Artificial Intelligence and Intellectual Property: 2nd Advanced Training Program 2019 at CEIPI
CEIPI’s advanced training program on “Artificial Intelligence and Intellectual Property”, 16-18 May 2019, Strasbourg. Registration is opened!
CEIPI is pleased to announce the offering of a new Advanced Training Program on “Artificial Intelligence and Intellectual Property” that will take place in Strasbourg from 16 to 18 May 2019. This new training follows the very successful edition of past year, gathering a high number of professionals coming from thirteen European countries, and as far as Brazil, Canada, United States, China and Malaysia, and including senior officials from renowned institutions.
Artificial Intelligence (AI) and robots have been the subject of science fiction for some time. That fictional future is now a present reality. The AI market is predicted to grow from $8 billion in 2016 to more than $47 billion in 2020. Investment in AI increased more than 300 percent in 2017 compared to 2016. Intelligent machines, machine learning algorithms, sapient bots and neural networks have invaded our daily life. Humans will be increasingly called to interact with AI, especially in the digital environment. The regulation of AI’s activities is set to become a primary policy issue. Lawyers, entrepreneurs, or policy makers must fast learn about the key legal challenges that AI will bring about, if they want to play a role in a revolution that will occupy the policy debate for the next years to come and beyond.
In particular, AI’s disruptive effects on traditional business models will force a re-consideration of the Intellectual Property (IP) framework. Today, AI and algorithms write news and novels, generate artworks, write and perform music. Automated intelligence can drive innovation processes on its own initiative. AI challenges the most traditional IP legal notions, such as “copying”, “originality”, “creator”, “author”, or “inventiveness”. Can a robot be an author? Can a robot be an inventor? Can a robot co-author a work with human intelligence? Should AI’s inventions be considered prior art? Who owns the dataset from which an artificial intelligence must learn? Who should be liable for creativity and innovation generated by AI, if they impinge upon others’ rights or other legal provisions?
This training program will explore these thorny questions by dissecting legal, policy and ethical issues concerning AI impact on creativity and innovation. In this course, CEIPI will gather leading experts in the field to discuss these novel issues from a broader international perspective and make sense of inconsistent and inadequate national solutions. This training program should be of interest to creative economies and high-tech professionals and entrepreneurs, attorneys, policy makers, judges, and students.
Module 1 will provide an introduction to the training program in the first 30 minutes. The remainder of the class will discuss the theoretical, historical, and market landscape against which regulation of Artificial Intelligence (AI), Machine Learning (ML) and robots is emerging, with particular emphasis on machine-generated or computational creativity. AI’s disruptive effects on traditional business models will force a re-consideration of the Intellectual Property (IP) framework. In this context, this module will first present legal tools available to protect AI, including trade secrets, copyright and patent protection. Later, it will briefly introduce the landscape of normative efforts in multiple jurisdictions that starts copying with a potentially ground-breaking revolution. This module will also consider how alternative approaches to computational creativity regulation will have far reaching commercial implications, shifting incentives for developing AI. SPEAKER: Giancarlo FROSIO, CEIPI.
Module 2: Artificial intelligence writes poems, novels and news articles, composes music, edits photographs, creates video-games, and makes paintings and other artworks. AI can engage in any creative activities as technology like 3D printing enables computers to create physical artifacts without the need of human intervention. Like Google’s Deep Mind, which generates and performs music or creates artworks, AI does so by listening to other music or analysing previous artworks online. Which are the conditions for protection of creations generated by deep neural networks under the main copyright regimes? Is AI an author according to tradition copyright standards? Should traditional copyright standards such as originality apply, and perhaps machine-generated creative works fall in the public domain? This module will try to provide an answer to these basic issues surrounding AI’s creativity by looking into legislation implemented in several jurisdictions and relevant case law. SPEAKER: Ana RAMALHO, Assistant Professor, Maastricht University.
