and worldwide have continued to express deep concerns about Big Tech firms and their extensive collection of personal digital data, which affects how markets operate and compete. In a I coauthored with Professor Kean Birch of 91ŃÇÉ«, we dove into these policy materials, using to explore recurring themes in across various regions. Published by the , our work also sheds light on how the collection of personal data is portrayed in the latest review of competition laws, policies, and regulations, and the implications for evolving competition policy

Why Competition Policy Matters

Big Tech firms are powerful political-economic actors within the economy, especially when it comes to the mass collection and use of digital personal data. As , in a data-driven digital economy, they can therefore shape and dominate markets by structurally and strategically undermining competition through their constructed platforms—data-driven ecosystems that appear separate from the market. This capacity gives Big Tech firms structural and techno-economic power over their competitors, making it more important than ever for competition law to step up its game. Through a thematic policy analysis, our research reveals a series of key issues that policymakers around the world are identifying as important structural and techno-economic implications of Big Tech for competition.

Structural and Techno-economic Dimensions of Big Tech’s Market Power

A significant part of Big Tech firms’ market power lies in economies of scale, which can create tough barriers for new competitors to break through. For example, as points out, the high costs needed to start a business can be a genuine hurdle for newcomers, while established companies can handle regulatory costs much more comfortably. Additionally, the costs involved in switching from one provider to another can make users hesitant to change. As highlighted by , the digital economy has sped up the impact of these economies of scale, in part because personal data complicates how we understand market definitions in competition policy. The basic assumptions that guide competition policy often use price theory to define markets and identify anti-competitive behaviour. These competition frameworks therefore struggle to address situations involving seemingly ‘free’ goods (like search engines) or the trade of these free goods and services for personal data. , ).

Meanwhile, the techno-economic side of the power held by these Big Tech firms includes both the strategic and responsive growth of relationships involving technology and political-economics. This growth is aimed at connecting a range of stakeholders, including governments, businesses, users, and academia, with the infrastructures and platforms created by Big Tech.

Structural Implications of Big Tech for Competition

Scholars such as have highlighted the significance of the network effect as a key structural implication of Big Tech for competition policy. These companies have established themselves as intermediaries in building multi-sided market platforms. Network effects result from how the number of users in a network (e.g., social media platforms, search engines) increases the usefulness of the network to its users, thereby raising its attractiveness for new users. Consequently, as the noted in 2020, network effects lead to a self-reinforcing cycle in which users migrate to the fastest-growing network. With this network effect, Big Tech companies are amassing a startling amount of data, providing them with an enormous competitive advantage, creating barriers to rivals entering or thriving in relevant markets, and allowing the incumbent digital platform providers to expand into adjacent markets.

The second structural effect is connected to but distinct from the first: investments made by Big Tech firms mean they can scale up with lower-than-usual costs. As the UK's 2019  put it, ‘Both the scale and the data that the platforms possess on consumers make it hard for other players, including publishers, to compete.’ Economies of scale have provided significant benefits for Big Tech firms as they have grown quickly to dominate their markets. This is clearly becoming a cause for concern amongst policymakers worldwide (as seen in, e.g., , , , OECD 2022). The main negative effect of such economies of scale is the loss of market contestability: there are significant barriers to entry into digital markets because Big Tech incumbents benefit from first-mover technology advantages; there are also significant disparities in market information; and then there are disparities in the capacity to adjust prices because incumbents benefit from greater information (e.g., data collection) and higher processing capacity (e.g., computing infrastructure). 

The third structural issue identified in our paper is the gatekeeping role of these Big Tech companies in our societies and economies. Policymakers have thus noted that a few digital gatekeepers hold the keys to the crucial digital infrastructure that impacts our everyday lives—whether it's staying in touch with friends, finding job opportunities, or accessing information. Gatekeepers can control access to the users and their data, which can hold significant value for other firms wishing to connect with consumers. The fact that this vital digital infrastructure, including personal data, is largely provided by Big Tech, makes it tough for startups and competitors to enter the market.

Techno-economic implications of Big Tech for competition

The first techno-economic issue we identify is the capacity of Big Tech to enter adjacent markets through data collection. As the  pointed out in 2019, ‘The extensive amount of data available to Google and Facebook provide these platforms with a competitive advantage and assist with entry into related markets.’ Data-driven business models enable Big Tech to enter adjacent markets through the modular extension of technical standards and terms and conditions (e.g., APIs, SDKs, plugins).

The second techno-economic issue concerns the spread of market power through the creation of digital ecosystems as ‘walled gardens.’ An ecosystem is more than a platform: it is the configuration of technical devices, applications and software, platforms, users and developers, payment systems, terms and conditions, and other legal rights and claims and standards (see: Autoriteit Consument & Markt, 2019). As explained by the , through this ecosystem, end-users get locked in, reducing the opportunity for competition, even when products and services (e.g., Gmail, Facebook) are notionally ‘free.’

The third techno-economic issue follows the second: Big Tech reinforces its market power by creating ‘enclaves’ in which they govern economic activities. These enclaves are distinct from markets; they sit inside wider markets, , but gatekeepers can also establish the internal ‘rules of the game’ and control market information. Policymakers have highlighted various relevant business strategies and practices—including the setting of defaults, cross-selling, and self-preferencing—that reduce competition within these techno-economic enclaves.

Challenges of digital personal data for competition and competition policy

The mass collection and use of personal data by Big Tech therefore has structural and techno-economic implications for competition policy—implications with which policymakers around the world are now grappling.

