Photo Credit: Ashley Moniz

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

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On June 28, 2021, IP Osgoode hosted a panel discussion in their Bracing for Impact Webinar Series titled AI’s Dirty Footprint. Organized in collaboration with the Harry Radzyner Law School at IDC Herzliya in Israel, Microsoft Canada, and Alectra's GRE&T Centre, the central question of this webinar was: in what way can we use artificial intelligence (AI) to ensure that the negative impacts of its energy consumption do not exceed its beneficial effects for environmental sustainability?

IP Osgoode’s own Professor Giuseppina (Pina) D’Agostino opened the panel by setting the stage for the discussion and introducing the speakers.Ìę

In his opening remarks, Dr. Amir Asif, Vice President of Research and Innovation at 91ŃÇÉ«, noted that AI remains a “key strategic area” for research at 91ŃÇÉ«. Emphasizing the need for an interdisciplinary approach, Dr. Asif also stated that exploring AI’s ethical and legal implications will require collaboration between researchers in the AI community, social sciences, and the humanities.

Indeed, “collaboration” was one common thread among all the speakers. In his first formal address since he took office, the Hon. David Piccini—Ontario Minister of the Environment, Conservation and Parks—turned not only to universities for ideas in using AI to improve the climate situation, but also to youth as part of the government’s broader environmental policy plan. The youth, he noted, must be “unapologetically engaged”. Given the , the Minister’s words are welcome if they signal any change.

The panel then proceeded to a discussion moderated by Dr. Aviv Gaon, professor at IDC Herzliya. In his introduction, Gaon brought attention to that outlines 17 internationally-agreed-upon sustainable development goals for 2030, spread across 169 targets. With respect to the environment, the study shows that AI’s potential to enable the environmental targets of these 17 goals outweighs its inhibitory effects.

The first panelist was Andrea Roszell, Director of Energy, Sustainability and Infrastructure at Guidehouse. Her discussion was centred on AI’s capabilities to increase efficiency in the energy and utility sector. In particular, she pointed to the “energy cloud”, a concept developed at Guidehouse that moves away from a “one-way flow” of power from energy centers to consumers, to a more networked, interconnected “multi-flow” dynamic. This requires an infrastructure—a neural grid—that utilizes artificial intelligence in technology, such as sensors software and monitoring systems, to create large “data sets” for utilities to access. Despite requiring increased energy consumption, Roszell stated that these data sets are a net benefit to the environment due to the new efficiency gained in management of greenhouse gases and predictive maintenance models that ultimately lead to a more sustainable and reliant energy infrastructure.

The second panelist was Dr. Audrey Lee, Senior Director of Energy Strategy at Microsoft. She started by pointing to Among other goals, Dr. Lee highlighted Microsoft’s plan to offset all of its electricity usage with renewable energy by 2025 and to be carbon negative by 2050. Lee noted, however, that the first step in achieving any such goal is to establish a proficient “measurement infrastructure” that can enable us to quantify our environmental footprint with sufficient precision—for example, data analytics that detail how and to what extent a particular utility uses electricity at each hour.

The panel then continued to its third speaker, Kapil Singhal, Co-Founder & CEO of Vyntelligence. At the very outset of his discussion, he too emphasized the need for collaboration. In particular, Singhal noted how Vyntelligence has made possible a new form collaboration between artificial intelligence and human brain power. Utilizing short videos of workflow in the field, artificial intelligence can augment workers’ awareness of a given project by revealing further areas of risk and benefit. This, when combined with human cognitive and decision-making power (which Singhal noted far exceeds what AI can learn), will yield more efficient outcomes. One such outcome is enhancing the infrastructure that allows for remote work (the importance of which is vividly felt in times of COVID-19), reducing thereby the carbon footprint of work-related travel.Ìę

Finally, the panel featured Neetika Sathe, Vice President of the GRE&T Centre at Alectra Inc. First, she noted that as more and more people gain access to the internet, global energy consumption is bound to increase. Thus, she emphasized the need for international collaboration beyond local efforts. She further mentioned that about half of the energy used at datacentres is used to cool their servers, which brings attention to the need for more efficient infrastructures.

In closing, it is important to address that, as the panelists mentioned, data centres account for only 1-2% of global energy consumption. However, as I mentioned in , AI’s “dirty footprint” is not confined to the energy it consumes, but extends to its ability to offer services for resource extraction which, for example, is enabled by the connection and collaboration between the tech and fossil fuel industries. Any meaningful policy directed at reducing AI’s negative environmental impacts must also account for this broader perspective.

