With Canadians' mounting frustration��and the dwindling prospects of exiting��this pandemic any time soon, it is vital that we unite as a nation to innovate. How Canada continues to respond to the pandemic will also define how we respond to future global challenges. Leading the development of new vaccines, more effective personal protective equipment, and new and improved systems��of distribution and administration of the vaccine are just some instances of what is necessary now. This pandemic has highlighted our societal inequalities and our fractured innovative landscape.

The university, one of Canada’s cradles of innovation, must continue to innovate out of this crisis and future crises. With innovation more critical than ever, how do we increase collaboration, coordination, and access to salient data and information during prolonged isolation?

Intellectual property (IP) is a powerful legal tool to foster innovation. It merits a context-specific approach on when, and whether, to protect assets from the inventor/ startup stage to the scale-up phase. However, COVID-19 has amplified the challenges faced by our brightest researchers and innovators. They are unable to access laboratories, have limited access to funds to start up a company, lack the know-how and support, and do not know where to go to obtain the needed help to protect their inventions. Under these conditions, IP can go undetected until it is too late. Patents, trademarks, and copyrights protecting valuable work are not well understood, and often never see the light of day. Finally, when IP is detected and advised to be protected, the innovation costs are prohibitive, starting with the patent pro- cess costing upwards of $20,000 to protect a single patent.

It is no wonder then how Canada, a country with so much talent and potential, is still playing catch up to other countries’ patent filings and, importantly, commercialization successes in the form of licensing deals, startups, and scale-ups from their own valuable IP.

As a response, closer partnerships between universities and industries��are becoming commonplace. Take as��an example the University of Oxford��and AstraZeneca trailblazing partnership to tackle the global pandemic with a COVID-19 vaccine. While these university-industry partnerships can help, they also risk a power imbalance between Canadian universities and multinational companies. There is no guarantee that Canadian jobs will be generated and retained in Canada, even though they may be founded on Canadian science and innovation.

Another promising mechanism is the use of university commercialization clinics such as the IP Innovation Clinic at 91��ɫ’s Osgoode Hall Law School. The clinic is the first of its kind, where law firms supervise law students who work directly with clients to formulate an IP strategy. This initiative accounts for more than 6,000 hours of pro-bono work, saving innovators close to $2-million to date during a nascent stage where resources are scarce.

One of the clinic’s success stories is Skygauge Robotics, a drone robotics company that landed a $3.3-million funding deal, and did so during a pandemic through the clinic’s support. Skygauge’s ambition is to build a company that keeps people innovating and working in Canada — a perfect example of how providing a friendly and supportive innovation ecosystem can be a game-changer to Canada’s innovation economy.

Seeing the need to continue innovating, especially during the pandemic, the IP Innovation Clinic, seized on the possibilities of artificial intelligence (AI). Enter Isaac Pewton, the IP Innovation ChatBot that��can now answer any number of intellectual property questions. Powered by AI, the ChatBot learns and becomes smarter the more questions are asked of it. The goal is to balance the informational asymmetry in the innovation ecosystem and make valuable IP knowledge accessible to everyone for free.

This ChatBot is more important than ever to underrepresented communities, including women and Indigenous peoples who have typically not fared well in our in- novation ecosystem, and whose conditions are exacerbated from the pandemic. The ChatBot empowers these disenfranchised and remote communities with valuable information and direct access to the clinic for further services for free.

The ChatBot itself is an innovative example of a successful university-government-private partnership. Funded by Innovation, Science, and Economic Development Canada’s IP Clinics Program, pursuant to the federal government’s National IP Strategy and developed by a team of lawyers and technical experts at Norton Rose Fulbright Canada LLP, and Osgoode Hall Law School, the AI-powered ChatBot, by providing highly valuable IP information, can help Canadian entrepreneurs scale and learn quickly to innovate us out of this crisis and help future proof Canada against the next one.