A key consideration in these policy materials is the techno-economic dimension of data-driven leverage. Policymakers repeatedly observe that Big Tech enjoys a competitive edge, primarily because of its vast personal data reserves and its ability to limit other companies' access to this valuable information. Although any digital firm can gather personal data, having substantial data holdings boosts innovation potential and offers a notable business advantage. This concern has been underscored by the.

Already concentrated digital markets are likely to concentrate further without concerted action to change competition policy. Our paper demonstrates the growing awareness among policymakers of the important effects of Big Tech and personal data collection on competition and market power. Of course, there's also a looming concern that the winner-takes-all dynamics fuelled by data control could influence the future development of important technologies like artificial intelligence, which significantly depend on large training datasets.

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

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Throughout her doctoral studies, Amanda Turnbull has grappled with the legal consequences of “machines doing things with words.” Her timely dissertation, Law, Language, and Authority: The Algorithmic Turn, completed in August 2024, offers a measured yet unflinching reflection on how artificial intelligence is transforming society and the law. Speaking over Zoom from her home in New Zealand, where she is now a at the University of Waikato’s Te Piringa Faculty of Law  Turnbull shared some insights from her research.

"With AI, there’s an algorithm at the end of the hammer.”

At the heart of Turnbull’s thesis is her contention that AI is “more than just a tool.” When we think of a tool, Turnbull suggests, we usually think of something like a hammer. There’s always a person at the end of the hammer, and they’re responsible for what the hammer does. In the context of algorithmic systems, commentators have proposed different alternatives for who that responsible party might be, including the programmer, the end user, and the company that owns the technology. But these approaches obscure the true novelty—and danger—of AI. With AI, Turnbull explained, “there’s an algorithm at the end of the hammer.”

Turnbull’s focus on algorithmically generated language reflects her thesis’s remarkable origins at the University of Ottawa’s Department of English. Although her original supervisor, the late Professor (Canada Research Chair in Ethics, Law & Technology), soon recognized that it belonged in a faculty of law, Turnbull’s dissertation maintains its indebtedness to mid-century philosopher of language, JL Austin—who, Turnbull was surprised to learn, was a close friend of the legal theorist . emphasized what words do in addition to what they mean. Adapting this framework to contemporary technology, Turnbull is less interested in what a generative AI like ChatGPT says than the difference it makes that a non-human actor says it.

The first of three “pillars” of Turnbull’s dissertation thus explores the consequences of AI’s participation in writing literary works. To be clear, “” according to Turnbull—but that doesn’t mean AI should have no legally cognizable role at all. Drawing on her early career as a classical flautist, Turnbull recognized that generative AI’s imitative reproduction of human-authored texts in its training data isn’t so different from the work of human artists. In her words, “there’s an amount of imitation that necessarily occurs when you’re being creative.”

Unexpectedly, Turnbull found inspiration in the “spectrum of authoring” developed by in the 13th century, long before the modern notion of authorship was developed. Generative AI, she asserts, resembles Bonaventure’s “commentator,” a mid-point between an author and a mere scribe, who clarifies and expands on pre-existing texts. By referring to generative AI as a commentator or “expositor,” lawmakers can reserve copyright for human authors without turning a blind eye to the authority embodied in algorithmically generated language.

That authority is at the centre of the second and third “pillars” of Turnbull’s research, which examine the legal implications of algorithmic contracting. As coined by , an algorithmic contract is a contract in which the main terms and conditions are drafted not by human actors, but by computer systems.

Key for Turnbull is how the systems behind algorithmic contracts exercise “derivative” authority without legal intent. For this reason, algorithmic contracting is “in no way” similar to earlier technologies such as click-wrap agreements, standard form contracts, or the archetypal pen and paper. In other words, there is no “functional equivalence” between algorithmic contracting and other platforms. Courts should therefore reconsider both the notion of technological neutrality and the application of intent-based contract doctrines, including the doctrine of unconscionability recently revived by the Supreme Court of Canada in .

In the third “pillar” of her thesis, Turnbull discusses how unconstrained algorithmic contracting creates the conditions for technology-facilitated sexual violence. She focused on Uber and the instances of sexual violence involving drivers and passengers documented in its 2019 safety report. Sadly, Turnbull described this chapter as “the easiest to write,” since it quickly “became obvious that this is a new way of exerting harm.” Yet the solutions to these problems are far from straightforward. In Uber’s case, the issue permeates the firm’s corporate culture and overall attitude toward innovation, she contends, which has failed to truly consider “the whole web of entanglements” impacting algorithmic language.

Ultimately, dealing fairly with AI will require “extraordinary ways of thinking” on the part of courts and regulators. But Turnbull is confident the law can adapt. The entire law of contracts and copyright, for example, can be seen as areas that have constantly adapted to new technologies. By approaching the algorithmic turn with both bravery and nuance, courts can learn to recognize AI as something that’s more than a tool, but no substitute for genuine human authority and intent.

Going forward, Turnbull is keen to use her dissertation, which was supervised by IP Osgoode Director , as a basis for further explorations of technology-facilitated gender-based violence such as platform violence and “onlife” harm—a term to describe the intersection between experiences online and in ‘real life.’ At the same time, Turnbull is interested in how algorithms have played a positive role in certain legal contexts. Although, as she says, “we’re hot to jump on technology and focus on the negatives,” in a forthcoming article on the 1999 Canada-US Pacific Salmon Agreement, she and co-author Donald McRae will explore how, “in this case, the algorithm solved the dispute.” Turnbull also plans to publish her dissertation as a book and to return to another book she began writing even before beginning her PhD—a fictional novel that is, aptly, about Austin, Hart, and the father of computer science, Alan Turing


John Nyman is a student at Osgoode Hall Law School (JD '26) and an IP Osgoode JD Research Fellow

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Photo Credit: Adib Hussain on Unsplash (edited by Ashley Moniz)

Photo Credit: Adib Hussain on .