A link to watch a recording of the event can be found on IP Osgoode's page.

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Artificial intelligence (AI) can create inherent benefits for all sectors, but governance of the technology is lagging, forcing industry experts and academics to confront the legal and ethical issues that this technology raises.

At Bracing for Impact: The Artificial Intelligence Challenge, a one-day conference hosted by IP Osgoode on Feb. 2, international speakers gathered to encourage the creation of a road map for the legal treatment of AI issues. Touching on cybersecurity, intellectual property and privacy, the point of the day was clear: AI is here and society must catch up.

Ryan Calo, an associate professor at the University of Washington School of Law, joined the conference by teleconference from the United States and addressed policy issues that can arise when AI is used. He highlighted implications for justice and equity as AI may not benefit all levels of society.

“We’re experimenting with artificial intelligence on populations that are the most vulnerable and have the fewest resources to seek redress,” he said, explaining that in some instances AI may not work well for people based on their race.

“There’s anecdotal evidence that people of colour, their hands are not always recognized by automated dryers or automated faucets because those faucets have been calibrated to white hands. Similarly, there’s evidence that, for example, people of Taiwanese ancestry are having an issue where facial recognition software meant to optimize cameras are not taking pictures or are warning that the subject of the picture actually has their eyes closed or is squinting. Because, again, the database upon which the software’s been trained contained few Taiwanese faces,” he added.

Calo noted that algorithms can reinforce bias, which can be particularly concerning if an AI is being used to determine the length of a prison sentence or being used by police in enforcement. He said this also leads to challenging questions about when AI is safe and can be certified.

“For example, if you’re going to be operated on by a surgeon, she’s got to go to medical school and she’s got to pass her board. But under development today in a number of labs are autonomous surgical units, which are amazing on one level because they allow for standardized and presumably, one day, safer surgery, or even surgery in places where a surgeon isn’t available to do the surgery. But at the same time, how do you go about establishing that the surgeon is adequate given that they’re not going to go to medical school and they’re not going to pass a board?” he said, asking what tests and standards are going to be used to vet AI when they replace a human in their work.

With AI being able to replace an increasing amount of human roles in the workforce, Calo noted that governments need to be alive to the issue of taxation and displacement of labour. He noted that the impact on income tax could be huge if in a short period of time a large amount of jobs are given to robots.

Calo said governments need to accrue expertise in technology in order to understand the impacts AI is having and create policy that will keep people safe.

“It’s very unlikely that we will come up with the wisest laws possible for infrastructure of AI in the absence of lawmakers and regulators and judges that have an inadequate mental model of the technology,” he said, adding that investing in research is key.

Society must make sure “to invest in basic, interdisciplinary research,” he said, “to not only further the state of AI, but also the state of social impact research about AI is critical. Thoughtful procurement — one of the issues we’re having today is people are buying AI enabled systems for use in places, like courts, without really understanding what they’re buying or understanding the consequences,” he explained.

Calo noted that regulation can go a long way in mediating issues surrounding AI, but an unusual paradox of wanting change, but insisting society remains the same is acting as a barrier.

“Artificial intelligence is going to remake every aspect of society, but there shouldn’t be any change somehow to law and legal institutions. That strikes me as deeply implausible. Either artificial intelligence is all hype or we’re going to need laws to address it. I think regulation at some level is inevitable. I think it’s premature today to top down regulate everything, but I think we should be watching for opportunities where there’s a gap between what the law assumes and what is happening on the ground in practice,” he said.

Maura Grossman, a research professor at the David R. Cheriton School of Computer Science at the University of Waterloo, like Calo, noted in her address to the conference that society needs to examine who benefits from the results of AI.

“I think we have to move away from the zero sum game and find ways to make this a win-win proposition for everybody,” she said.

Grossman was working at a large law firm in New 91ŃÇÉ« when she was faced with the challenge of going through millions of documents with only five lawyers to do document review.

“It occurred to me that technology is the problem. Technology needs to be the answer, so I started to go to computer science programs and started to learn about machine learning,” she said.

Grossman was at one of these conferences when she met Gordon Cormack, a researcher in information retrieval, and together they began a research study pitting lawyers against algorithms.

“Gordon and I took 896,000 documents that were part of the Enron dataset that was released during the course of that litigation and we took third year law students who had volunteered their time pro bono, and we took contract attorneys who volunteered their time, and then we took some of these supervised machine learning algorithms and we put them back to back on five requests for production. We said ‘find all of the documents that relate to these topics.’ And then we looked at who did better,” she explained.