Prof Giuseppina D’Agostino is a senior fellow with CIGI’s International Law Research Program (ILRP), effective November 2016. She is��the Founder & Director of IP Osgoode, the IP Intensive Program, and the Innovation Clinic, the Editor-in-Chief for the IPilogue and the Intellectual Property Journal, and an Associate Professor at Osgoode Hall Law School.

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In recent years a lot has been made about the impact that AI can have on different areas, including job recruitment. Hiring can be expensive. have put the cost of hiring a new employee at roughly $250,000 USD [1]. With the ever-increasing data companies are accumulating on their hiring processes and employees, AI is being looked at as a tool that could lower the costs of hiring and improve hiring efficiency [2]. This all sounds great, right? Unfortunately, AI has the potential to be just as biased during the hiring process as humans are. AI hiring tools are trained by humans, typically using data sets from the real world. Training AI using data that reflects your current workforce can expose the fact that hiring managers have given preference to certain types of candidates over others []. One example is the AI recruiting tool that Amazon developed in 2014 to hire engineers. As soon as the , it started discriminating against women []. The data that the AI was being trained on was based on existing engineers in the Amazon workforce []. Due to hiring biases, this workforce was disproportionately comprised of males []. As a result, the AI recruiting tool was favouring male candidates over female candidates based on gender alone.

As this example demonstrates, the issue of selection bias is particularly impactful when AI recruitment tools are deployed in typically white male dominated fields, like law. This brings me back to the OCI process.�� I mentioned that the process is broken, one of the major reasons why is because members of minority groups []. In theory, AI tools should be able to mitigate this issue. However, the Amazon example shows us that the AI system you’re implementing will only be as good as the data you use to train it.

Large corporations like Amazon have the resources and market power to gain access to high quality data and have still managed to run into data-related issues with their AI. What chance do smaller players in the AI industry have for filtering out low-quality data when they train their AI, if corporate giants aren’t always successful at it? If Canada wants to foster innovation and combat AI bias, we need to promote quality data. One way that this goal could be accomplished is by introducing a Text and Data Mining (TDM) exception into the Copyright Act. As Prof. Pina D’Agostino noted in her to the Standing Committee on Industry, Science and Technology, a TDM exception would allow data to be mined for AI training purposes, and provide Canadian stakeholders with the legal clarity they need to access quality data that can be used to develop their AI [].

The application of AI to law student recruitment highlights the potential for AI to affect our lives in ways we may not anticipate. Law students, as soon to be lawyers, are in a unique position to impact how AI is regulated moving forward. The future direction of AI will affect all of us, not just students interested in IP. It’s important to think about how AI interacts with the law, and how the law may need to adapt in order to effectively regulate AI. For example, is a TDM exception the right approach or would another approach be better?

At the risk of sounding too preachy, I think AI is something all law students should be informed about. Even if you’re not a law student, AI will have an increasingly direct impact on your everyday life moving forward. The more you know about it, the better equipped you’ll be to adapt and respond to it. In recent years IP Osgoode has been at the forefront of educating the legal community on the challenges posed by AI through their [9]. I encourage you to read over the conference proceedings as it’s a great way to learn about AI from some of the foremost experts on AI in Canada and abroad.

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Written by Lucas Colantoni, Osgoode JD Candidate, enrolled in Professors D’Agostino and Vaver 2019/2020 IP & Technology Law Intensive Program at Osgoode Hall Law School. As part of the course requirements, students were asked to write a blog on a topic of their choice.

��

[1] Rudina Seseri, “How AI Is Changing The Game For Recruiting” Forbes (29 January 2018), online: <https://www.forbes.com/sites/valleyvoices/2018/01/29/how-ai-is-changing-the-game-for-recruiting/#78abd1d71aa2>.

��

[2] Ibid.

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[3] Aaron Holmes, “AI could be the key to ending discrimination in hiring, but experts warn it can be just as biased as humans” Business Insider (8 October 2018), online: <https://www.businessinsider.com/ai-hiring-tools-biased-as-humans-experts-warn-2019-10>.

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[4] Jonathan Penney, Address (Why is Data so Important to the Development of AI?, presentation delivered at Osgoode Hall Law School, 91��ɫ, Toronto, 21 March 2019), online:<aichallenge.osgoode.yorku.ca/files/2019/05/Panel-1-Why-is-Data-Transcript.pdf>.

