How systemizing law firm work allocation enhances diversity efforts and overcomes affinity bias

Blakes and Cassels discuss their policies and how analysis reveals lawyers don't always assign fairly

How systemizing law firm work allocation enhances diversity efforts and overcomes affinity bias
Nikki Gershbain, Kari Abrams, Shlomi Feiner, Anne Glover

To overcome the inequality of opportunity inherent in traditional work allocation, law firms are designing systems that eliminate bias and chance and ensure associates have comparable experiences.

Historically, says Nikki Gershbain, proximity and familiarity have driven work allocation in the legal profession. The traditional, relationship-based, free market model allocates work to “whoever pops to mind first” when a partner has work to assign. Gershbain is an equity, diversity, and inclusion strategist, founder and CEO of IDEA Consulting Group and the former chief inclusion officer at McCarthy Tétrault.

If the associate successfully demonstrates their abilities on the initial file, the partner offers them more. Eventually, the associate becomes the person the partner trusts most, and the partner talks about it. The associate develops a reputation, receiving desirable assignments from other partners and earning skills and expertise. Provided that the associate possesses the other attributes necessary for success, they are firmly on the partnership track, says Gershbain.

“Contrast this with what the experience is like for an associate who doesn't get tapped,” she says. “They don't pop to mind, for whatever reason, and it means they don't get those initial high-quality opportunities. They don't get on the radar of key partners. They're unable to develop the competencies, visibility, or networks they need to progress.”

The less experienced associates are more expensive for clients, so it becomes more challenging to staff them on files.

The associate flounders; their performance reviews are not great, and they read the signals that they are not rising at the firm, so they leave.

The traditional, free-market work allocation system can be arbitrary and have nothing to do with ability or interest. Kari Abrams is the director of legal personnel and professional development at Blake, Cassels & Graydon LLP. She says a certain associate may “pop to mind” because of a past relationship with the partner or even because they happened to have crossed paths in the office kitchen when the partner was thinking about the file.

“It can be something as innocuous as office location,” says Shlomi Feiner, a partner at Blakes whose practice involves M&A, securities, and corporate law. “The easiest place to find someone to do your work is to look right next door. We don’t want something as random as office location to determine whether or not an associate had access to opportunities.”

The research shows that the associate who comes to mind tends to be a white man, and those who do not tend to be women and men of colour, says Gershbain.

“The main problem with this traditional, relationship-based model is that it puts a premium on relationships, and therefore it risks affinity bias,” she says.

Affinity bias is the unconscious tendency to gravitate to those with similar backgrounds, interests and beliefs.

While it is human instinct to prefer those with whom one has commonalities, it leads to problematic outcomes in work allocation, says Kim Bonnar, chief professional resources officer at Cassels Brock & Blackwell LLP.

Several years ago, when Blakes examined associate attrition, they noticed that many who left the firm were junior associates with lower hours who did not feel like they were part of the team. According to Abrams, Blakes found that women and people from equity-deserving groups were disproportionately represented in this group and were leaving the firm earlier than others.

“We stepped back and decided that it was time for a change and started looking at ways that we could allow for a more equal access to opportunity in our associate ranks.”

The alternative to a relationship-based model is “structured allocation,” where work is dealt to associates systematically and based on objective criteria, says Gershbain.

The system is often geared toward providing associates with an optimal, wide-ranging experience.

Gershbain provides an example of how the system would look for a litigation practice. Litigation associates need particular experiences. They need to go on a motion, draft statements of claim and defence, and run a trial. Structured allocation would aim to ensure the associates had the opportunity to check all the necessary experience boxes within a certain timeframe.

Structured allocation distributes work more evenly and manages it more effectively, she says.

Blakes studied the experience of firms in the US who had already implemented their own work allocation systems. They created their own and piloted it with their securities group.

Bonnar says Cassels took a similar approach, borrowing pieces that fit with the firm’s culture from the systems in legal and other professional services organizations. She adds that it is essential that the system aligns with the organization’s values.  

Blakes examined various types of transactions with which a junior associate should have experience and built a matrix that would guide incoming work so its benefits were evenly distributed, says Abrams. It was crucial to get partners to buy into the system, she says.

Feiner says the system spreads experience, work hours, and partner interaction evenly so each associate can benefit equally from all three elements.

Whereas at Blakes, the partners allocate the work according to the system, Bonnar says Cassels’ system is run by their professional resources team, with the input and collaboration of the partnership.

Abrams says Blakes’ proof of concept worked so well that they rolled it out to other practice groups.

“We're doing this because we care about our associates,” says Anne Glover, partner in Blakes’ litigation and dispute resolution group. “This works because it involves people who know our associates. We don't use AI. We take a lot of data, and we make very considered decisions about it.”

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