The results fell staggeringly in favour of the algorithm side with the AI making fewer mistakes and being highly efficient. Grossman thought these results would lead the legal profession to use algorithms across the board, but she didn’t take into account the large amount of people who make money doing document review.

“We don’t forgive errors in algorithms and we don’t believe they can learn for some reason, even though we talked today about one of the scarier things about algorithms is that they can learn,” she said, noting that the legal profession is slow to adopt to technology that it doesn’t trust.

“Obviously we don’t want people to overuse algorithms and over-trust them when they shouldn’t, but they should use them when it’s logical,” she added.

Grossman used the example of air travel as a time when people rely on AI even when they doubt its safety.

“When people say to me ‘I could never use one of these algorithms to do document review. It’s too risky.’ And then I’ll say ‘do you get on an airplane?’ And they say ‘of course I do.’ Are you aware that there are four minutes of that flight that is flown by a human. The rest is flown by an algorithm. And almost all the accidents that occur, occur in those four minutes or in transition from the algorithm to the human,” she explained.

Grossman said in order for people to trust in AI they have to feel some sense of control and more peer-reviewed research is needed to keep the technology moving forward.

“We really need people to take the time to do the careful research. We need the funding of the research. I think that convinces people in the long run,” she said.

Amanda Jerome is a Digital Reporter for The Lawyer’s Daily

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Organized by IP Osgoode and its collaborators theÌęÌęand theÌę, this full day conference focused on AI innovation, legal issues, cybersecurity and ethical considerations.Ìę ÌęThe participants of the conference included leading researchers in AI, legal scholars, practitioners and industry experts from Canada and around the world.

The conference was generously supported by conference partners, 91ŃÇÉ« Centre for Public Policy & Law and The Zvi Meitar Institute at IDC Herzliya, and conference supporters, the Canadian Institute for Advanced Research (), , VPRI Office at 91ŃÇÉ« and The . The conference was also funded by a Social Sciences & Humanities Research Council Connection Grant.

To view the video and audio recordings, please visit the conference website, accessible .

To read the IPilogue’s commentary for each of the four panel discussions, please click on the relevant links below:

Panel 1 blog:Ìę

Panel 2 blog:Ìę

Panel 3 blog:Ìę

Panel 4 blog:Ìę

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, a conference organized by IP Osgoode earlier this year, included a panel entitled “AI & Industries” that covered a wide range of current issues as a result of AI technologies and anticipated other looming issues. The panelists shed light on what may be sensible to expect from AI governance in the near future and hinted at what may constitute the cornerstones for effective AI regulation.

Competent practice management

As I boiled down the main threads called forth by the panel, I realized they provided scope for an interesting case for AI regulation in light of a lawyer’s duty of , as per the Law Society of Ontario’s Rules of Professional Responsibility (“Rules”). Practice management by way of available systems, technologies, and other methods is one of the areas of competence where lawyers must meet minimum standards to ensure they serve clients well, timely, and at a reasonable cost (see, , and commentary ). These three mandates of competent practice management provide an initial framework for addressing the concerns raised by the panelists and gauging whether the deployment of AI meets minimum ethical standards in different circumstances.

Well-served society

To gauge the appropriateness of AI, one may need to make an initial assessment of how well the deployment of a specific technology serves society. Generally, the in favour of the machines, for the high efficiency and low likelihood of errors technology offers. Dr.Ìę, Chief Pediatrician at the Hospital for Sick Children, discussed the benign effects that flow from the synergyÌębetween artificial and human intelligence, while Prof. , Professor at the UC Berkeley School of Information, made a counterpoint by touching on problems related to inappropriate uses of AI.

Dr. Cohn noted that AI is a tool that allows professionals —in his particular case, physicians — to make better informed decisions. AI-enabled tools do not eliminate (medical) school training nor the need for a skill set based on first principles for their use (see, though, concerns with deskilling and ). AI has been crucial, Dr. Cohn added, for the development of medicine’s three main scopes: prediction, prevention, and precision. He remarked that AI-assisted clinical trials are cheaper, faster and more informative; yet, the doctor still bears the decision-making responsibility andÌęthe human element that allows for effective communication with patients. As to preventive medicine, Dr. Cohn remarked that AI data analytics tools permit physicians to quickly identify environmental risk factors and promptly propose actions in response. Finally, Dr. Cohn noted that the third scope of medicine — precision — has improved substantially with AI technologies, which are able to identify pattern deviations and that could take even the most experienced, senior professional .

