Faculty of Law

Permanent URI for this communityhttps://hdl.handle.net/1807/75711

Established in 1887, the Faculty of Law is one of the oldest professional faculties at the University of Toronto, with a long and illustrious history.

Today, it is one of the world's great law schools, a dynamic academic and social community with more than 50 full-time faculty members and 15-25 distinguished short-term visiting professors from the world's leading law schools, as well as 600 undergraduate and graduate students.

This collection showcases some of the research and scholarly work by its faculty members.

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Browsing Faculty of Law by Author "Aidid, Abdi"

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    Predicting Economic Substance Cases with Machine Learning
    (Wolters Kluwer, 2020-05-20) Alarie, Benjamin; Aidid, Abdi
    The economic substance doctrine is intended to prevent taxpayers from engaging in transactions that have no substantive purpose other than to obtain a tax benefit. As one of the judicially-created substance-over-form doctrines, its meaning is almost entirely derived from the series of court decisions articulating its application. Unfortunately, these cases have not always been clear; scholars have long noted that courts have applied the doctrine inconsistently, judges have criticized it for being tantamount to a “smell test” and even derided it as the government’s “trump card.” Recent scholarship suggests that the doctrine’s codification in 2010 in Section 7701(o) of the Internal Revenue Code might have contributed to even more confusion. For practitioners, this creates a major challenge. How do you advise clients as to whether their transaction has economic substance when the case law is “confusing and conflicting,” particularly when you know that clients expect confident, near-certain advice? Fortunately, advances in computing power offer an opportunity to cut through the morass and more fully understand the law. Alongside colleagues at the University of Toronto and Blue J Legal, we have developed artificial intelligence systems that identify and assess patterns in tax cases. Using techniques in supervised machine learning, we analyze historical case law to surface hidden insights and predict how future courts will respond to new tax situations. Sophisticated tax practitioners can make use of these insights to provide better advice, structure tax-optimal transactions, respond effectively to tax authorities and resolve disputes more efficiently. In this note, we take a brief look at how machine learning can improve our understanding of the economic substance doctrine. In Part I, we discuss the importance of prediction in tax practice, noting that the emergence of computational technologies has made the age-old desire to predict legal outcomes considerably easier. In Part II, we discuss the economic substance doctrine in detail and identify areas of confusion that cannot be resolved by conventional legal research methods. In Part III, we apply machine learning to two recent Tax Court cases – Cuthbertson (2020) and MCM (2019) – to demonstrate how algorithms can correctly predict the case outcomes and give practitioners opportunities to refine their understanding and ultimately provide better tax advice.
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    Using AI to Characterize Financing Between Related Parties
    (Tax Analysts, 2020-05-18) Alarie, Benjamin; Xue Griffin, Bettina; Aidid, Abdi
    The debt-versus-equity question is largely relegated to the common law. The central inquiry is the “extent to which the transaction complies with arm’s length standards and normal business practice.” The courts have established a list of recurring factors to determine whether a transfer should be characterized as a debt interest between the contributing party (the lender) and the receiving business (the borrower) or whether the transfer resulted in an equity interest between the contributing party (the holder) and the receiving corporation (the issuer). We show how it is possible to identify patterns within a data set of past debt-versus-equity cases by applying machine learning algorithms. By comparing two recent decisions involving related parties, one with a debt outcome and the other an equity outcome, we illustrate the relative significance of various factors as they pertain to related parties. The presence of formal indicia of debt such as a loan document is not significant in relation to other factors that address the substance of the transaction as opposed to the way it was papered. The existence of enforcement rights is slightly more significant, but how the parties behaved in terms of any repayment is the most significant out of the three. Machine-learning-powered systems can allow lawyers to make more confident and efficient predictions based on all the relevant information. And while there remains some anxiety about the disruptive potential of artificial intelligence for the legal field, it is important to recognize that machine learning is not a replacement for the judgment of human lawyers. Instead, it is a powerful new tool to augment their professional knowledge and instincts.