legal tech machine learning employment law employment foresight

Downtown Legal Services and Blue J Legal: working towards a more equitable and accessible legal system

November 14, 2018 | by Lisa Cumming

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655 Spadina Avenue may not mean much to passers-by, but to some it means the world. One block above the busy intersection at Spadina and Harbord in Toronto sits Downtown Legal Services (DLS), University of Toronto’s law clinic that has been serving members of disadvantaged communities for over 40 years by offering legal advice and representation.

Under Legal Aid Ontario’s financial eligibility criteria, low-income individuals and University of Toronto students, many of whom can’t afford paid legal representation, can access the clinic free of charge. The clinic supports clients on a range of issues related to criminal, family, refugee and immigration, housing, and employment law. Students looking for help on issues stemming from university affairs can also access the clinic’s services.  

When Blue J Legal launched Employment Foresight - its AI-powered legal research software – in November, 2017, providing complimentary access to DLS seemed like a natural fit. Similar to DLS, Blue J Legal is on a mission to “provide absolute clarity to the law, everywhere and on demand” and is committed to creating a more equitable and accessible legal landscape.

To a busy downtown legal clinic, access to a legal software predicated on making research more efficient has helped staff lawyers increase their capacity and responsiveness to individuals in need of fast and effective legal help.

DLS re-opened its Employment Law Division in 2016 and has been serving precariously employed workers who typically earn at, or just above minimum wage, according to Jennifer Fehr, member of the Association of Human Rights Lawyers and a staff lawyer at the clinic.

Currently, DLS has over 30 clients with active files in the Employment Law Division but many of those clients have more than one matter that they need assistance with.

In employment cases, the clinic provides advice to and represents workers — not employers. Forums where DLS lawyers represent workers include at the Ministry of Labour, at Small Claims Court where the damages sought are less than $25,000, and at the Human Rights Tribunal of Ontario.  

The clinic primarily uses Employment Foresight to determine reasonable notice at common law and to figure out if a client is an employee or an independent contractor, according to Fehr. They do this by utilizing Employment Foresight’s Worker and Reasonable Notice Classifiers: tools that analyze the entire body of case law to predict, with an average accuracy of 90%, if a worker is an employee or independent contractor and to predict reasonable notice awards more accurately than any other tool currently available.

The Worker Classifier is particularly useful given recent amendments to the Employment Standards Act which make it increasingly risky for employers when it comes to misclassifying employees. With the Worker Classifier, DLS is able to quickly determine whether a worker should be classified as an employee or an independent contractor. This “simple” classification has huge implications for DLS clients who are given more clarity on the nature of their working relationship with their employer.

These clients, says Fehr, often don’t have employment contracts. This makes them reliant on the statutory minimums in the ESA. Given the new changes to the ESA, Fehr says that access to Employment Foresight has been really beneficial to the clinic.

“Finding sufficient cases to provide meaningful advice on a person’s reasonable notice entitlements is time consuming,” she says. “So being able to input the requisite information into Employment Foresight and have it do that work for us, allows us to serve more clients, more efficiently.”

Some employers don’t pay dismissed employees appropriately and when those workers come looking for legal advice, DLS staff lawyers — with the help of the Reasonable Notice Classifier — are able to quickly determine the reasonable length of notice that an employer must provide to an employee upon dismissal.

“Where Employment Foresight predicts the worker is an employee, we are more confident taking their case forward to the Ministry of Labour where one has to be an employee to receive compensation,” says Fehr. “Rather than proceeding through Small Claims court where there are fees required and the risk of paying costs if you are not successful.”

Sukhmani Virdi, a 2L student from University of Toronto’s Faculty of Law, worked in the Employment Law Division at DLS over the summer and into the fall semester.

Virdi says that Employment Foresight helped her quickly and accurately gain an understanding of where each client stood. This preliminary research was helpful in the next step of providing a well-grounded Summary of Advice Letter.

But, Virdi says, her use of Employment Foresight didn’t stop there. In the later steps of litigation, the tool helped with narrowing the scope of research and "what could take hours to get done can be done in half an hour.”

Through taking advantage of Employment Foresight as a research tool, DLS is able to assess client positions and offer legal advice and representation to those who need it quickly.

Together, Downtown Legal Services and Blue J Legal remain committed to creating a more transparent and accessible legal system one client at a time.

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Tags: legal tech machine learning employment law employment foresight

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