Prof. Mulligan presented a counterpoint related to the inappropriate use of AI, such as when this unduly interferes with professional judgement. Two examples arise in the context of the and the healthcare industry. In both fields, on algorithms that are not always free of and may trigger . Prof. Mulligan argued, “error avoidance is an ethical imperative, both to maximize positive, short-term consequences and to ensure that, in the long run, informatics is not associated with error or carelessness, or the kind of cavalier stance sometimes associated with high-tech boosterism.” She added, citing K. W. Goodman, that the expansion of the field should be encouraged “”. Transparency and proper justification of decision-making by professionals that rely heavily on AI may provide a solution to address undue bias built into the algorithms.

Time is of the essence

, Professor at the University of Washington School of Law, raised the question of whether it is premature to think about regulation. Prof. Calo , while it may be premature to top-down regulate every aspect of life that AI touches upon, a sensible approach towards governance involves “watching for opportunities where there is a gap between what law assumes and what is happening on the ground in practice”.

In addition to confronting the legal and ethical social issues that may arise from the ever-broader use of AI, Prof. Calo noted that we should also be attentive to the issues of justice and equity that lurk in AI governance. There are concerns in our community respecting the inappropriate use of AI and, at a macro level, there is still some degree of uncertainty as to whether AI’s benefits and costs will be within reach of everyone equally.

Reasonable cost

Prof. , the Canada Research Chair in Ethics Law and Technology at the University of Ottawa, tackled the problem of costs associated with AI use. In his presentation, he warned “to accept that things will be more expensive because we will have things that we never had before is to support a neo-liberal fantasy”. As AI tends to outperform humans in virtually every field, and as we increasingly seek ultimate efficiency in service provision, Prof. Kerr noted that this ongoing search for ever-enhancing efficiency may result in elevated costs whenever AI is engaged.

Indeed, it may be hasty to assume that AI, and technology in general, will always but with no delay will become largely democratized. Although tech goods have become in the past decades, and the ’ are what have increased the availability of these goods in the market. What must not be underrated as a associated with AI is the to build, maintain, and monitor . Companies and other service providers across industries are likely to make poor financial decisions if they cut expenses in this area and disregard this risk factor in their business as they might eventually feel the backlash from the .

The monetary costs associated with and the are hardly overemphasized. As a result, cost regulation of AI may warrant increased security to stakeholders on a more regular basis, while also preventing AI from leveraging vulnerabilities in sensitive fields such as the healthcare industry.

Good governance and competent management of AI, as with the provision of legal services, may be tied to serving society well, timely, and at a reasonable cost. Hence, because time is of the essence, it flows logically that society will be underserved if the efforts to bring the rule of law into the technological world become deferred.

Bruna D. Kalinoski is a contributing editor for the IPilogue and holds an LLM from the Osgoode Professional Development Program at 91ŃÇÉ«.

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The Current Legal System on AI and Big Data

AI today creates and with minimal human input. This seems to create the expectation that AI machines will eventually reach fully decisions, in spite of the debate of the amount of time it will require. Yet, regardless the significant strides in the field, such as “AIVA”, the AI-powered , and the news that Saudi Arabia became the first state to grant to an AI robot, “Sophia”, IP law stands as a passive observer, with legislators hesitating to attribute authorship or inventorship, thus ownership, over AI-generated work.

As Osgoode Hall Law School PhD Candidate, , explained from a theoretical IP perspective, “there are three stages of development: computer-assisted, computer-generated, and AI works”. In computer-assisted works the computer is nothing more than a tool, like a pen. This was the conclusion in , which maintained that ascribing rights to a computer is as absurd as attributing authorship to a pen. But, as Gaon pointed out, even where there is strong evidence of minimal to zero human influence on the creative process, “courts will try to find some human ingenuity to establish authorship within the computer assisted work safe haven”, relying on the decision of the Alberta Court of Appeal in which addressed the issue of copyright protection of collection and computer assessment of seismic data.

The issue of IP rights over AI-generated work seems intertwined with ownership over data and databases, since AI algorithms employ big data. While in the United States (US) raw data and databases are not ordinarily copyright-protected, , Director at Zvi Meitar Institute of IDC Herzliya, highlighted the fact that, if a database is uploaded online bearing digital rights management protection, said database along with its underlying data is deemed copyright-protected under the . Raw data can also be protected as trade secrets or under . Conversely, accessing password-protected online data is considered (prohibited) unauthorized access, thus a violation of the US Criminal Act. That being said, GreenbaumÌę argued that the real value is not in the data but in the analytics, providing as an example Celera Genomics, which gave away genomic data that cost to sequence.