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[5] Ibid.

��

[6] Ibid.

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[7] Jane Gerster, “Reality check: Does name-blind hiring help improve diversity>” Global News (17 July 2018), online: <https://globalnews.ca/news/4329893/name-blind-hiring-diversity>.

[8] Pina d’Agostino, “Submission to the Standing Committee on Industry, Science and Technology for the Statutory Review of the Copyright Act” (IP Osgoode, 2018), online: <https://www.ourcommons.ca/Content/Committee/421/INDU/Brief/BR10269431/br-external/DagostinoGiuseppina-e.pdf >

[9] Osgoode Hall Law School, “Bracing for Impact: The Artificial Intelligence Challenge” (2018 and 2019), online: Osgoode Hall Law School <aichallenge.osgoode.yorku.ca>.

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From an efficiency perspective, it would be ideal to have technology neutral legislation. The speedy pace of technological advancement is simply not aligned with the much slower speed of legislative reform. There is a significant amount of turbulence in technology and innovation. However, the extent to which legislation can stay away from technologies is heavily dependent on its ability to provide sustainable legal certainty. This causes a tension with respect to solving a significant issue in technology, which is the urgency with which legal problems should be resolved. The impacts of a data breach, for example, are immediate and significant. This means that should a smart clothing consumer suffer a data breach, there must be accurate and applicable regulations in place to determine the correct course of action for relief.

Given this binary dilemma, it seems that a balance of both sides provides the most comprehensive answer. That is, while regulations should be technology neutral, they, at the same time, must be multi-leveled, with open-ended formulations, and offer a mix of both abstract and concrete rules. Furthermore, such regulations must be periodically reviewed to ensure relevancy and test the scope of the rules to govern advancements and innovations in the foreseeable future at the time of each review. The speed of technology, coupled with its serious security and privacy implications, demand no less.

For example, inventors of smart clothing in the United States are serious issues in their pursuit to navigate through the legal hurdles with respect to their products, simply because the law is not aligned with the advancement of technology. Data management, for instance, has not yet been properly regulated to correspond to such technologies. Regulatory approvals also pose a significant problem for device manufacturers and researchers since consent from the FDA (or a similar body) may take several years. This further prolongs getting approval and certification from insurance companies for products like smart clothing. Furthermore, forthcoming technologies, such as electronics miniaturization and new biocompatible materials will need to be considered through a regulatory lens for various legal concerns, such as consumer safety and environmental impacts.

That being said, the strategy posed in the next instalment of this series should be immediately applied to the privacy laws in Canada given their . Furthermore, such reform should not take the traditional form, as consumer and user input is highly important in order to keep the regulations relevant and direct. In this way, the regulatory structure would not only be effective, but it would also have a “coherent moral centre that the public can comprehend and accept”, according to Professor David Vaver in his well-known intellectual properly textbook. This new method of a reform strategy is outlined in more detail below.

This is the fourth post in the Toronto Wearables Series by Saba Samanian regarding wearable technology and its IP and privacy law implications. Saba was recently appointed the Toronto Ambassador for and seeks to do her part in fostering the wearables community in Toronto.

Written by Saba Samanian, 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's�� 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.�� 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 Penney��(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 Piovesan��(Partner & Co-Founder, INQ Data Law) ��echoed the importance of data to AI systems, and touched upon the two growing debates regarding data exchange and privacy.�� 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.�� 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)��shared 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 panel�� 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.�� For example, in the development of Google's information retrieval system, the company faced many copyright issues.�� 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)��contemplated 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.�� 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 a��showcase of his connected health venture, .�� 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; 91��ɫ Research Chair in Human and Computer Vision, 91��ɫ) 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)��discussed 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)��highlighted��SickKid'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.�� 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.)��advocated 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, 91��ɫ; The City Institute at 91��ɫ)��challenged the audience to think about who should own, control and govern data related to smart cities.�� 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.)�� 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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