Ali Mesbahian is an IPilogue Writer and a 2L JD Candidate at Osgoode Hall Law School.

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The ever-growing reliance on artificial intelligence (AI) in our everyday life and industry is an undisputable condition of our time. Whether we speak of gaming, speech and facial recognition, smartphones, medical research, agriculture, trading and investment, cybersecurity, or resource extraction (and of course, much more)—few, if any, sectors fall outside the purview of AI. As with any emerging technology, as to whether AI is a “net good” are abundant. This brief article focuses on this issue with respect to AI’s environmental impact.

For starters, it is important to acknowledge AI’s potential in combatting climate change. Among other things, lead to better climate predictions, create virtual simulations that demonstrate what a given area would look like after the impacts of climate change, and help track the source of carbon emissions for regulation purposes.

On the other hand, AI requires infrastructure that consumes a great deal of energy. study conducted at the University of Massachusetts Amherst shed light on the enormous scale of this consumption: the energy required to train a single natural language processing (NLP) model leaves a carbon footprint of roughly 300,000 kg— (for a fascinating map on the human and environmental costs of AI, see ). Of more concern, “.” But what contributes to this increasing energy-intensive dynamic? Consider the following two points .

First, some researchers and academics have raised concerns about the AI community’s hyper-focus on their models’ accuracy and which come at the expense of cost and energy-efficiency considerations. Accordingly, calls are being made to research “ Ìęthat not only incorporates the energy consumption levels of a given AI model in its evaluative criteria, but also factors in the renewability of energy-sources and the extent to which a given model’s research results can be reproduced for future research.

Second, a 2020 , illustrates the close connection between tech and fossil-fuel industries. For instance, while Microsoft has vowed to become “carbon negative” by 2030 in order to counteract its contribution to environmental damage, it also offers AI capabilities to oil and gas companies such as ExxonMobil “in all phases of oil production.” Microsoft is not alone in signing these kinds of lucrative contracts; it’s joined by companies such as Amazon and Google. This casts huge doubt on the achievability and commitment of tech firms’ own climate goals. As was the case when , it is important for both civil society and insiders in the tech industry to pressure corporate executives to stop assisting the extractive activities of the fossil fuel industry and be more aggressive in reducing their own carbon footprints.

It is important to mention that the success of initiatives aiming to reduce the negative impacts of AI depend on the , both domestically and internationally. In this regard, I end with a hopeful starting point: Germany’s supreme constitutional court’s historic ruling in April 2021 that rendered the government’s climate goal to achieve carbon neutrality by 2050 as . The Court found that the government’s policy simply does not go far enough in protecting future generations from the catastrophes of climate change. As a result, the German government is in the process of bringing forward a —which undoubtedly bears regulatory implications for the tech industry.

To learn more about the relationship between artificial intelligence and the environment, check out next week’s , hosted by IP Osgoode and featuring a panel of leaders in the fields of AI and sustainability.

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Photo Credit: Prof Pina D'Agostino

Prof Giuseppina D’Agostino is the Founder & Director of IP Osgoode, the IP Intensive Program, and the IP Innovation Clinic, the Editor-in-Chief for the IPilogue and the Intellectual Property Journal, and an Associate Professor at Osgoode Hall Law School. She is also very proud of her new book!

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I am excited to share that I just published a collection on Artificial Intelligence (AI) and the law, Leading Legal Disruption: Artificial Intelligence and a Toolkit for Lawyers and the Law (Thomson Reuters 2021). Co-edited with Dr. Aviv Gaon and Carole Piovesan, the book provides a provocative analysis on the emerging terrain of AI and how it interrogates various areas of the law. The book, that features a foreword from the Hon. Marshall Rothstein (formerly of the Supreme Court of Canada), is an international collaboration of thought leaders in AI, with contributors from Canada, the USA, Europe and Israel. Issues discussed include intellectual property, privacy, contract law, regulation, governance, ethics, business and more. Importantly, such issues merit a toolkit of practical and international perspectives as they are increasingly complex and ajurisdictional. ÌęÌę

In many ways this book is also a reflection of Osgoode’s strengths in AI. My co-editors, Dr. Aviv Gaon, Director at IDC Herzliya of Experiential Programs, is a PhD graduate (class of 2019) publishing several other books on AI and emerging technology, and Carole Piovesan (class of 2009) has co-founded her own firm, INQ Law. ÌęI am myself an LL. B graduate from Osgoode (class of 1999), eventually returned as faculty to found and run IP Osgoode and I am currently co-chairing the 91ŃÇÉ« AI & Society Task Force, among many other initiatives in this space.

I am particularly thankful to the Osgoode JD students who provided helpful research assistance: Elif Babaoglu, Daniel Joseph, Joseph Simile, Rachel Marcus, Christopher Tsuji, and Julianna Felendzer.

I am most grateful for the enthusiastic endorsements by Prof David Vaver (Professor of Intellectual Property Law, Osgoode; Emeritus Professor of Intellectual Property & Information Technology Law, University of Oxford), Prof Jane Ginsburg (Morton L. Janklow Professor of Literary and Artistic Property Law, Columbia University School of Law), Justice Michael Manson (Federal Court of Canada), and Dan Bereskin (Partner, Bereskin & Parr LLP) all which can be read on the back cover.