Commercial Practices on Big Data and AI-generated work

Given that under no regime is AI considered an author or inventor, an invention is either owned by people or falls under the public domain. That is why establishing a high degree of human influence on an invention is important, a point underscored by , Associate at McCarthy TĂ©trault LLP. From a legal perspective Piovesan also prompted AI stakeholders to illustrate the of the system in order to strengthen their claims for IP rights before courts, for instance elucidate whether the system is a product or a service, if it is a tool or an agent, and whether it is being controlled by the programmer or the user.

On the other hand, although the law is not settled on IP protection of big data, the latter is being commercially exploited. , Partner at Norton Rose Fulbright LLP, illustrated some practical aspects of commercializing big data: industry trends in licensing agreements include between “owners” of a (unique) data set and those who have the AI tools to process it. Additional value can be generated by “clearing data in a bad state”. Collaboration agreements, as Medeiros stressed, must draw a clear line on the expectation of who owns what. Data providers will aim at controlling access and retrieve it when the agreement ends, while AI providers will try to own or control data aggregation from different data sources. Such expectations should be clear in the pertinent contract, even though, as Medeiros emphasized, the issue of IP protection on the ownership of the transformation of data aggregates has not yet legally settled. Given the above, co-ownership between data and AI tech providers would not be recommended by Medeiros, who introduced sublicensing data for commercialization revenue share of the ultimate refined AI tool, as a trend.

Addressing the legislative gap

While AI is growing exponentially and robots are developing , regulators can address the legislative gap, for example, by framing AI as an employee, which would require minimal amendments in Copyright Law, assuming it encapsulates vicarious liability provisions. Nevertheless, as Piovesan argued, gaps could also be overcome by systems, as is being considered by legislators in Estonia.

Alternatively, Gaon proposed that a model for computer-generated work could prevent unlawful exploitation of AI works through promoting integration of knowledge and recreation of works. According to this model, IP rights could be divided between the programmer and the AI computer which, practically, means that computer rights would become available to the public for a short time or profits would be invested for the public good. Naturally, this model requires development of a test in order to establish human impact on creation, as Gaon noted.

Finally, , Senior Lecturer at UNSW Sydney Law, foresees that the AI challenge will be resolved by espousing the same principles that law evolved on, since the metaphysics have not changed. However, she argued that revolutionary change requires that we should have put that to place, as AI is already upon us. Similarly, , Associate Professor at Osgoode Hall Law School, echoed George’s views and positedÌę that we ought not to think about expanding the confines of IP without revisiting the normative justifications and rationales on which existing IP rights are premised. Yet, Piovesan noted that some of the fundamental principles of IP law are being challenged due to AI’s very nature, referring to fact that AI cannot be incentivized to innovate through recognition and reward. Moreover, although we have yet to reach “”, AI’s developing “emotional nature is pushing the boundaries of how we conceptualize and identify humans”.

All in all, the longer we delay addressing the issue of AI rights, the more radical of a reform will be required as the existing legal “boxes” may not be sufficient to fit the growing capabilities of AI, being the least of the recognitions it can attract. On the other hand, it seems contentious to approach this issue based on what AI can do; focus should be placed on what AI is, especially in light of it eventually reaching fully autonomous decisions. AI remains a tool, even if it ultimately behaves like humans. It is therefore imperative that, as a tool, AI continue to be under control, something which may be disincentivized if AI-generated work were to fall under the public domain.

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Yonida Koukio is an IPilogue Editor and an LL.M. Candidate at Osgoode Hall Law School.Ìę

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First Off, what really is AI?

Traditionally, computers were thought to be creatures of instructions. However, over six decades ago, the umbrella term, Artificial Intelligence, was coined to refer to a computer’s ability to make decisions without direct human intervention. According to Arthur Samuels, it is “a field of study that gives computers the ability to learn without being explicitly programmed.” that allow for human-like responses to problems by identifying patterns from enormous pools of data. From , , , to , AI is literally in our faces and has the potential to infiltrate nearly every aspect of our lives.

AI Poses Both a Risk and Opportunity

Cyberspace, despite its many advantages, continues to be exploited. Since 2013, . AI provides a unique opportunity to bolster cybersecurity solutions by utilizing predictive analysis capabilities. Almost and it appears cybercriminals are always a step ahead because .