Module 3: After reviewing standards for AI’s authorship, Module 3 will delve into complex matters related to ownership of machine-created works and infringement. Who owns the copyright in a work generated by a machine? Should specific arrangements conferring authorship to the agents spending skills, labour and efforts to create AI in the first place regulate the field? In this context, however, ownership might still be tricky to allocate. Does it belong to the person who built the system, the person who trained it, or the person who fed it specific inputs? Again, AI might engage into copyright infringement as a result of its creative activities. How does the dichotomy idea/expression, the notion of originality or the doctrine of fair use apply to computational creativity? Open questions become more complex in light of the growing power of ML algorithms to rewrite reality. ML tools can turn shots of horses into zebras, black bears into pandas, dogs into cats, apples into oranges, and porn stars into celebrities, multiplying grounds for violation of economic and moral authorship rights and personality rights. Where to cast relevant liability for infringement in all these cases? SPEAKER: Ana RAMALHO, Assistant Professor, Maastricht University.
Module 4: This module will focus on AI’s applications in content moderation on digital platforms. In particular, copyright enforcement has been increasingly dealt through automated filtering and other algorithmic means. While semiotic governance online has become an issue that calls for extreme measures, taking down content through automated means poses challenges for online expression and access to information. In this scenario, governments and policy-makers are heavily pressuring companies to take action and a few jurisdictions have already responded with new regulatory initiatives. Meanwhile, judicial decisions have highlighted the inconsistences between automated enforcement and fundamental rights. SPEAKER: Giancarlo FROSIO, CEIPI.
Module 5 will look into novel issues emerging in connection with AI and data management. Data and Big Data processing is indeed a fundamental portion of machine learning. On one side, data ownership might emerge as critical issue to be carefully considered when dealing with AI and ML systems. Developing AI and ML systems generally involves training it using large datasets, so the system can continuously improve its decision-making abilities. Who owns the IP in the datasets which are used to train the system? Although data might be freely available online, it cannot be used for any purpose. Therefore, genuine issues of liability for use of proprietary data in ML processes might arise. On the other side, data protection regulations will play an important role in the evolution of AI and ML systems. This section will consider relevant legislation and case law, with particular emphasis on the interpretation of the upcoming EU General Data Protection Regulation’s provisions on profiling and automated decision-making. SPEAKER: Andres GUADAMUZ, Senior Lecturer, University of Sussex, United Kingdom.
Module 6 will consider a vast array of issues related with patenting AI and ML systems. In this context, this module will review international legislation, case law and patent offices’ practices, with special emphasis on EU-US comparative analysis. First, a fundamental challenge for protecting AI technologies with patents involves claiming subject matter that is patent eligible. Also, this module will consider how to identify what contributed to the development of an AI-related patent for the purposes of determining whether someone was an inventor. Further, satisfying disclosure requirements can be challenging when seeking patent protection for AI-based inventions. What should be disclosed in AI inventions to meet the requirements? Again, how an AI-based invention claim should be drafted? How does the doctrine of equivalents apply to AI inventions? Do different standards apply to rule-based systems and neural networks? SPEAKER: Jean-Marc DELTORN, Patent examiner, European Patent Office, EPO.
Module 7: What if an AI-enabled machine invents something? What if an AI algorithm—without any human intervention—develops a new business method, a drug, a machine, or other invention? What if an AI develops a technical improvement of itself? In this respect, as well as in the copyright domain, AI challenges the most basic patent notions. Can a robot be an inventor? Who owns AI generated inventions? This section will present the conditions under which the products of AI processes/systems can be granted protection. The issue of industrial applicability and plausibility will be discussed, as well as the relation to article 64(2) EPC (when the AI parent process is patentable). The problem of inventive step will be discussed too as well as the issue of defining the person skilled in the art when machines are imbued with an increased level of autonomy and (technical) creativity. The second part of the presentation will be devoted to the issue of inventorship and the (necessary) mention of the inventor on the title. This section will also expand upon the identification of the inventors and the difference between US and EU. As part of the “All Prior Art” project, an AI system continuously produces any possible meaningful combinations from published patents. A sister website “All The Claims” is attempting the same thing, but with the use of claims. These projects presumably aim at making the published concepts not patentable. Algorithms, AI and deep learning could potentially make everything under the sun prior art. This might support the idea that AI generated inventions without human intervention should be generally excluded from prior art. Is this the case? To which extent publication of computergenerated content should be treated as prior art and allowed to prevent others from obtaining patent protection on independently created inventions? Under current law and practices, do automatically generated claims qualify as prior art? Should automatically generated claims qualify as prior art? SPEAKER: Jean-Marc DELTORN, EPO.
