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
Date Written: December 17, 2018
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: Suggested Citation