Photo by Adeolu Eletu
Traditional recruiting practices don't always yield the most accurate results, but predictive hiring tools can make it easier to match the right talent with the right firm.
We previously discussed how law firms can leverage data to recruit better candidates and cut costs associated with retention. But how exactly can data analytics and artificial intelligence (AI) be used in recruiting? Although people may initially feel more comfortable trusting their personal intuitions and assessments over computer algorithms, research suggests that AI can reduce error and bias associated with the way employers traditionally recruit legal talent.
What traditional recruiting practices get wrong
Bias is everywhere—it can impact how we make decisions, and even how we perceive each other. And, unsurprisingly, it shows up in the decisions we make about job candidates, especially in the screening process, often leading to costly hiring decisions.
Traditional methods like phone or resume screening, typically used to filter candidates at the beginning of the hiring process, are often inaccurate. This means that many qualified candidates get screened-out and unqualified candidates get screened-in from the very start—leaving law firms with more candidates that are not as likely to succeed.
In fact, a study by researchers at Yale University School of Management reveals that the potential for this begins the moment that we hear someone speak. In the study, factors like speech patterns, mannerisms, gestures, or demeanor led to judgments about social class, and interviewers inadvertently played favorites with those who appeared to have a higher social status—often conflating perceived class with competence. Considering most firms primarily rely on short interviews to screen candidates and make hiring decisions, it is easy to understand how this could adversely impact the “right” candidate’s chances, no matter how qualified they might be for the job or how well they would perform at the firm.
While personal feelings about a potential candidate can be telling, they are oftentimes subjective and inaccurate. Chris Miller, a social psychologist and Assistant Professor at the University of Alaska Fairbanks, warns that this sort of bias can lead to unfair and even discriminatory hiring practices.
“As much as we like to think we are objective judges, that’s just not how our brains work. When we learn things, such as racist or sexist stereotypes, that information is always with us whether we like it or not,” explains Miller, in his hiring insights series for Cangrade Technologies.
How can predictive hiring algorithms help?
At the end of the day, it’s up to hiring managers and interviewers to hand out job offers. But if there are only five available positions and hundreds of applications, a sharp, cost-efficient, and accurate form of narrowing down applicants is vital.
More accurate “screening in” and “screening out”
In sorting through the large pool of applicants, recruiters aim to surface the candidates who are most likely to succeed in the role. If they were to throw all of the resumes in a hat, mix them up, and randomly pick out a few, they would likely get a mixed bag of average, top, and bottom performers.
This is where artificial intelligence and hiring algorithms can be put to good use. More AI-based talent management companies are relying on advanced sets of data and complex data analytics to help their clients get and keep better employees.
Using AI for smarter hiring
There are myriad ways in which to use AI to make better hiring decisions. One of the most effective methods leverages research by organizational psychologists who have consistently demonstrated the increased accuracy of using personality traits and soft skills to predict workplace success. Blending this research with cutting-edge AI, can be used to identify which personality traits and competencies drive the success of a specific law firm’s associate population. This data-driven approach creates robust recruiting models that help firms identify their next superstar hires.
To do this, organizations will typically deploy a structured personality questionnaire as the first touchpoint, which standardizes and equalizes the candidate experience. The personality traits and/or competencies surfaced can then be connected to structured interview questions—allowing the interviewer to focus on a more nuanced evaluation of a competency that is particularly important for the role without being biased by initial impressions. It also ensures that no red flag goes undetected or unexplored.
This standard evaluative framework significantly decreases the time that interviewers spend with candidates who may look great on paper but prove to be a poor match during the interview. Using this method allows organizations to avoid the typical pain points of human-led hiring practices: consistency, accuracy, and efficiency.
With lower costs and overall better talent, smarter screening and interviewing appears to be the future of hiring, regardless of the firm or industry.