AI is a game changer, which can allow for a more proactive and dynamic approach to cybersecurity. For example, deep learning technologies can , run numerous simulations, and predict potential attacks and respond accordingly.

However, AI comes with a number of risks, such as threat agents using AI to develop automated attacks that learn and adapt to vulnerable systems in real time. AI models also thrive on data, so bias or false positives could adversely affect decisions or actions taken by the algorithm. Issues of accountability and even tort liability may arise if the AI model goes rogue and does what it was not programmed to do.

Managing Director and CEO of ABCLive Corporation, , expressed that the capacity of AI to learn and evolve will undoubtedly exceed human capacity. For example, in 2016, where its sutures were found to be superior and done with more precision. As astounding as this may be, there are still risks to consider. If this AI bot was somehow compromised and succumbed to a cybercriminal’s ransomware attack, human lives could potentially be at risk.

Privacy vs. Security – Do We Tradeoff or Can We Have Both?Ìę

So, how can these risks be mitigated? It appears that efforts to do so could impact privacy rights or even the fabric of a nation’s security.

According to Benjamin Franklin, Jurisdictions around the world continue to struggle with this concept, especially in light of autonomous AI weapons and other national security concerns. , special counsel, Yigal Armon & Co. and former Israeli legal advisor for the National Security Council, argued that an invasion of privacy might be a necessary trade off in some instances, . Keidar posited that while an individual’s privacy rights should be protected, the concept of freedom also extends to border security issues and other national security concerns. Clearly, this topic is a mammoth task for governments, so a natural corollary is that in order to preserve the security architecture of a nation, tools need to be developed to allow for a certain level of security. Consequently, in an attempt to keep citizens safe, this invariably might encroach on individuals’ privacy rights. Conversely, privacy expert maintains that it is quite possible to have both privacy and security. Cavoukian argues that over one’s data and this should be reflected in free and democratic societies. Her proposed approach seeks to change the paradigm from flawed ‘zero-sum’ models to ‘positive-sum’ models. Privacy and security would no longer be competing interests because measures safeguarding privacy would be proactively embedded into technological operations and security considerations. Cavoukian predicts that the implementation of the (GDPR) in May 2018 will replace current privacy laws in all European Union member countries, making privacy the default. As such, the use of data will be ‘user-centric’ and only used for the purposes it was collected for. Entities who do not abide by the GDPR could face fines of up to 4% of their global revenue. Cavoukian’s proposed would allow for transparency and oversight of algorithms with high levels of accountability, which could help to facilitate ethical algorithmic designs and data symmetry.

Is Legislation the Answer?

Technology enthusiasts around the world, including Stephen Hawking and Elon Musk, argue that AI is an existential threat to humanity and are calling for nations to . Indeed, there are concerns about AI’s impact on weapons and privacy rights, but whether legislation can restrain this ‘monstrous beast’ is moot. Not only are there jurisdictional issues as it relates to regulating AI in a borderless Cyberworld, but AI is evolving and do not have a good of .

Even if policy makers manage to develop a legislative framework for AI, that is only one aspect of this labyrinthine technology. Other issues to grapple with include potential job losses in those roles at risk of automation. For example, a revealed that due to AI bots taking over assembly line work traditionally done by humans. Ìędue to automation as well. Even the legal fraternity is not immune, especially with talks of possibly replacing lower-level legal assignments carried out by articling students or junior lawyers. Governments would also need to consider wider economic implications such as the decline in tax dollars received from those jobs.

AI could boost by 2035, so policy makers should be cautious in over regulating this invaluable resource that could drive innovation. Over regulation could possibly stifle growth in AI by making it a less attractive field for investors. For example, a potential backlash could be Google and other tech giants such as no longer investing in Canada.

Perhaps more work needs to be done in developing ethical oversight of AI; particularly teaching AI the unique aspects of human values like privacy and freedom. who have been for over 30 years, so their expertise in determining the extent to which these principles can be reflected in AI technologies could then inform legislation. Canada would then need to focus its policy lens on training and research, thus building and sustaining Canada’s AI ecosystem.

Andrae Campbell is an IPilogue Editor and an LLM Candidate at Osgoode Hall Law School.

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The conference brought together experts from a broad range of disciplines to discuss artificial intelligence (AI) innovation and the impact machine learning will have on our social, moral, and legal norms. Throughout the day, tough questions were asked and critical issues about commercialization, cybersecurity, and the application of AI for social good were discussed. In my blog, I will share a piece of this journey with you and focus on the last panel entitled, “AI for Social Good.”

