Amendments to Ontario’s ESA: Misclassification of Employees Riskier Than Ever for Hirers

The consequences of misclassifying employees as independent contractors have always been serious. Just a few examples of the risks hirers face if they make this make include liability for overtime pay, lawsuits arising from vicarious liability or lack of reasonable notice when ending the working relationship and damage to reputation. Workers risk being potentially deprived of their rights under relevant worker legislation and shouldering responsibilities and risks that they might not be best suited to bear.

But now the negative consequences of misclassification for hirers are far greater. Amendments to Ontario’s Employment Standards Act, 2000 (“ESA”) came into force on November 27, 2017. These amendments include criminal prosecution for hirers who misclassify their workers under certain circumstances. In certain proceedings or investigations by employment standards authorities, there is a rebuttable presumption that a worker is an employee and the hirer has the burden of proving otherwise (see newly added section 5.1 to the ESA).

Hirers therefore need to review their practices and processes to ensure that they are meeting their responsibilities to classify workers correctly. Employment Foresight ’s Worker Classifier provides an accurate and easy to use tool for checking a worker’s classification, decreasing the likelihood of making a mistake and increasing peace of mind for both hirers and workers.

How Can Employment Foresight Help?

Employment Foresight (“EF”) uses machine learning to predict worker classification. EF’s Worker Classifier analyzes a hirer-worker relationship using over 25 different factors and compares the relationship to those in the hundreds of litigated cases that address this legal issue. To determine how a court, board, or tribunal would likely resolve your case, you need only fill out a short, easy-to-use questionnaire.

Employment Foresight is Accurate, Easy to Use—and Insightful!

EF’s Worker Classifier provides the user with the likely outcome and a confidence rating. EF ties these together with an explanation into the likely outcome that gives the user additional insight, indicating how the different relevant factors affected them.

In a recent test, we used EF’s Worker Classifier to predict the likely outcome in 40 recent cases that the EF program had not seen before. These cases were from 2016 to 2017 and not yet in EF’s database (see the table at the end of this post).

Employment Foresight correctly predicted the correct outcome in 36 of the 40 cases – an accuracy level of 90%. These included three cases where the worker was found to be a Dependent Contractor.

Even more impressive was how confident the EF algorithm was in its predictions. In 31 of the 36 correctly predicted cases, our program was at least 80% confident in its result. In 25 of those, EF was over 90% confident.

There were four cases where our algorithm predicted the incorrect outcome. But, in doing so, our algorithm identified these as borderline fact patterns. EF’s confidence rating for these four cases suggested that these were close cases that required closer examination by the user.

Key Takeaway

Changes to Ontario’s employment standards legislation emphasize that the proper classification of workers is more important than ever and hirers bear the responsibility for ensuring this is done. EF’s Worker Classifier is an accurate, easy to use and insightful tool that can help hirers meet their responsibilities and give both hirers and workers more clarity as to the nature of their relationship. Try it here.