Artificial intelligence systems often “give the vibe” of complete automated processing without human involvement. However, as reminds us, upon a closer “vibe check” there are layers of unseen and under-appreciated human inputs, efforts, and labour involved. The efforts of those unseen human hands are, in fact, the engine of AI innovation.

Dr. Dagne is the Ontario Research Chair in Governing Artificial Intelligence and an Associate Professor at 91��ɫ’s new Markham campus in the School of Public Policy & Administration. He also teaches Property Law at Osgoode Hall Law School, where he is an Affiliated Researcher with IP Osgoode. His current project, which he recently presented at the at the University of Cape Town, highlights how copyright enables the proactive exploitation of digital workers’ contributions as inputs to AI training or, in some cases, AI-assisted outputs.

By bringing to the fore the roles of digital workers, Dagne hopes to unearth the collaborative creation that goes into the AI production chain and feeds into the AI output. His paper, “Unseen Hands, Invisible Rights: Unmasking Digital Workers in the Shadows of AI Innovation and Implications for the Future of Copyright Law”, is soon to be published in a forthcoming volume on IP’s Futures: Exploring the Global Landscape of Intellectual Property Law and Policy (Ottawa UP, 2025), which Dagne is co-editing with and . His chapter probes the future of copyright law, attempting to turn the focus of copyright to collaborative authorship. This move, Dagne argues, could respond to demands for the fair allocation of rights between digital workers, as authors or joint authors in some cases, and AI designers as exploiters of digital works. 

Digital Workers are the Lifeblood of AI Development

As , “[AI] doesn’t run on magic pixie dust… [AI training] is a job that actually takes quite a bit of creativity, insight, and judgment.” Such ingenuity involves the preparation of data works for the datasets used to train and build AI technologies, which consists of a number of decisions as to the kind of data to collect, curate, clean, label, abstract, index, etc. The process of dataset development starts with formulating the problem, which is the conceptualization of the machine learning task by making the problems “into questions that data science can answer”. The task conceptualization is typically the responsibility of the AI designer, which may be an AI company like Open AI or Anthropic AI, for example, or platform company like Microsoft, Meta, or Amazon. After the conceptualization process comes the data collection, refining, and measuring stage. Dagne’s focus is on the “digital workers” who enter the picture at this stage in the AI production process.

According to these digital workers contribute to the training process of AI systems in three steps: generating and annotating data (AI preparation), verifying model output (AI verification), and directly mimicking model behaviour to produce a service (AI impersonation).  They range “from higher-skilled, ‘macro-task’ […] workers [who] offer their services as graphic designers, computer programmers, statisticians, translators, and other professional services, to [those engaged in] ‘micro-task’ [work] which typically involve clerical tasks that can be completed quickly and require less specialized skills.” () As described by , “complex projects are broken down into smaller, easily accomplished tasks, which can then be distributed to a large number of workers.” Micro-task activities mainly involve the AI preparation aspect of AI training processes but can also include the AI verification and AI impersonation steps in AI training.

The Copyright Question

Much of the debate around copyright and AI has focused on whether using the underlying work of which inputs are constituted (the images, texts, musical works and other subject matter) for unauthorized learning constitutes copyright infringement. However, Dagne’s focus is on the copyright that can subsist over collected data, as we see in some and cases, and whether digital workers’ activities in the preparation of training data sets in the AI pipeline could itself give rise to a copyright interest. This question can be answered by examining the nature of digital workers’ contributions to the tasks assigned to them and the ownership of copyright under the contractual agreements that digital workers sign with platforms.

Digital workers in the AI production value chain collect raw data and help add extra meaning by associating each piece of data with relevant attributive tags. Although have argued that this attributive task is a mundane exercise that could ultimately be automated, others like have contended that tasks such as attribution will always be assigned to humans because of their capacity to recognize and classify data. Indeed, human intervention is now in demand to recognize the nuances and sophisticated details of specific data. As noted by , an example of such demand is in the medical field, where an understanding of scientific vocabulary is required.

From a doctrinal perspective, the copyright question is whether the contribution of digital workers described above meets the threshold of originality—which is defined, in Canadian law, by the Supreme Court of Canada’s ruling in , and requires more than trivial skill and judgment in the selection or arrangement of data. If so, we might ask whether recognizing the copyright status of such contributions could address these workers' invisibility. Even if, on account of originality, the tasks executed by digital workers amount to authorship, of course such authorship does not automatically translate into ownership. The ownership of the creative tasks conducted by digital workers as part of the collaborative venture is determined either by the workers’ status as employees or otherwise by contract—which means that it is determined in the context of significant power asymmetries and the routine exploitation of digital workers.

If copyright entrenches the inequities of an asymmetrical situation—by ensuring that the collective effort of digital workers in compiling essential datasets for AI training and AI development remains unseen and undervalued—Dagne thinks the time has come to confront its complicity. He suggests that, spurred by the arrival of AI, the copyright system needs to restructure the relationship between authors-as-(data)workers and corporate proprietors in pursuit of greater fairness.

‘Damola Adediji is a Visiting Researcher with IP Osgoode and Doctoral Candidate with the Centre for Law, Technology & Society at the University of Ottawa.

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Emily Xiang is an IPilogue Writer, a Senior Fellow with the IP Innovation Clinic, and a 3L JD Candidate at Osgoode Hall Law School.