AI in the Public Sector & Biases

Our journey into AI started with Dr.Ìę’s presentation on the uses of AI in the public sector, the power of various AI applications to promote equity, and the biases we need to be aware of in designing algorithms. Dr. Nonnecke brought the audience’s attention to the rapid growth of metropolitan centers. According to , 30 years from now, cities are going to have a huge influx of population with 66% of the world’s population living in cities by 2050 compared to 54% in 2014. This will disrupt the status quo and dramatically change how our cities function, explained Dr. Nonnecke. In anticipation of this rapid growth, the public sector is already looking at cognitive technologies that could eventually revolutionize every facet of public services and government operations including oversight, law enforcement, labour, and human rights.

Dr. Nonnecke acknowledged AI’s promise to promote efficiency, effectiveness, and equity. AI can be used, for example, to locate human trafficking hotspots, mitigate biases in job application processes, and detect discrimination in law enforcement. Although AI has the power to promote equity, this power is not an inherent one. AI is as prone to bias as the humans who design its algorithms. Given that algorithms and machine learning (ML) are increasingly used to make decisions, developers need to be aware of their human fallacies that can easily make their way into ML in the form of bias in data and prediction.

Dr. Nonnecke also stressed the importance of ensuring inclusiveness and equity in all stages of AI development. She cautioned that, if we want a good design and an unbiased outcome, we need non-heterogeneous groups, not only in the purview of technical ability, but also in every interdisciplinary team involved in the development of AI from engineers to legal scholars.

Designing for the Average

Big Data inherits methods from quantitative research where outliers (or “noise”) in data is eliminated to find dominant patterns and generalizable findings. In effect, this method “normalizes” the data that is used to recognize speech, faces, illnesses, or to predict loan and credit worthiness, academic potential, and future employment performance.

As Prof.Ìę pointed out, just like , AI designs could easily fail to consider people who do not fit the “norm” and when AI applications are offered to everyone but are, at the same time, designed with the average person in mind, “normalization” of data becomes a large issue.Ìę Prof.Ìę pointed out that we cannot rely on predictive models or be overconfident in statistical tests where the minority can eventually be discarded as “noise in data.” Rather, we need to recognize diversity and rethink our methodologies having regard to the individuals at the margins. Although the audience was left with questions on how to tackle the potential biases of AI design towards the “average person,” the panelists drew everyone’s attention to the scary fact that as AI permeates our daily lives, the effect of serving the “average person” will lead to further marginalization and widening disparity between those who fit the norm and those who do not in one way or another.

Autonomous Cars for the Unreasonable Person

Traffic, congestion, and parking – situations that will make any driver not want to drive - but what if you could sit back and read a newspaper on your way to work in the comfort of your own car and not have to deal with all that? Prof. , a proponent of autonomous cars, argued that we need to get rid of regular cars.Ìę He argued that despite our (over)confidence in our driving ability, it is difficult to find a “reasonable person” on the road.Ìę The effect of “” allows drivers to feel anonymous and makes them feel less accountable for their risky behaviours behind the wheel.Ìę Citing the high number of fatalities by car accidents everyday around the world, the economical costs of keeping a car that we only use for 10% of the day, the amount of space wasted on parking (e.g. if we get rid of cars in the US we will avoid using a territory that is the size of Sri Lanka just for parking cars), etc., Prof. Seidman argued that it does not make much economic sense to keep regular cars and if autonomous cars can alleviate some of these burdens even slightly, we will see a huge economic improvement.

However, the promise of autonomous cars is tempered by a caution about some of the flaws of the technology as it currently stands. For example, Prof. Treviranus explained that in simulations involving autonomous cars, a pedestrian propelling backwards due to her disability is hit by the autonomous car because the technology failed to recognize the “out of the norm” movement.

Overcoming Algorithm Aversion

While some of the earlier panelists voiced their concern about the over-reliance on algorithms, Prof. argued that the problem is an under-reliance on algorithms.Ìę Prof. Grossman states that and hold algorithms to a much higher standard. She voiced her concerns that we will not reap the tremendous benefits of AI innovation because it is hard to get people to rely on algorithms even though one of AI’s key attributes is its ability to learn.Ìę Given that we can rely onÌę lawyers, doctors, and pilots with our lives, how can we justify our skepticism towards using algorithms that can be more accurate than humans? If there is even a chance to reduce the hefty legal costs and improve access to justice, then why are we not relying on algorithms more often in the legal system?Ìę Prof. Grossman stated that in certain low risk situations where using algorithms is the better and more logical alternative we should be using algorithms.Ìę So how do we alleviate this aversion to using algorithms? The research shows that to get people over this hump in using algorithms we may need to sacrifice some of the efficacy of algorithms and give people back some level of control.Ìę Furthermore, it is critically important to have peer-reviewed research and scholarship on algorithms in order to give it credibility in the long run.Ìę ÌęIn conclusion, Prof. Grossman suggested that we need to look at the psychological, social and economical incentives, and move away from the zero sum game and find ways to make this aÌę win-win proposition for everyone in order to reap the benefits of AI.

