Advances in technology, the development of the metaverse(s), and the expectations of today’s consumers continue to propel the demand for next-level content. The considerable cost of producing high-quality, ultra-realistic artwork at a faster rate is a harsh reality for creators across many industries, including games, film, television, automotive, architecture and more. The finite amount of creators and time available to design adds another layer of challenges and causes an increasing number of industries to turn to AI assisted artistry to solve the problem of producing and scaling high-quality content.

Introduction

AI uses machine learning technologies to review, digest, and analyze vast quantities of data to create rules of application called algorithms. Once “trained,” machine learning software can continually improve itself through the analysis of new data sources and through the observation of its own data output. In recent years, AI has expanded to include computing systems that aim to replicate the function of the human brain in analyzing and processing information (called artificial neural networks), as well as pairing computer networks in generative adversarial networks where the computers learn from each other.

The massive ingestion of data by AI machines and the works they create have generated considerable debate in the legal world, from which two key questions have emerged:

1. Can AI digest massive databases that include works protected by copyright and use machine learning to “author” creative works without infringing on copyright?
2. Is the output generated by an AI system protectable under copyright laws?

Another area of increasing scrutiny in the sphere of machine learning and AI is that of ethical compliance of AI systems – as evidenced by the increasing number of academic papers and debates occurring in that space.

Training AI with data protected by copyright

Generating works using AI is a creative process that often differs from traditional computer-generation. With the latest types of AI, the computer program can make many of the decisions involved in the creative process without human intervention, thereby elevating it from the status of “tool” to that of “creator.” At European policy level, considerable thought is currently being given to this particular question of AI-generated creations, as indicated in particular by the European Commission in its Communication of November 25, 2020.¹

Separately, policy-makers continue to debate questions arising from the use of data that is protected by copyright for machine learning purposes, during the stage leading to the development of software capable of self-generating “creations.”

Data and information used to train an AI system may or may not be subject to restrictions. Not all information is “protected” or “owned” – for example, protection is unlikely to extend to historical information about weather patterns, pollution levels, the shape of clouds, satellite imagery or birdsongs.

What about content protected by copyright? In any text and data mining (“TDM”) process it is typically necessary to “clean” the text and data being mined (which in some cases takes up to 80 percent of the mining time), in order to remove inconsistent, unreliable or redundant data, and to “normalize” the data into a specific format adapted to the relevant application. These mining operations usually involve copyright issues because they involve upstream acts of reproduction of the works or databases concerned. In order to be “read” by an AI system, they must be stored, at least temporarily, and sometimes modified (e.g., by formatting, cutting, merging, compilation, etc.) to make them usable. Each of these copying operations is likely to engage the right of reproduction that is reserved to the relevant copyright owners, which requires the express authorization of those copyright owners for the exercise of those rights. In the same vein, the storage and, if necessary, the communication of copies of the initial data set to third parties without such authorization is likely to infringe the monopoly rights of those copyright owners, unless an applicable exception exists. One of the most frequently used exceptions, under U.S. law, is the doctrine of fair use. However, the U.S. law approach differs considerably in that respect from the approach adopted recently under EU law, at articles 4 and 5 of the Copyright Directive (2019-790).

The differing, patchwork approaches of different jurisdictions to TDM exceptions creates opportunities for arbitrage of national copyright laws when it comes to carrying out TDM, particularly for commercial purposes. The absence of an untrammeled TDM exception within the EU clearly has potential to encourage AI users to train their AI systems on data placed on servers in jurisdictions with clear copyright exceptions, and to create consequential effects in areas such as business structuring, investment decisions and talent retention.

Text and data mining in the United States

As AI search engines crawl through the Worldwide Web endlessly seeking, digesting, and aggregating content, they inevitably digest copyrighted works such as music videos, songs, novels, and news stories. Since this digestion – which generally requires the making of a copy – is frequently performed without the express consent of the copyright holder, its legality often depends on whether it is permitted under an exception to, or outside the framework of, copyright law. Under U.S. copyright law, the exception that is most frequently relied upon is “fair use.”

Under section 107 of the Copyright Act, “fair use” is a four-factor test: (1) the purpose of and character of the use; (2) the nature of the copyrighted work; (3) the amount and substantiality of the portion used in relation to the whole; and (4) the effect of the use on the potential market for, or value of, the copyrighted work. Fair use of a copyrighted work for such things as teaching, scholarship, and research is specifically permitted by section 107. A key consideration that courts have used in deciding whether fair use exists is whether the use is “transformative.”

