Benjamin Alarie (Osler Chair in Business Law, University of Toronto; CEO, Blue J Legal) & Kathrin Gardhouse (Legal Research Associate, Blue J Legal), Predicting Worker Classification in the Gig Economy, 173 Tax Notes Fed. 1733 (Dec. 20, 2021):
In this article, Alarie and Gardhouse examine the classification of workers in the gig economy and use machine-learning models to evaluate the legal factors that determine their categorization as employees or independent contractors for federal income tax purposes.
Machine-learning models can provide invaluable insight for hiring entities in positions similar to those faced by Uber and other gig economy service providers. Our analysis has revealed that the proper worker classification cannot be undertaken for an entire workforce at once, absent legislation that determines the question one way or the other. Instead, the most accurate approach is likely to be one based on a fact-based inquiry that is tailored to the working terms and conditions faced by different kinds of workers (for example, those that work part time, for the competition, on their own, or via assistants, etc.) Equipped with a predictive machine-learning model such as the one we used for our scenario testing, the task of gauging the risk of misclassifying workers can be made simpler and faster, leading to better-informed risk-taking and more effective lobbying, risk mitigation, and tax and employment law litigation strategies.
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