After the closing remarks of the conference were delivered, attendees and panelists engaged in further discussions at a cocktail reception. By the end of the day-long conference, I believe we were all in agreement that algorithms make mistakes, just like humans. More importantly, the conference was a call for our nation to invest in AI research and uncover the key elements to sparking the next AI innovation wave and better understand the impact of human cognitive bias on AI.

Ekin Ober is an IPilogue Editor and a JD/MBA candidate at Osgoode Hall Law School and the Schulich School of Business.

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The growing use of artificial intelligence (AI) raises complex ethical and legal concerns that will be examined in “Bracing for Impact: The Artificial Intelligence Challenge,” a one-day international conference on Friday, Feb. 2, organized by IP Osgoode, Osgoode Hall Law School of 91ŃÇɫ’s intellectual property law and technology program.

The conference, which takes place at the Law Society of Ontario, Donald Lamont Centre, 130 Queen St. W., Toronto, from 8:45am to 4:45pm, will feature a group of internationally renowned AI experts who will discuss some of the fundamental questions that arise when machines start to think for themselves.

The risks and opportunities that AI presents for ethics and public policy, intellectual property and commercialization, cybersecurity, international risks, and social good are among the topics that conference panelists from government, industry and academia will address.

“The conference is designed to bridge the gap between different disciplines and fields and drive the conversation forward about how governments should prepare for and react to the impacts that AI will have on Canadian society,” said Professor Giuseppina D’Agostino, founder and director of IP Osgoode. “Canadian governments must move quickly in order to set out a vision for the future of AI that will position this country as a world leader and destination of choice for companies looking to invest in artificial intelligence and innovation.”

D’Agostino noted the Bracing for Impact conference is aligned with the Canadian federal government’s commitment to fund “a Pan-Canadian Artificial Intelligence Strategy for research and talent” that will cement the country’s position as a world leader in AI. The strategy, announced in the 2017 federal budget, will serve to attract and retain top academic talent in Canada, increase the number of post-graduate trainees and researchers studying artificial intelligence, and promote collaboration between Canada’s main centres of expertise in Montreal, Toronto-Waterloo and Edmonton. The program will be administered through CIFAR, the Canadian Institute for Advanced Research. The Ontario government has also followed suit to invest in AI and position the province as a leader in this emerging space.

Osgoode experts Professor D’Agostino, Professor Carys Craig, Professor Emeritus Jean–Gabriel Castel, Visiting Professor David Lepofsky, Adjunct Professor Bob Tarantino and PhD candidates Aviv Gaon and Ian Stedman will be joined at the Bracing for Impact conference by Ian Kerr (U Ottawa), Ryan Calo (U Washington), Ronald Cohn, MD, (SickKids), Deirdre K. Mulligan (UC Berkeley), Maya Medeiros (Norton Rose Fulbright,LLP), Dov Greenbaum (IDC Herzliya), Carole Piovesan (McCarthy TĂ©trault LLP), Alexandra George (UNSW Sydney), Roy Keidar (Yigal Arnon & Co., former Israeli legal advisor for the National Security Council), Ann Cavoukian (Ryerson U), Victor Garcia (ABC Live Corporation), Matthew Castel (Orion Legal Group and Logos LP), Jutta Treviranus (OCAD U), Brandie M. Nonnecke (UC Berkeley) and Guy Seidman (IDC Herzliya).

The conference organizing committee acknowledges the sponsorship of conference partners, 91ŃÇÉ« Centre for Public Policy & Law and The Zvi Meitar Institute at IDC Herzliya, and conference supporters, the Canadian Institute for Advanced Research (CIFAR), McCarthy TĂ©trault LLP, VPRI Office at 91ŃÇÉ« and The Lassonde School of Engineering. The conference is also funded by a Social Sciences & Humanities Research Council Connection Grant.

For more information about “Bracing for Impact: The Artificial Intelligence Challenge,” including the full agenda, visit the conference .

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