They say you can’t judge a book by its cover, but I particularly like this one, inspired by an AI and suggestive of our youth, our future ultimately grappling with AI and other emerging technology, that will iterate in every generation.

I look forward to hearing from you on your thoughts on the book (and the cover!). You may order your copyÌęhere.

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Augmenting the Legal Profession
While AI can give us comprehensive models that no human has come up with, and it can continue to learn based on new input, it does have its limitations. A great example of one such limitation, illustrated by Richard Zuroff, Senior Manager of Industry Solutions at Element AI, is in deciphering the meaning of seemingly simple sentences. In the phrase: “The trophy does not fit in the suitcase because it is too big”, any person would naturally draw the conclusion that the trophy is the object being referred to as “too big”. But, a simple switch to “The trophy does not fit in the suitcase because it is too small” triggers a different understanding. The human mind understands that the suitcase is the object being referred to as “too small” because the mind can draw on experience and context. However, this simple switch is still something that stumps AI, despite how advanced the technology has become in many areas. Zuroff attributes this to a lack of real world understanding by AI - he says pattern matching, which is what AI does, does not equate to logical reasoning.

So AI is not perfect, and neither is the human mind. Zuroff believes those gaps may lead to a perfect partnership. He supports an augmentation approach to the legal profession, rather than an automation approach. AI is currently able to create transcripts from recorded interviews or testimony, create summaries of case histories, and even make suggestions regarding the best sources to use for research. Lawyers can then apply this efficiently accumulated knowledge, using context and life experience to come up with prudent legal advice or to help craft an argument. With this augmentation approach, Zuroff believes large firms can become more efficient, while small firms can gain the capabilities of a much larger firm.

The quality of any given AI technology is highly dependent on good AI design. Zuroff emphasized that however AI is designed, AI technology needs to be tailored to a specific industry and predicted use.

As we move into an age where AI will almost certainly play a bigger role than it already does, Zuroff had a few suggestions. He said that investing in complements to the new technology is critical. We need infrastructure within firms to support the new AI strategies, and a willingness to depart from the typical workflow. We also need governance and oversight over AI solutions. Without oversight, risks of implementing AI may include , , and having the AI .

Litigating AI
Criminal defence lawyer, Jill Presser stressed the need for good regulation of AI, which could prevent the need for litigation in this area. To Presser, good regulation means that AI outputs should be reviewable for reliability, and their often proprietary codes need to be subject to orders of disclosure in court. She also flagged the need to determine the admissibility of expert evidence of those who understand the decision-making processes of AI.

One other issue that triggers the need for strict regulation is automation bias, as demonstrated in , a case in which an actuarial tool assessed a Metis man’s psychopathy and risk of recidivism and placed him in maximum security prison. The tool was developed and tested on non-Indigenous populations, and no research confirmed the results would be valid when applied to Indigenous persons. Presser says that with adequate regulation, the burden of proving that these AI tools are well tested and reliable would be placed on the party seeking to use those tools. Regardless of regulations, however, Presser believes AI tools should not be determinative, and should only be helpful aids in sentencing and other decisions. Read more about issues with bias and transparency in AI policing tools , and more about the issues with governments using and regulating AI .

Immigration and AI
Patrick McEvenue, Director of Digital Policy at Immigration, Refugees and Citizenship Canada (IRCC), explained that IRCC is currently trying to determine how AI can be used in the application review process. Right now, they are looking at their back catalogue of applications to mark straightforward applications that were approved. They then feed those to the AI program, which learns and makes decisions about new applications. Applications that are not initially approved by the program are brought to an officer for review, and the officer’s ultimate decision feeds back into the system to help it learn. McEvenue stressed that the process is very human-centred, and the AI component of the process follows the to ensure transparency and procedural fairness. So far IRCC is only using AI to approve applications, not deny them. McEvenue anticipates that a lot of work will need to be done and a lot of questions answered by the IRCC before it can start using AI to deny applications. You can read more about the concerns of using AI in immigration .

AI and ‘Smart Contracts’
The final panelist was Amy ter Haar, a lawyer and doctorate of law candidate. Her area of interest is smart contracts, which may have multiple uses in the near future, including banks giving out loans or facilitating automatic payments, insurance companies processing claims, and even postal services enabling payment upon delivery. explains smart contracts pretty well, but if you don’t have time, here’s the basic rundown: A smart contract is a piece of code stored in a blockchain. In the video, the example is Kickstarter, the popular crowd-sourcing website. If a company chooses to find supporters through Kickstarter, both the supporters and company must put their trust in Kickstarter as the intermediary who holds their money and either grants it to the company once the fundraising goal is reached, or refunds it to the supporters if the goal is not reached. In contrast, if a smart contract is used, the contract holds the supporters’ money in escrow until the final date to reach the fundraising goal arrives. At that point, it automatically allocates the funds to the appropriate party. The smart contract removes the third party (Kickstarter) from the process, and therefore removes any trust concerns or delays. It never allows total control over the money by any party. Smart contracts are also immutable, so once made they can never be changed. Some say this is a benefit, in that contracts cannot be tampered with, while others are concerned about the inability to correct or edit the contract. See for one critic’s concerns.

Other issues smart contracts may raise is that parties may enter into them with a pseudonym. This may pose issues regarding the legal capacity to enter into a contract. In addition, there is a question as to where and how contracts could be enforced, if neither party discloses their location. See for a more comprehensive understanding of the concerns.

