Three Laws Blog: Law and Artificial Intelligence

AI and IP Law--Position Statement of the Max Planck Institute for Innovation and Competition of 9 April 2021 on the Current Debate @mpi_inno_comp

Artificial Intelligence and Intellectual Property Law--Position Statement of the Max Planck Institute for Innovation and Competition of 9 April 2021 on the Current Debate (Max Planck Institute for Innovation & Competition Research Paper No. 21-10). Here is the abstract.

This Position Statement presents a broad overview of issues arising at the intersection of AI and IP law based on the work of the Max Planck Institute for Innovation and Competition research group on Regulation of the Digital Economy. While the analysis is approached mainly from a perspective de lege lata, it also identifies questions which require further reflection de lege ferenda supported by in-depth interdisciplinary research. The scope is confined to substantive European IP law, in particular, as regards copyright, patents, designs, databases and trade secrets. Specific AI-related issues are mapped out around the core questions of IP law, namely, the eligibility for protection under the respective IP regimes, allocation of rights and the scope of protection. The structure of the analysis reflects three key components of AI: inputs required for the development of AI systems, AI as a process and the output of AI applications. Overall, it is emphasised that, while recent legal and policy discussions have mostly focused on AI-aided and AI-generated output, a more holistic view that accounts for the role of IP law across the AI innovation cycle is indispensable.

Download the Position Statement from SSRN at the link.

Ring on the IPO Artifical Intelligence and Intellectual Property Call for Views Copyright

Caoimhe Ring, University of Oxford has published IPO Artificial Intelligence and Intellectual Property: Call for Views Copyright. Here is the abstract.

In response to the UK Intellectual Property Office’s call for views on Artificial Intelligence and Intellectual Property, this paper by the Oxford Intellectual Property Research Centre addresses the impact of artificial intelligence (AI) on exceptions in copyright law. Response focus on balancing the protection of the rights of copyright-holders as against the need for a variety of large data inputs from international sources for ethical, robust and innovative AI development. This paper argues that AI developers may benefit from the quotation exception as a form of ‘global mandatory fair use’, whilst calls for enhanced collective licensing arrangements to ensure proper remuneration for copyright-holders.

Download the call from SSRN at the link.

Specht on Copyright Protection for Algorithm Products @louisa_specht

Louisa Specht, University of Bonn, is publishing Copyright Protection for Algorithm Products? in Wettbewerb in Recht und Praxis 2021. Here is the abstract.

A portrait of a somewhat pale-looking man painted in a style of an artist dead for over 340 years now: “The Next Rembrandt” is an incredibly realistic 3D print work created in 2016 based on data mined from 346 original Rembrandt paintings via algorithm. When the line between man and machine blurs, our existing laws governing inherent anthropocentric rights reach their limits of adequacy — such as copyright protections. Are such algorithm generated works copyright protected and shall they be copyright protected? Do we need to change copyright to protect them? The paper proposes a "phase model" for copyright protection of such algorithm products de lege lata and a new neighboring right de lege ferenda.

Download the article from SSRN at the link.

Mendis, Noto La Diega, Romero-Moreno, and Onishi on AI and IP: The View of BILETA @LawTechGadget @guidonld @bileta

Dinusha Mendis, Bournemouth University, Guido Noto La Diega, University of Stirling, Felipe Romero-Moreno, and Hiroko Onishi, Kingston University, have published Artificial Intelligence and Intellectual Property: The View of The British and Irish Law, Education and Technology Association (BILETA). Here is the abstract.

Between September and November 2020, the UK Intellectual Property Office (UKIPO) ran a consultation about the implications Artificial Intelligence (AI) may have for Intellectual Property (IP) policy, as well as the impact IP may have for AI. This response was prepared on behalf of the British and Irish Law Education and Technology Association (BILETA) by Prof Dinusha Mendis, Associate Prof Guido Noto La Diega, Dr Felipe Romero-Moreno, and Dr Hiroko Onishi. BILETA was formed in April 1986 to promote, develop and communicate high-quality research and knowledge on technology law and policy to organisations, governments, professionals, students and the public. This submission focuses on the maincopyright and trade mark issues in AI.

Download the article from SSRN at the link.

Matulionyte on How Australian Copyright Law Impedes the Development of AI: What Are the Options? @rita_matu

Rita Matulionyte, Macquarie Law School, has published Australian Copyright Law Impedes the Development of Artificial Intelligence: What Are the Options?. Here is the abstract.

Artificial Intelligence (AI) is an emerging technology that has a huge potential in contributing to the Australian economy and addressing economic and social problems in society. However, Australian copyright laws are likely to impede the development of AI, and machine learning in particular, by requiring authorization every time when copyrighted content is used in machine learning processes. This puts Australian AI industries in competitive disadvantage, since other AI-focused jurisdictions, such as the US, EU, UK and Japan, allow such use under copyright exceptions. After analyzing the scope of copyright with relation to machine learning and licensing options, this paper examines different copyright exceptions (fair use, fair dealing and EU-style specific TDM exception) as potential solutions for Australia.

Download the article from SSRN at the link.

Hacialiefendioglu on Matter of Authorship in Works Created With Contribution of Nonhuman Sources @APerzanowski

Meric Hacialiefendioglu, Case Western Reserve School of Law, has published Matter of Authorship in Works Created with Contribution of Nonhuman Sources. Here is the abstract.