Module 8: In the same way that AI challenges traditional concepts of authorship and inventorship, it also raises fundamental challenges to the concept of enforcement. The latter is generally understood as requiring the participation of the right holder and public authorities and is characterised by the implementation of fair trial basic tests. This session explores the possibilities arising from devices that automatically enforce intellectual property rights and what does self-enforcement mean for legal theory and in daily practice. This session will also present advances in deep learning relating to IP enforcement and how practice in courts and legal firms is being transformed by data analytics and AI. SPEAKER: Xavier SEUBA, Associate Professor, CEIPI.
Module 9: This module will consider trade secrets as an additional legal tool for protecting AI. This module will contrast patent protection for AI inventions with trade secrets protection and consider potential shortcomings of patent protection, in general. Unlike a patent, whose granting period might take a few years, trade secret protection arises automatically if secrecy of information creates a competitive advantage and there are reasonable measures in place to maintain secrecy. In this regard, trade secret protection may be especially well-suited for fast developing and changing AI inventions, whose improvements occurs at an extremely rapid pace. This module will consider the international protection of trade secrecy for AI by looking into recently adopted EU trade secrets directive, with some legislative examples. In particular, this module will discuss a case study where a trade secret and patent protection plays an increasingly interchanging role in relation to medical innovation, with special emphasis on personalized medicine. SPEAKER: Nari LEE, Professor, Hanken School of Economics, Finland.
Module 10: This module will discuss connected and autonomous vehicles as an emerging field where AI might raise relevant IP, trade secrets, and liability issues. This module will first introduce the landscape of autonomous driving projects and market applications. It will map out present legislation and emerging policy proposals to regulate autonomous driving. This module will discuss relevant issues related to the licensing, transfer and protection of autonomous driving technology. IP rights will play a critical role in enabling industry players to establish, and maintain, a position within this emerging market, with thousands of possibly competing patents being sought. The race to achieve market share will inevitably lead to a flurry of IP disputes, on the basis of patent, trademark, design, copyright or trade secrets infringements, as the Uber/WAYMO case might already show. Finally, this module will consider in details, IP liability that might arise from autonomous driving enabled machines. Under which standards liability should arise? To whom liability should be attributed for damages caused by machines and vehicles autonomously operated by an AI? These are all very novel questions that have been considered so far only from a theoretical perspective but shall have soon to be tested in practice. SPEAKER: Nari LEE, Hanken School of Economics.
Module 11 will focus on blockchain technology and alternative, decentralized architectures that rely on peer-to-peer networks and distributed technologies to provide secure and autonomous platforms for online interactions and communications (BitTorent, Bitcoin, Ethereum, etc). This module will analyse the legal framework in which these platforms operate, as well as alternative governance models combining regulation by code, contracts and social norms. «Distributed ledger technologies» (of which the blockchain is one instantiation) will also be discussed in relation to patentability (and exclusion thereof) either as mathematical method, business methods, or computer programs. In addition, this module will highlight how blockchain technology provides opportunities for both infringement and enforcement. Blockchain allows to track ownership/ transactions, effect payments, integrate data, and provide transparency. Existing implementation of blockchain technology for IP management include inter alia the music platform Muse and Ujo Music or the blockchain patent exchange Kyna. Finally, this module will consider liability that might emerge from managing and using these technologies, especially in the context of financial transactions. SPEAKER: Jean-Marc DELTORN, EPO. SPEAKER: Andres GUADAMUZ, Sussex.
For further information, please see the program: Please contact Ms. Isabelle Mahoudeau for administrative questions/registration (email@example.com) and Mr. Giancarlo Frosio for questions related to the academic content of the program (firstname.lastname@example.org).