Whether copying of copyrighted material for the purpose of machine learning constitutes fair use is a hotly debated topic that will affect the future of AI in the United States. For example, Thomson Reuters and West Publishing Corp. have sued Ross Intelligence, Inc. over, among other things, its alleged use of machine learning to create a legal research platform for Ross from the Westlaw database. The outcome of this case is still pending, although Ross’ motion to dismiss was denied.²

Will fair use protect machine learning?

In a seminal case from 2015, the Second Circuit found Google Books’ scanning of more than 20 million books, many of which were subject to copyright, to be a “non-expressive” and transformative fair use of the texts because Google Books enabled users to find information about copyrighted books, as opposed to the expressions contained in the books themselves.³  A key learning from the case was the distinction made between ”expressive” and “non-expressive” use of copyrighted materials, the latter being deemed fair use by the court. Applied to AI, could the solution mean that so long as the original text does not “express” in the final work product, the act of machine reading is fair use?

We are not aware of U.S. courts applying fair use in the context of TDM, in part because cases considering AI functionality have often involved the express use of copyrighted material that qualified as traditional copyright infringement. For example, the Second Circuit found in a 2018 case, that although TVEyes’ “search feature” for Fox News content in and of itself might have been sufficiently transformative to be fair use, the fact that TVEyes also had a “watch feature” that redistributed copyrighted Fox News content to TVEyes users for a monthly fee did not permit a fair use defense (Fox News Network, LLC v. TVEyes, Inc., No. 15-3885 (Feb. 27, 2018)).

In practice, major TDM search projects are generally dealt with under contract, which has resulted in low instances of litigation. Academic and commercial arguments have also been raised against over-reliance on “fair use” for TDM. As a practical matter, a key factor that U.S. courts will look at is whether TDM deprives the copyright owner of the value of their copyrighted material.

Text and data mining in the European Union (Directive 2019/790)

In Europe, the recent Copyright Directive adopted in 2019 created two TDM-specific exceptions.

1. TDM for research that focuses on TDM by research organizations and cultural heritage institutions, limited to the purposes of scientific research (art 4).
2. TDM for any purpose that applies for everyone else, but with a significant caveat: the ability for copyright holders to opt out of that exception (art 5).

The caveat allowing rights owners to opt out is significant, and could potentially place a considerable burden on the shoulders of businesses that would arguably need to verify, each time a training set needs to be copied, whether owners of the underlying copyright-protected material have opted out or not. Otherwise, businesses could inadvertently be infringing copyright.

Given that there is no incentive for rights owners not to reserve their rights, we suspect that a great number of (traditional) copyright owners will want to reserve their rights and “opt out.” With regard to the manner in which rights owners could exercise their opt out, the Directive is somewhat unclear. It explains that a rights owner may only reserve those rights by the use of machine-readable means, and should be able to apply measures (e.g., technical measures) to ensure that their reservations in this regard are respected. This raises significant questions such as: (1) the exact manner in which the opt-out must be expressed, (2) at what point the TDM user needs to check whether the opt-out has been exercised (e.g., at the time when it first accesses the data, or on a continual basis?); (3) who bears the burden of proof as between the rights owner and the user (bearing in mind the difficulty a user will have in “proving a negative,” i.e., that the opt-out right has not been exercised); or (4) how to determine the period of permitted retention.

Assuming that certain types of rights owners will largely seek to exercise their opt-out rights, these new TDM exceptions are likely to provide a contrasting level of protection to businesses, depending on the type of data they use. If the data being used is likely to belong to the most traditional areas of the entertainment industry, then these exceptions may provide little support for use in commercial AI applications. The geopolitical context thereby created is one in which other jurisdictions have positioned themselves favorably in the race to become global centers for TDM and AI development, through their more developed, fit for purpose copyright exceptions.

Is AI-created content copyrightable?

AI creations are certain to constitute large parts of the landscape of the metaverse’s virtual worlds – sometimes literally, as in the case of the Azure-driven location models and maps generated in Microsoft Flight Simulator. The questions of rights and ownership in the outputs of AI systems raise their own problems.

International law espouses the human-centric concepts of personal expression, authorship, and originality as prerequisites for the existence of copyright in a creative work (and therefore for its protection and “ownership”).