Regulation, regulation, regulation
Amongst all the interesting information conveyed at the conference, the one sentiment that was repeated throughout the day was that there is a real need for the regulation of AI, and soon. Tough questions posed by Carole Piovesan, a co-founder and partner at INQ Data Law, such as - who is liable for the outcomes of AI decisions? What happens when there is a , for example? Or, when ? – underscore this need. While these tough questions need answers, they do create an exciting and interesting space in the legal profession. Those interested in taking up the challenge will likely discover that regulation has to be industry-specific, and it must take into account potential biases, tendencies towards collusion, privacy concerns, enforceability, and transparency, amongst other factors.

Written by Rachel Marcus, IPilogue Editor and JD Candidate at Osgoode Hall Law School.

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On March 21, 2019, we had the pleasure of attending IP Ogsoode'sÌę conference series held at the Toronto Reference Library. This year’s conference theme was data governance, with a focus on novel legal issues with respect to two key sectors - health/science and smart cities. Professor D’Agostino’s opening remarks touched on the legal and ethical dimensions of data governance, given the large amount of activity over the last year in the AI space.Ìę The day was broken down into five panel discussions, with a luncheon keynote by Professor Kang Lee from the University of Toronto.

Why is Data so Important to the Development of AI?

The first discussion focused on the impact of data quantity and quality which determine AI capability. Jonathan PenneyÌę(Assistant Professor of Law; Director, Law & Technology Institute, Schulich School of Law, Dalhousie University) provided three instances where data was more important than the AI systems themselves: in advancing AI, in addressing bias and discriminatory practices in existing systems, and in AI accountability and transparency to understand decision making. Notably, Alexander Wissner-Gross examined the last 30 years of AI development, and found that the recent advances were largely due to the availability of large data sets. In 2011, IBM Watson Jeopardy Champion used data from 8.6 million Wikipedia articles and in 2014, GoogleNet object classification used 1.5 million images on ImageNet to train its AI system. Carole PiovesanÌę(Partner & Co-Founder, INQ Data Law) Ìęechoed the importance of data to AI systems, and touched upon the two growing debates regarding data exchange and privacy.Ìę The crux of the privacy debate focuses on the trade-off between privacy as a quasi-constitutional value versus the importance of innovation and the need for data to produce public goods. She called upon the audience to think about what a fair exchange in today’s data marketplace means to them.Ìę Finally, the shifting policy led by the EU's adoption of the General Data Protection Regulation (GDPR) was discussed. In Canada, current regulations still focus mainly on consent. Both speakers acknowledged that we should be moving towards establishing standards as very few people actually enforce their rights.

Intellectual Property at a Crossroad

Three key ideas came out of the second panel discussion, namely, the issue of whether AI systems and programs are eligible for copyright or patent protection under current statutes, the international implications and developments, and the importance of AI in collaboration. Dave Green (Assistant General Counsel, IP Law & Policy, Microsoft)Ìęshared Microsoft’s perspective on AI’s role in enabling machine intelligence to simulate or augment elements of human thinking. Two copyright issues that come into play with AI are defining “Works of Authorship” and identifying whether specific types of “copying” are enough to create liability, both of which have been complicated by the use of computer programs and factual materials. Internationally, the requirement that humans be the authors of creative works is found in the constitutions of US, Hong Kong, India, New Zealand, in the UK and other countries. As technology and AI advances, do we want to continue to insist upon the requirement that authors of creative works be humans? If we don’t, what does that say about downstream issues such as intent, infringement, and liability? In regards to international approaches to data mining - should there be a fair dealing exception, particularly when you look at addressing the issue of bias? The WIPO recently established a new division that focuses purely on AI, which will be especially important given the spike in AI patenting activity that has occurred over the past several years. Shlomit Yanisky-Ravid (Faculty Member and Lecturer, ONO Academic Law School and Fordham Law School) challenged the audience with the Turing Test, proving that it is often difficult to identify between works created by AI or a human being.

Catherine Lacavera (Director of IP, Litigation and Employment, Google Inc.) shared her belief that the existing patent and copyright systems are robust enough to deal with changes we are seeing in AI, though the regulatory and social impact front of AI are changing at a fast pace. In this regard, it is important to balance social benefit with the potential for abuse and the importance of building diverse data sets and incorporating privacy and affordability in our design principles going forward. Maya Medeiros (Partner, Norton Rose Fullbright Canada LLP) stressed the importance of using IP rights to facilitate multi-party collaborations to protect AI innovation and incentivize collaborative behaviour. Furthermore, she raised the issues of fair dealing in data mining and the use of different types of IP rights to protect different aspects of works being generated.

Resolving Data Barriers

The third panelÌę focused on the tools required to access data and facilitates the development of AI.

Momin Malik (Data Science Postdoctoral Fellow, Berkman Klein Center for Internet & Society at Harvard University) discussed how AI is beneficial in certain contexts, such as for predicting behaviour. However, the data that is valuable for AI is often limited by access to copyright protected materials.Ìę For example, in the development of Google's information retrieval system, the company faced many copyright issues.Ìę However, they were able to successfully navigate the copyright challenges by entering into agreements with publishers to create , and ultimately make data more accessible to the public.

Paul Gagnon (Legal Counsel, Element AI)Ìęcontemplated whether sui generis legislation is the way forward. Europe, for example, relied on the existing concept of fair dealing as an exemption for data mining. However, this exemption is limited as it only applies to researchers and not commercial institutions. Having open data and accessible data are two distinct concepts. Accessibility does not necessitate that you can use the data. Uses may be restricted by specific purposes, such as “for academic use only”.

