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Not sure how to handle upcoming workplace impacts from legalized marijuana use? Ask an artificially intelligent legal tool.

By Benjamin Alarie

Not so long ago, the legalization of recreational marijuana and the practical application of artificial intelligence in human resources seemed like remote possibilities. Yet, as far out as they may seem, these developments are a reality and their impacts on the workplace are imminent.

This summer, recreational marijuana will be legalized in Canada – and there remain many lingering questions about its consequences for workplaces. HR professionals are particularly curious since they will inevitably be confronted with the challenging question of what constitutes legally justified testing for employee cannabis use.

To answer this question, and others like it, employers and employment lawyers are turning to a widely discussed and rapidly growing technology: artificial intelligence (AI).

AI’s introduction as a practical and reliable tool for professionals couldn’t have come at a better time. Given the prospect of increased employee cannabis use, HR teams around the country are actively laying the groundwork for the drug testing policies they’ll be introducing or revamping to ensure workplace safety.

Uncharted territory

In the coming months, many HR professionals will reach out to legal counsel to tackle questions such as whether or not prospective employees can be screened for drug use and how to fairly and legally conduct employee drug testing (randomly, targeted, post-incident, etc.); some will attempt to find the answers on their own. However, one notable challenge stands in the way: there isn’t yet a statutory regime that provides a clear and fixed set of rules that outline when employers can mandate drug testing for employees.

Until now, judges and adjudicators have relied heavily on the common law; that is, on decisions reached by the courts in similar cases in the past. There are hundreds of past decisions addressing a multitude of nuanced drug testing situations.

When trying to determine how a particular scenario should be handled, manually reading through these hundreds of cases is cumbersome and resource-intensive. What’s more, even when extensive research is completed, it can be difficult to obtain a definitive answer that takes into account all relevant factors from all relevant cases.

For the first time, software is now available that quickly, precisely and comprehensively analyzes the findings of hundreds of relevant decisions to make predictions about how these relevant factors will impact new cases.

A balancing act

At the centre of most cases stand two opposing interests that HR professionals are intimately familiar with: privacy and safety. Many employees feel strongly that what they do outside of work is a personal matter that shouldn’t be subjected to scrutiny by their employer. Drug testing can be invasive, anxiety-provoking and time-consuming, and it can undermine the trust that is crucial to maintaining healthy employer-employee relationships. HR departments, however, have a responsibility to ensure that the workplace is a secure, harm-free environment. If employees know that they will be subjected to drug testing, it may dissuade them from using marijuana at work, fostering a safer workplace for all.

Judges and adjudicators consider a range of relevant factors when assessing the privacy and safety tradeoff. For instance, was the employee in a role where her safety or the safety of others was a primary concern? Did the employee show signs of impairment (abnormal speech, red eyes, odour, etc.)? Was there physical evidence of the employee using drugs at work? What was the extent of the damage to others or to property, if any? Were there plausible alternative explanations for the incident? Did the employer explicitly consider the worker’s privacy interests before requiring a drug test? Was the testing random or targeted based on other factors?

Data as fuel

The multitude of cases and the factors addressed in each written decision generates a trail of valuable data, which lawyers use to identify patterns in past case outcomes. These breadcrumbs are also fundamental inputs that AI systems use to provide tailored insights about how decision-makers have weighed various factors. For the first time, software is now available that quickly, precisely and comprehensively analyzes the findings of hundreds of relevant decisions to make predictions about how these relevant factors will impact new cases.

Science behind the software

AI sometimes connotes an image of a confusing black box of ones and zeros. In reality, newly available software for drug testing is relatively transparent and user-friendly. It works by first asking for a series of inputs through a short, plain language user questionnaire suitable for lawyers and HR professionals alike. It collects information about the user’s context such as the applicable province, the past disciplinary record of the employee, the degree to which the employee cooperated with the employer, the nature of the employee’s work responsibilities, the specific indicators of impairment, any alternative explanations and more.

With inputs unique to the situation at hand, the software goes to work, instantly comparing the information provided by the user to all relevant past cases. The result is a report that conveys how likely it is that a drug test would be found to be legally permissible or impermissible, along with a corresponding confidence level (expressed as a percentage). The software also generates a succinct and easy-to-understand explanation for the predicted outcome and a list of past decisions that are most similar to the circumstances inputted. The user can try multiple scenarios and see how the predicted outcome would change given different assumptions. When tested against cases that the system has never seen before, an AI-based prediction system is able to achieve 90 per cent or greater accuracy. The most advanced systems are also updated with new decisions as they are published, enabling the system to improve its predictions and provide up-to-date outputs.

What lies ahead

In the same way that software programs have improved internal processes like payroll and tracking employee timesheets, AI-based tools represent a generational leap forward for tackling legal employment issues. Indeed, software powered by AI is quickly becoming one of the most valuable resources for HR professionals and employment lawyers, making their work smarter, faster and more thorough. Similar to the introduction of workplace computers in the 1980s, the newness of AI has created a combination of fear and excitement. Although history does not repeat itself perfectly, it does rhyme. It is only a matter of time before it will be hard to imagine working as an HR professional without the help of AI software. 

Benjamin Alarie is CEO of Blue J Legal and the Osler Chair in Business Law at the University of Toronto.

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