovation as safely as possible? This book details how to regulate AI in high-risk civil applications (for example, automated vehicles and medicine), addressing both liability and regulatory structure. It highlights crucial liability themes for technology governance; provides tools to bridge the gap between regulators and technologists; examines jurisdictional approaches to AI regulation in the EU, UK, USA, and Singapore; and ultimately suggests a jurisdiction-agnostic blueprint for regulation.

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.

Bambauer and Woods on AI, Artists, and Anti-Moral Rights @dbambauer @uarizonalaw

Derek E. Bambauer, University of Florida College of Law, and Robert Woods, University of Arizona College of Law, are publishing AI, Artists, and Anti-Moral Rights in volume 113 of the Georgetown Law Journal Online (2024). Here is the abstract.

Generative artificial intelligence (AI) tools are increasingly used to imitate the distinctive characteristics of famous artists, such as their voice, likeness, and style. In response, legislators have introduced bills in Congress that would confer moral rights protections, such as control over attribution and integrity, upon artists. This Essay argues such measures are almost certain to fail because of deep-seated, pervasive hostility to moral rights measures in U.S. intellectual property law. It analyses both legislative measures and judicial decisions that roll back moral rights, and explores how copyright’s authorship doctrines manifest a latent hostility to these entitlements. The Essay concludes with two suggestions for reformers: to frame arguments in pecuniary terms rather than personhood ones, and to turn to trademark law instead of copyright.

Download the essay from SSRN at the link.

Wachter, Middelstadt, and Russell on Whether Large Language Models Have a Legal Duty to Tell the Truth @SandraWachter5 @oiioxford

Sandra Wachter, Brent Middelstadt, and Chris Russell, all of the Oxford Internet Institute, have published Do large language models have a legal duty to tell the truth? Here is the abstract.

Careless speech is a new type of harm created by large language models (LLM) that poses cumulative, long-term risks to science, education, and shared social truth in democratic societies. LLMs produce responses that are plausible, helpful, and confident, but that contain factual inaccuracies, misleading references, and biased information. These subtle mistruths are poised to cumulatively degrade and homogenise knowledge over time. This article examines the existence and feasibility of a legal duty for LLM providers to create models that “tell the truth.” We argue that LLM providers should be required to mitigate careless speech and better align their models with truth through open, democratic processes. We define careless speech against “ground truth” in LLMs and related risks including hallucinations, misinformation, and disinformation. We assess the existence of truth-related obligations in EU human rights law and the Artificial Intelligence Act, Digital Services Act, Product Liability Directive, and Artificial Intelligence Liability Directive. Current frameworks contain limited, sector-specific truth duties. Drawing on duties in science and academia, education, archives and libraries, and a German case in which Google was held liable for defamation caused by autocomplete, we propose a pathway to create a legal truth duty for providers of narrow- and general-purpose LLMs.

Download the article from SSRN at the link.

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.