Dave Green concluded the panel discussion by contemplating whether copyright could “make nice with AI”. AI does not copy for the purpose of replicating the work or infringing on the underlying value of expression, but rather it can unlock different insights than “Works of Authorship”. This is the difference between the use of a photo as a work, for aesthetic purposes or factual reporting, and the use of a photo as data.Ìę Green looked at examples of how different jurisdictions are making copyright safe for AI and machine learning, such as the fair use exception in Israel. Democratizing the right to learn and research is essential to this field and it remains to be seen how other jurisdictions may embrace this fact.

Luncheon Keynote: Affective Artificial Intelligence & Law: Opportunities, Applications, and Challenges

Kang Lee (Professor and Tier 1 CRC Chair in developmental neuroscience, University of Toronto) amazed the audience with aÌęshowcase of his connected health venture, .Ìę Dr. Lee's interdisciplinary invention brings together research from neuroscience, psychology, physiology, and deep learning to produce AI that can detect, measure, and analyze human affect through physiological cues. The mobile application turns smart devices into a personal health tool that individuals can use to manage stress and get updates on their personal health. It uses (TOI), which uses video to recognize facial blood flow imaging from the human face. This image is then processed by , which is the AI that can detect and measure different human emotions. Dr. Lee’s work is significant as it demonstrates how AI can improve the health and science fields to give patients more control over their health care.

Big Data, Health & Science

The fourth panel discussion focused on the unique AI and data issues in the health and science sectors. James Elder (Professor, Lassonde School of Engineering; 91ŃÇÉ« Research Chair in Human and Computer Vision, 91ŃÇÉ«) discussed potential uses for converting raw data into 2D images and subsequently converting these images into 3D models. 3D modelling with real data has applications for road and pedestrian traffic. The technology may also address some privacy concerns since his 3D virtualization technology turns the 2D images into avatars, which has the effect of anonymizing visual appearances. There are many opportunities for visual AI to help improve daily processes.

Victor Garcia (Managing Director & CEO, ABCLive Corporation)Ìędiscussed how big data can transform the health sciences. Data helps to improve the way companies in this sector do business. Clinical, insurance claims, pharmaceutical, research and development, patient behaviour, and lifestyle data can all contribute a plethora of knowledge to the health sector. These can improve process efficiencies and make hospital resources available sooner to new patients. For example, Humber River Hospital used data analytics to improve their health care services and increase efficiency by 40%.

Ian Stedman (PhD Candidate, Osgoode Hall Law School; Fellow in AI Law & Ethics at SickKids’ Centre for Computational Medicine)ÌęhighlightedÌęSickKid's move to integrate AI into their practice with the development of a task force to examine how data governance and policies, infrastructure, AI solutions, and ethics interacted before implementing new AI tools.Ìę Stedman stressed that data source and quality are essential because in the health sector, it is essential to ask all the right questions to make accurate conclusions and diagnoses. With clinical studies, it is much easier to access data since there is a research plan, which includes the research purpose, the targeted population, and the results the researcher hopes to observe. However, with the data that AI relies on, in order to unlock its potential value, researchers study data to find patterns. Therefore, it is difficult to ask for secondary use disclosure before the research is conducted when the researcher may not know what they are looking for. The takeaway is that regardless of the industry, harmonization and collaboration are key. There is opportunity to put data together from different sources to discover the potential of new clinical decision making tools.

What Makes a Smart City?

In Toronto, and internationally, data privacy issues have come to the forefront of public discussion due to the development of smart cities. Given the proposed Sidewalk Toronto, the collection, storage, and use of data has led to a heated debate about data governance. The Mayor of Barrie, Jeff Lehman, discussed the project which calls upon start-ups and small organizations to develop new technologies that use data to address civic challenges. Instead of putting out a traditional municipal tender, the cities released a Request for Solutions and invited responses from the public to provide a cohesive opportunity for collaboration. In response to the issue of data localization in the Sidewalk Toronto debate, Mayor Lehman believes that consent is possible, but that the data must reside in Canada to ensure that the national government can set the rules around the data being collected. Finally, Mayor Lehman advocated for the use of Privacy Impact Assessments to evaluate the impact of new technology on privacy.

Neetika Sathe (Vice President, Advanced Planning, Alectra Inc.)Ìęadvocated for the importance of data policies regarding smart cities to be worked on at every level of government to develop a national data strategy. Furthermore, Sathe introduced the audience to some of Alectra’s projects and the data collection challenges associated with each. These projects included (end-to-end integrated EV workplace charging pilot project), the (which collects smart meter data), and the (which uses a private blockchain network that limits access to data).

Natasha Tusikov (Assistant Professor, Dept. Social Science, 91ŃÇÉ«; The City Institute at 91ŃÇÉ«)Ìęchallenged the audience to think about who should own, control and govern data related to smart cities.Ìę Prof. Tusikov discussed the issue of conflicting public and private authority, raising her concern that Waterfront Toronto is not an expert in IP, but in land development. As an example of regulating the governance of smart cities, Barcelona developed a manifesto outlining the importance of technological sovereignty and maintaining digital rights.

To close the panel discussion, John Weigelt (National Technology Officer, Microsoft Canada Inc.)Ìę spoke about the importance of solidifying the participants involved in developing a smart city and the business model we want to create. If employed correctly, AI will solve societal challenges. Municipalities and companies that can thoughtfully clarify their approach to AI first will prosper the most from its benefits.

The conference encouraged thought-provoking discussion about data governance and its implications on health and smart cities. We hope that the discussion about data collection and what we value as society continues beyond this event. Thoughtful and inclusive discussion will allow us to collectively brace for impact as AI technology continues to advance.

