Anita GogiaĚý is a IPilogue Writer and a 2L JD Candidate at Osgoode Hall Law School.

During the Refugee Law Lab’s online seminar in September, students learned about how primary law can be used as data to increase its machine readability. Sarah Sutherland, the president and CEO of the Canadian Legal Information Institute (CanLII), discussed the role of technological solutions in legal work.

Sutherland Ěýfirst addressed how the structure of primary law affects its usage as data. Primary law is recorded and published in a way that can be referred to in ensuring proper process and governance. Legal data is “linguistic, narrational, situational, contextual, ambiguous, and process-dependant.” It is complex, just as human relationships are. When case-related documents are converted to legal data, it is semi-structured. While section headings are visible, it’s possible that the content within them is not marked or tagged in way that makes it efficient for document creation. This means the machine-readability is weak, as the content in the underlying document cannot be analyzed, requiring manual tagging.

Sutherland used Amazon to exemplify the impact when analyzing large amounts of data. Amazon, records millions of interactions and, through that data, they can extrapolate and predict what the user may be interested in purchasing. In some instances, Amazon can even prepare shipments before customers make purchases because of their forecasting accuracy. On the other hand, legal data lacks “yes/no” binary data and is incompatible with statistical analysis, despite employing large data sets. Specifically, in reference to legal data, “statistical methods may not have enough data points to give reliable results,” while “machine learning works well, and natural language processing techniques are more helpful.”

Statistical analysis requires a representative random sample — a criterion impossible for legal data to meet, as case law itself is non-random. Sutherland noted such arbitrariness existed at any level. For instance, “litigants are biased towards people who have money and high conflict complex problems, cases are selected by judges for which they will write and publish decisions, and matters heard by appellate courts are selected,” Overall, people who have legal problems are not random.

Language, specifically unintentional and intentional imprecision, is yet another reason it can be inefficient to use legal data. There is unintentional imprecision in language because of ambiguities in human syntax, as well as a lack of definition and/or precision about concepts. There is also intentional imprecision because not all situations can be anticipated and because of subjective human interpretations. For example, the legislature is mindful of the fact that if the drafted statute does not anticipate or is unclear about a specific set of facts, it will go to the courts who will apply their tools of statutory interpretation. Sutherland suggested that we should approach data the same way we approach creating statute — by accounting for imprecision.

Sutherland also discussed that machine-readable law can solve such problems through tagging and coding that makes the law explicit – this is referred to as “law as code.” Publishing law in such a way makes it widely usable across different applications that increase efficiency within the legal system. How can this be done? It was suggested that the law could be expressed in a literal way —law as code— and then in a human way. Rather than converting law expressed in human language to code, computer code would be converted to human language. Ěý

While it is unclear whether this would allow for more functionality with ambiguity in the law as law makers would require sophisticated levels of technical and computing skills to make it possible, there is potential to make the legal system more productive.

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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.Ěý

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.Ěý

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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