Is technology’s rapid rise really a great equalizer for improving social welfare globally?��

Professor Ruth L. Okediji is the Jeremiah Smith Jr. Professor of Law at Harvard Law School, where she teaches contracts, international IP, patents, copyright, and courses on Biblical Law. She is also the Co-Director of Harvard University’s Berkman Klein Center for Internet & Society. Her research and scholarship focus on examining innovation policy, the digital economy, and global knowledge governance. On September 29th, students and staff at Osgoode Hall Law School were honoured to welcome Professor Okediji in person at Osgoode, where she delivered a lecture on ‘’.��

The paradox is outlined as followed: while the existence of intellectual property (IP) protection is often justified by its contributions to improving human welfare (such as incentivizing innovators to create public goods), the benefits of technological improvements protected by these same systems are, in reality, not, experienced equally. . ��Legal regimes, including the IP system, often work to facilitate and increase global inequality by accruing wealth for their holders in the global north. However, this system often decreases the value of labour and unilaterally defines protectable subject matter based on imperialist norms.

In the tech sector, “skill-biased” advancements increasingly reduce opportunities for low-skilled workers, while elevating the productivity and earnings of high-skilled workers. Increasing prices of technology have only served to further disparities in access and skill development. The evolution of artificial intelligence (AI) systems, along with the effects of the recent pandemic, have also played significant roles in displacing human labourers. In terms of overall welfare, IP injustice is almost exclusively felt from the bottom-up.

In her lecture, Professor Okediji briefly charted the history of technological imperialism, and highlighted the structural, demographic, geographic, and cultural inequalities that arise from the modern IP regime. IP protection may no longer be said to primarily generate rewards and incentives for creativity and innovation. Instead, the structure of the modern IP system lends itself to be used as a vehicle to accumulate wealth and investment for the already affluent. Individuals and companies commonly ��engage in the strategic purchasing of critical blocking patent portfolios. For example, the rise of patent trolls, who litigate cheaply-bought patents, use the IP system as a legal weapon. Moreover, damage demands in litigation cases involving IP are rising. In 2021, for patent infringement relating to chip-making technology – one of the highest ever patent damage awards in the US. As modern IP regimes continue to tend towards serving purely economic interests, one cannot help but concede to the significant role that the modern IP system plays in perpetuating economic inequality.��

The global IP system is also ill-suited for the needs of developing economies. Not only is it inherently embedded in colonial traditions, even modern agreements such as TRIPS that seek to tie IP protection with trade policy effectively coerce developing nations into accepting stronger IP standards that are ill-suited to their needs. . Formal IP protection requirements such as standards of fixation, originality/novelty, individual ownership, or limited terms of protection, are all deeply rooted in Western ideals and norms, and often preclude valuable conceptions of innovation and creativity from Black and Indigenous populations in particular.��

Emerging scholarship increasingly demonstrates how inequality is embedded in the very foundations of IP law. In considering proposals for doctrinal reform in order to address these inequalities, Professor Okediji emphasizes the need to thoroughly explore related institutional and technological trade-offs, and advocates for radical transformation. Exceptions to international IP standards, enforcing mandatory licensing provisions, or speaking about “access” as the primary measure of equality may no longer be enough. Instead, we should focus on engendering educational reform in law schools, boosting literacy, improving the quality of education in marginalized communities, or developing and ameliorating community healthcare systems. Just as the problem is deep-rooted, the solution must be as well.

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Photo by Charles Fair ()

Emily Chow is an IPilogue Writer and a 1L JD Candidate at Osgoode Hall Law School.

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Over the past few years, there has been a rise of copyright strikes purporting to moderate infringing content on YouTube. Although this may protect copyright holders such as musicians and artists, its potential for misuse can , as is the case here.

On the 29^th of June in 2021, James Burch in solidarity and support for the family of . Steven Taylor, a 33-year-old Black man, was at a Walmart in San Leandro. As Burch and other supporters of the campaign outside listened intently to the pre-trial hearing, officers approached and asked them to move a banner. Burch and a fellow supporter started recording the interaction,

The . At , the police officer quickly begins to play Taylor Swift’s Blank Space from his cellphone speakers. Beyond the irony of trying to escape accountability and fill space with copyrighted music, the officer deliberately chooses to obscure the recording in the hopes of getting the video copyright stricken by YouTube’s AI algorithm. At , the officer says: “[the protestors] can record all [they] want, [but] I just know it can’t be posted to YouTube.” ��

This incident has not been the of such behaviour by the police. However, these unsettling attempts at eschewing accountability point to larger issues regarding freedom of speech, Anti-Black racism, policing, and obscure algorithms in America and beyond.

The right to record the police in the US has been reaffirmed in and , where the courts determined that recording a public officer’s actions resonates with principles enshrined in the : The right to record was in Derek Chauvin’s trial for his murder of George Floyd.

While the US courts have evidently prioritized the public’s rights to film officers, video and live streaming services like YouTube, TikTok, and Instagram have been much more obscure regarding how their algorithms target and remove videos. A preliminary search into YouTube’s is unhelpful in determining how videos are assessed on a “case-to-case basis” and instead redirects you to . The is a non-profit organization whose mandate is to protect user privacy and defend digital civil liberties. They have published , which is more user-friendly. However, it is important to note that automated processes like Content ID are alleged to independent , ; different equity-seeking groups have filed lawsuits against YouTube, including a group of and . TikTok has come under fire for flagging “vulnerable” creators and preventing their videos from reaching audiences. This practice is dubbed “.” Moderators identified creators through short clips, and most singled out creators with tags in their bios like “#disabled” or with pride flags.

Burch posted the video on July 1^st, 2021. It has not yet been taken down. However, questions remain as to the apparent lack of oversight for large US corporations and what this means for activists and creators. Just as the internet has expanded opportunities for mass global viewership and collective action, so too have the dimensions of existing power structures and inequality.

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