Those concepts break down when the link between a human author and the creative work is interrupted – most infamously in the “monkey selfie” case, where a photograph taken by a monkey was found not to enjoy copyright protection.⁴  Outputs generated purely by AI systems (which are, depending on the facts, distinguishable from works created by humans with AI assistance) challenge the norms that only contemplate human creation of copyright works. Even the UK’s unique provision governing “computer-generated works,” – where the person “by whom the arrangements necessary for the creation of the work are undertaken” is deemed the author – confirms the need to identify a human rather than a system as the author of a “creation.”

Likewise, traditional justifications for copyright protection, such as incentivizing creation of works or protecting the natural rights of creators, break down when the creator is a machine requiring no incentivization and having no personality.

In short, both the EU and the UK legal systems do not appear to welcome or accommodate creations by robots, which (currently) seem destined to fall into the category of information that is free and free-flowing. Could an AI-generated metaverse reset our world by providing a great space for the public domain and “commons” to thrive?

Will an AI-generated metaverse compete with human-generated worlds in a great clash of intellectual property battles? The android’s doodle of an electric sheep may have no author and no copyright protection, but the programmer of the android may still want to license it to you.

In the United States, the primary purpose of copyright law is to promote the production of creative works by providing an economic incentive to authors through the protection of their works. This economic incentive is provided to authors for the public good, because enabling authors to be rewarded monetarily for their works will lead to the production of more creative content. As AI companies continue to invest in the technologies necessary for the machine-based production of creative works, will they be able to enjoy the economic protections of copyright?

Section 102 of the Copyright Act requires that for a works to be copyrightable, they must be “original works of authorship fixed in any tangible medium of expression now known or later developed…” While neither the Copyright Act nor the U.S. Constitution addresses the requirement of human authorship, the courts and the Copyright Office have operated on that basis. The Copyright Office has rejected attempted registrations of works produced solely by mechanical processes, and has included the requirement of human authorship in its Compendium of Copyright Office Practices.⁵

In 2018, the Copyright Office rejected Stephen Thaler’s application to copyright “A Recent Entrance to Paradise,” a work generated by his AI system and listed author, the Creativity Machine, on the grounds that it “lacks the human authorship necessary to support a copyright claim.” The Copyright Office also rejected Thaler’s claim that AI can be an author under the work-for-hire doctrine.⁶

The view of the Copyright Office is that a work generally needs to be of human authorship in order to be copyrightable, with the computer merely being an assisting instrument, and where the traditional elements of authorship (such as literary, artistic or musical expression) were conceived and executed by a human.⁷  This means that AI-created works in the United States will likely become part of the public domain when created and can be freely distributed. As it stands, this has profound implications for the development of AI-created works because the companies and investors behind the machines that produce them at present are not afforded protection under U.S. copyright law. There has been a lot of discussion as to whether U.S. copyright will evolve to afford this protection.

One argument for extending copyright protection to non-human authors is that other non-natural persons have been extended legal rights. Corporations in the United States have long been afforded the right to enter into contracts and enforce contracts to the same extent as human beings, as well as the obligation to pay taxes.

Some commentators have argued that the end user of an AI program generating creative content should be the owner of that content, using a concept of a machine- based work-for-hire doctrine, with the AI program being deemed the equivalent of a contractor who is hired by an employer to produce content owned by that employer.⁸  Others have cited the creative contributions that the end user makes in directing the AI program to produce a creative work as a justification for the end user being deemed an author of the AI-produced content, viewing the AI program as a tool of the end user.⁹

AI as an enforcement mechanism to protect copyright

Beyond having the ability to produce creative works, machine learning also provides human authors with the ability to enforce their rights and to better monetize their rights. Companies like Audible Magic, as well as Google and YouTube, have developed AI software that recognizes content and helps detect potential copyright violations. Their technologies should yield significant economic benefits for human authors.

Is AI-created output infringing?

The fact that AI can create output that mimics human expression and personalization means that AI’s use of copyrighted works for the purposes of machine learning may harm the market for works by human authors and thus come under increased scrutiny by (human) rightsholders. Even if the creation of the AI systems in and of itself is not infringing, if output generated by an AI system that has been trained on a particular type of data is substantially similar to the data in the dataset, it may be an unauthorized “derivative work” that infringes copyright in the preexisting works, which is a scenario far more likely to unfold with small and very small datasets.

Should AI copyright be based on creativity?

Some countries, such as the United Kingdom, have moved toward protecting computer-generated works (steered by humans) based on the elements of creativity contained in the work in order to encourage investment in AI systems. As AI continues to develop and generate more “creative” works, the debate over the ability to copyright these works, and who can own them, will undoubtedly grow.