Humans have always been prone to finding new ways of creating works. As they discovered untraditional tools, or in other words contributors, the copyrightability of these works become questioned. This dissertation analyzes two kinds of contributors: computer programs and animals. Copyright law does not provide nonhuman authorship. Therefore, the question is whether or not the works generated with the contribution of computer programs and animals bear human expression so that they can be considered human works, thus be copyrightable. This dissertation proposes a test for finding human authorship and applies the test on real-life examples to provide guidelines to determine the copyrightability of the works.

Yu on Whether Algorithms Can Promote Fair Use @FIULAWREVIEW

Peter K. Yu, Texas A. & M. School of Law, is publishing Can Algorithms Promote Fair Use? in volume 14 of the FIU Law Review (2020). Here is the abstract.

In the past few years, advances in big data, machine learning and artificial intelligence have generated many questions in the intellectual property area. One question that has attracted growing attention concerns whether algorithms can be better deployed to promote fair use in copyright law. The debate on the feasibility of developing automated fair use systems is not new; it can be traced back to more than a decade ago. Nevertheless, the recent technological advances have invited policymakers and commentators to revisit this earlier debate. As part of the symposium on "Intelligent Entertainment: Algorithmic Generation and Regulation of Creative Works," this Article examines whether algorithms can be better deployed to promote fair use in copyright law. It begins by explaining why policymakers and commentators have remained skeptical about such deployment. The article then builds the case for greater deployment of algorithms to promote fair use. It concludes by identifying areas to which policymakers and commentators should pay greater attention if automated fair use systems are to be developed.

Download the article from SSRN at the link.

White and Matulionyte on AI Painting the Bigger Picture for Copyright Ownership @rita_matu

Courtney White (no affiliation) and Rita Matulionyte, Macquarie Law School, have published Artificial Intelligence Painting The Bigger Picture For Copyright Ownership. Here is the abstract.

To receive copyright protection in Australia works must be original, amongst other requirements. The originality standard involves ‘independent intellectual effort’ that originates from an actual person. The reality of today’s creativity domain is that works are not always originating from actual persons. Due to impressive advancements in technology, some works are being created by artificial intelligence and without the involvement of an actual person. These works cannot meet copyright requirements under current law and subsequently do not receive copyright protection. This paper endeavours to answer a two-tiered question posed by the challenges artificial intelligence works have on traditional concepts of copyright. Firstly, should copyright subsist in works created by AI? Secondly, who would possibly be the copyright owner for such works? Answering these questions involves a discussion of utilitarian and natural rights theories and references to US and UK discussions on the conversation around copyright and artificial intelligence.

Download the article from SSRN at the link.

Asay on Artificial Stupidity

Clark D. Asay, Brigham Young Law School, is publishing Artificial Stupidity in volume 61 of the William & Mary Law Review (2020). Here is the abstract.

Artificial intelligence is everywhere. And yet, the experts tell us, it is not yet actually anywhere. This is so because we are yet to achieve true artificial intelligence, or artificially intelligent systems that are capable of thinking for themselves and adapting to their circumstances. Instead, all the AI hype — and it is constant — concerns rather mundane forms of artificial intelligence, which are confined to performing specific, narrow tasks, and nothing more. The promise of true artificial intelligence thus remains elusive. Artificial stupidity reigns supreme. What are the best set of policies to achieve true artificial intelligence? Surprisingly, scholars have paid little attention to this question. Scholars have spent considerable time assessing a number of important legal questions relating to artificial intelligence, including privacy, bias, tort, and intellectual property issues. But little effort has been devoted to exploring what set of policies are best suited to helping artificial intelligence developers achieve greater levels of innovation. And examining such issues is not some niche exercise, since artificial intelligence has already or soon will affect every sector of society. Hence, the question goes to the heart of future technological innovation policy more broadly. This Article examines this question by exploring how well intellectual property rights promote innovation in artificial intelligence. I focus on intellectual property rights because these are often viewed as the most important piece of United States innovation policy. Overall, I find that intellectual property rights, particularly patents, are ill-suited to promote radical forms of artificial intelligence innovation. And even the intellectual property forms that are a better fit for artificial intelligence innovators, such as trade secrecy, come with problems of their own. In fact, the poor fit of patents in particular is likely to contribute to heavy industry consolidation in the AI field, and heavy consolidation in an industry is typically associated with lower levels of innovation than ideal. I conclude by arguing, however, that neither strengthening AI patents rights nor looking to other forms of law, such as antitrust, holds much promise in achieving true artificial intelligence. Instead, as with many earlier radical innovations, significant government backing, coupled with an engaged entrepreneurial sector, is at least one key to avoiding enduring artificial stupidity.

Download the article from SSRN at the link.

Rai on Machine Learning at the Patent Office @DukeLaw

Arti K. Rai, Duke University School of Law, has published Machine Learning at the Patent Office: Lessons for Patents and Administrative Law as Duke Law School Public Law & Legal Theory Series No. 2019-37. Here is the abstract.

The empirical data indicate that a relatively small increment of additional USPTO investment in prior art search at the initial examination stage could be a cost-effective mechanism for improving accuracy in the patent system. This contribution argues that machine learning provides a promising arena for such investment. Notably, the use of machine learning in patent examination does not raise the same potent concerns about individual rights and discrimination that it raises in other areas of administrative and judicial process. That said, even an apparently easy case like prior art search at the USPTO poses challenges. The most important generalizable challenge relates to explainability. The USPTO has stressed transparency to the general public as necessary for achieving adequate explainability. However, at least in contexts like prior art search, adequate explainability does not require full transparency. Moreover, full transparency would chill provision of private sector expertise and would be susceptible to gaming.

Download the article from SSRN at the link.