Turning Standards into Rules — Part 3: Behavioral Control Factors in Employee vs. Independent Contractor Decisions

(2018) 242 DTR 15 (Bloomberg BNA)

3 Pages Posted: 22 May 2019

See all articles by Benjamin Alarie

Benjamin Alarie

University of Toronto - Faculty of Law; Vector Institute for Artificial Intelligence

Date Written: December 17, 2018

Abstract

With the growth of the gig economy, the employee / independent contractor distinction has received renewed attention in both the media and the courts. While rulings on the status of gig economy workers like Razak v. Uber Techs. Inc. and Lawson v. Grubhub Inc. have focused on the issue of employment status in particular states rather than on federal tax, these decisions necessarily have tax implications for the workers and hirers involved. The consequences of an incorrect classification can be costly, leaving the taxpayer liable for deficiencies and additional penalties — not to mention the cost of fighting the Internal Revenue Service in court. Recent advances in AI make it possible to answer these kinds of characterization questions with new levels of precision. As we saw in the previous articles in this series, machine learning algorithms can be trained on data extracted from existing cases in order to make predictions about how a court might rule on a new scenario. We’ve seen how accurate these algorithms can be when applied to a financial question like debt vs. equity, and this article explains that machine learning works equally well when applied to the more tangible question of whether an employment relationship exists for tax purposes.

Keywords: machine-learning, worker classification, characterization, gig economy

JEL Classification: H2, H20, H29

Suggested Citation

Alarie, Benjamin, Turning Standards into Rules — Part 3: Behavioral Control Factors in Employee vs. Independent Contractor Decisions (December 17, 2018). (2018) 242 DTR 15 (Bloomberg BNA). Available at SSRN: https://ssrn.com/abstract=3374051

Benjamin Alarie (Contact Author)

University of Toronto - Faculty of Law ( email )

Jackman Law Building
78 Queen's Park
Toronto, Ontario M5S 2C5
Canada
416-946-8205 (Phone)
416-978-7899 (Fax)

HOME PAGE: http://www.benjaminalarie.com

Vector Institute for Artificial Intelligence ( email )

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