Written by Lauren Chan and Summer Lewis. Lauren Chan is an IPilogue editor and a business student at the University of Guelph, and Summer Lewis is an IPilogue editor and a JD candidate at Osgoode Hall Law School.

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Developed collaboratively by 91ŃÇÉ« and IBM, this virtual assistant demonstrates how technology – specifically AI – can be used in an educational setting to enhance the quality of the overall student experience. This is the first time that IBM AI technology has been used in this way at a Canadian university.

91ŃÇÉ« is continually introducing inventive new forms of experiential education and technology-enhanced learning that students want. The virtual assistant brings this same creativity to student services, so they are more tailored and responsive to individual student needs whenever they arise. More than 100 91ŃÇÉ« students are directly involved in developing this solution. They are helping the virtual assistant to get better and better at guiding students to the right self-service or in-person contact for academic support or counselling in such areas as mental health, campus involvement and career services.

Since starting a pilot phase in January, there have been more than 75,000 student interactions with the virtual assistant. These interactions and over 1,700 feedback comments have been used to train the solution, improving the way it understands student questions and the answers it provides.

“This is a transformative time for learning and 91ŃÇÉ« is proud to be collaborating with a global tech industry leader like IBM to connect our students immediately to the right network of people and supports to help them meet their goals,” said Lisa Philipps, 91ŃÇɫ’s provost and vice-president academic. “Together, with IBM’s powerful AI technology and 91ŃÇɫ’s innovative student services professionals, we are learning how to combine high-quality, in-person services with real-time information that is delivered to students’ personal devices. The unique virtual assistant is a breakthrough for 24-7 student support services and 91ŃÇÉ« is leading the way.”

Leveraging IBM’s AI technology, the student support solution relies on augmented intelligence to interact with students, letting them communicate in their own words, in English or French. The virtual assistant uses information about a student’s program of study and year level to respond to questions submitted in a free-form chat window. The more the solution is used, the more it is trained to better understand students’ questions.

“We are seeing a real appetite for transformative innovation in our post-secondary communities right across Canada, and 91ŃÇÉ« is one of the pioneers,” said Colette Lacroix, the national leader for higher education at IBM Canada. “By harnessing the power of artificial intelligence to make interactions personalized and engaging, 91ŃÇÉ« and IBM are making significant strides in improving the student experience. 91ŃÇɫ’s commitment to the success of its students is impressive and it’s been a great experience working together.”

The virtual guide offers immediate assistance to students seeking answers to their questions
“The virtual guide provides assistance in cool and engaging ways with the addition of emojis and interesting facts within the chat,”Ìęsaid Jasmin Itaychany, a student at 91ŃÇÉ«. “It has the potential to solve many problems. One very helpful tool is the fact that it sometimes provides a list of questions a student may want answers to and all they have to do is click on one to reveal a very detailed answer. This guide is especially useful for students who may suffer from anxiety when speaking to someone in person, which will help increase inclusivity and accessibility on campus.”

91ŃÇÉ« and IBM are committed to exploring innovative educational services that personalize learning both inside and outside of the classroom.

This article was originally posted on yFile, click to read the original post.

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While theoretically superior in terms of public safety, autonomous cars and advanced algorithms bring up a type of mechanical morality -- in decisions to be made between life and death of pedestrians, occupants, or property damage. Determined by humans, these ethical ‘what-ifs’ translate into potentially tangible scenarios; extensive hypotheticals and their implications are not a question of existence, but of approach. While coded ethics, according to Noah J Goodall, a University of Virginia research scientist in transportation, will have “situations that the programmers often will not have considered [and] the public doesn’t expect superhuman wisdom but rather rational justification,” what counts as ‘ethical’ is subjective . Factors in ethics go beyond death toll. Janet Fleetwood, from theÌę Department of Community Health and Prevention, Dornsife School of Public Health, Drexel University, how age, assigned societal value, injuries vs. fatalities, future consequences on quality of life, personal risk, and the moral weight of killing against a ‘passive’ death all contribute to the problem.

The legal framework for autonomous vehicles does not yet exist, but the laws and policies that govern automated cars must not derive from their creators.ÌęDoing so would place considerable responsibility on the programmers of automated vehicles to ensure their control algorithms collectively produce actions that are legally and ethically acceptable to humans. The ethical decisions must be uniform for the sake of simplifying liability and establishing an optimal process for future programming. Negligence, the current “governing liability for car accidents,” will expand to the field of programming. Consequently, it is important to in the cases of accidents.

Ethics being prescribed solely by manufacturers may result in an arms-race to best serve the consumer’s interests. The technique of marketing reveals itself in ethical ambiguity of preferential treatment of the consumer; instead of being ethically neutral, an autonomous car may be marketed to show strict deference to the consumer, diluting ethics into a protectionist game. Conversely, programming an automated car to slavishly follow the law might also result in dangerous and unforeseen consequence. Extensive research and decision-making experiments explored in a non-profit based model may simplify the scope of the problem; instead of imagining how to market the product, discussions over autonomous cars become purely performative. Consideration from multiple stake-holders such as consumers, corporations, government institutions, organizations and others may be necessary to construct clear and stringent ethical guidelines that is necessary in the implementation of automated vehicles into society, which requires extensive efforts to reach a uniform conclusion. However, ethical dilemmas regarding control algorithms that determine the actions of automated vehicles will inevitably be subject to philosophical issues such as “the trolley problem” — a no-win hypothetical situation in which a person witnessing a runaway trolley would either do nothing and allow it to hit several people or, by pulling a lever, divert it, killing only one person. In such circumstances, there is simply no right answer and this make ethical guidelines difficult to construct.

While one may propose a utilitarian approach should be adopted for the sake of simplicity, questions such as would humanity be comfortable having a computer decide the fate of their life -- and what if the machine’s philosophical understanding extends beyond dire incidents would undoubtedly arise. Professor Azim Shariff of University of California that found that respondents generally agreed that a car should, in the case of an inevitable crash, kill the fewest number of people possible regardless of whether they were passengers or people outside of the car. However, this raises the question of, would a customer buy a car in which they and their family member(s) would be sacrificed for the benefit of the public?

To delineate the complexity of the situation, Fleetwood multiple hypothetical situations regarding moral preferences to participants. The study concluded 76% of participants favored a utilitarian approach in which the maximum number of lives were saved, yet participants were reluctant in purchasing a vehicle that sacrificed its own passengers. Understandably, consumers maintain a bias towards their own life; few would desire a product that chooses to sacrifice its owner.

Perhaps, legal ethics of automated vehicles should not exist in human management but rather in a machine’s own ability to learn. This is known as machine learning, where the program provides systems the ability to automatically learn and improve from experience. Machine learning focuses on developing computer programs that can access data and use it learn for themselves without human intervention. Static programming arrives at pre-determined ethical conclusions, while machine learning generates its own decisions, distinct from purely human determined ethics. While introducing an objective or impartial arbiter to complex situations would be desirable, questions on how accurate its judgments may arise. Scholars propose to model human behaviour to ensure that cars, rather than behaving better, behave exactly like us, and thus impulsively, rather than rationally. One by Leon R. SĂŒtfeld et al. state “simple models based on one-dimensional value-of-life scales are suited to describe human ethical behaviour” in these circumstances and as such would be preferable to pre-programmed decision-making criteria, which might ultimately appear too complex, insufficiently transparent, and difficult to predict. This solution regarding machine-learning to mimic human decision-making appears to be oriented towards an essential aspect of social acceptance; the uniformity of robots with human behaviour. Therefore, instead of regulating automated vehicles through ambiguous ethical guidelines, they will base their decisions through humanistic thinking while still lowering accident rate compared to human drivers. Nevertheless, other issues still exist; questions of whether this technology is achievable in the future and who should be held responsible for automated incidents in the context of machine learning. Regardless, whether the guidelines for automated vehicles arise from policy regulators or machine-learning, society needs to embrace that autonomous cars will debut on the market in the coming years, and work towards addressing the floodgate of concerns for wider applications including life-or-death accidents.

Rui Shen is an IPilogue Editor and a JD Candidate at Osgoode Hall Law School.

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Professor Mac Síthigh drew on Sadiq Khan’s, the Mayor of London, this year and stressed the important role the law has in relation to technological and social development. At SXSW, Khan explained that the law plays a balancing role in mitigating the potentially negative impact of disruption while allowing society to evolve.

The concept of “smart cities” is something that highlights how the law is performing in the face of twenty-first century “disruption”. Professor Mac Síthigh linked the smart city concept to the sharing economy, which he defined as a situation that deals with transforming under-utilized assets in a manner that makes them more accessible to a community. This could lead to a reduced need for individual ownership of these resources.

Citing a recent , Professor Mac Síthigh explored how the collection of data in these cities unveils new legal tensions. For example, Alphabet’s Sidewalk Labs is reimagining Toronto’s eastern waterfront area, . This variation of a smart city will use sensors to measure garbage disposal, recycling, noise, and pollution. The increased presence of cameras can even collect data to help improve the flow of traffic. While the project promises some of the twenty-first century innovations many have been waiting for, it also reveals how some of the risks of such technologies are underexplored.

There is an inherent trade off in collecting data to help cities become more efficient and green. Residents will be giving up their privacy rights for the good of society. There is no way to live off the grid in this type of environment, which means that if individuals want to be excluded from data collection, they would likely reside outside of this community. Is full consent or exile the only choice in the age of smart cities?

Currently, different Canadian laws may apply depending on which entity is collecting the data, thus presenting different methods of action for residents.

1. If there is a commercial technology company collecting the data, the (PIPEDA) applies to these processes.
2. When this data is collected, accessed, or used by federal government institutions, the applies.

Both of these acts regulate how personal information can be shared and this may be applicable to data collected through smart cities.

Research from the (CIPPIC) reveals one of the weaknesses of the law in their current forms. Where information is not “personal”, it can be freely shared with third parties. In order for data to be non-personal, technology companies would be required to strip the data of personal identifiers. So, the data on garbage disposal, for example, cannot be linked to any addresses, names, photographs, and so on in order for the information to be sharable. Another caveat in sharing personal information is that individuals can choose to protect their information through confidentiality terms in a contract. This means that there could be a great onus on the residents in smart cities to find ways to protect their information if they truly wish for their data to remain private.

As Professor Mac Síthigh’s talk makes clear, smart cities and the concept of a sharing economy are not new forms of technology, rather they are new processes that rely on data in novel ways. In the same way that technology companies have rethought data collection, it is necessary for lawyers and policy makers to rethink how the law applies to this newest iteration of technology. It requires a careful balance of the existing laws that seem applicable to smart cities, such as privacy laws, in addition to new provisions that give consumers more opportunities to protect and take control of their data without completely excluding them from the innovation process.

Summer Lewis is an IPilogue Editor and a JD candidate at Osgoode Hall Law School.

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