How AI Сan Support Law Firms To Make More Accurate and Less Biased Hiring Decisions
AI is a relatively nascent technology. At this stage in its development, one of its best use cases is for predicting future outcomes based on its ability to identify patterns in incredibly large data sets. TikTok’s AI-powered engine only recommends the content users want to see derived from highly personalized data collection and tagging.
Music applications like Spotify use the information they have to learn, predict, and then serve their listeners’ playlists that they think their listeners will enjoy. Ideally, users give these companies feedback about how correct their predictions are, and the AI uses that input to make itself even more intelligent.
However, social media, and even music apps like Spotify, often trap their users in information echo chambers. Often, users are only exposed to news articles, music, or videos that reinforce their political or world views and ultimately remain isolated from those with different ideas.
The exciting thing about AI is that it doesn’t have to be this way. Algorithms do what their creators tell them and can easily be trained to help users question their own opinions or biases. When applied to hiring, the same types of accurate, customized, and efficient outcomes are possible, with one important distinction: the best AI-enabled hiring tools help their users become less biased, more objective, and accessible.
Here are some ways AI can help hiring teams accomplish less biased and more accurate outcomes.
Get a More Holistic View
High-volume applications and competitive recruiting timelines make it impossible to consider every candidate effectively. Law firms are often forced to focus their efforts on a small portion of the talent landscape, only considering candidates from a select number of schools with the “right” credentials, traditionally impressive backgrounds, or high GPAs.
AI allows us to go beyond these traditional factors and interpret infinitely more data points. But what other data points can AI use to help make good hiring decisions? Inputs such as personality traits, cognitive abilities, stress response styles, and values have all been shown to reduce bias and increase selection accuracy. Often, these inputs can be collected via assessments or other industrial-organizational psychology tools, expanding the amount of data that can be used to make hiring decisions.
Importantly, whatever data you collect must be relevant to the job you are hiring for. Every trait or skill you screen for must be directly applicable to performing the role of an associate/attorney. This way, the AI is making recommendations based on data that is objective and applicable to the job at hand, instead of subjective traits that often get picked up in an interview or resume screen.
Expand the Definition of a “Good” Candidate
Consciously or not, we all have ideas of what makes someone a good fit at our firm. However, our view of what makes someone “good” is not always accurate or linear.
Maybe you believe that being outgoing or extraverted is necessary for being a good attorney, so you inadvertently favor candidates who present these traits during an interview. But a question AI can help you solve is whether or not a trait like “extraversion” is actually predictive of performance. Perhaps it is, but when combined with other traits such as “assertiveness,” performance actually suffers. And again, maybe it is, but those who are “introverted” and “assertive” are just as high performing as your traditional extraverted attorneys.
AI is great at helping hiring teams pick up on these nuanced ideas, thereby allowing more space for different types of people within an organization. Instead of relying solely on resume factors to determine which candidates to bring into the process, recruiters can expand their consideration to include these other, often more predictive, factors of on-the-job success.
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Achieve Diversity Goals
It’s easy to see how AI can help us expand a firm’s diversity of thought, but what about the diversity of demographics?
Three dozen of the country’s “elite” colleges enroll more students from households in the top 1% of the income scale than they do students from the bottom 60% of the scale. Unfortunately, most firms recruit from the same “elite” places, creating an unnecessary amount of competition and leaving many candidates out of the process.
According to the ABA, Black and Hispanic students represented just 8.1% and 13.1%, of the first-year enrollment for law schools in the fall of 2021. Looking on a school-by-school basis, firms, in aggregate, would need to reach a pool of candidates that includes every Black student from the top 110 law schools and every Hispanic student from the top 76 in order for Big Law Summer Associate programs to reach parity with the US population.
The magnitude of these numbers demonstrates that the industry will not be able to solve this issue unless firms fundamentally rethink their approach. With AI and assessment science, firms can give fair and comprehensive consideration to every candidate in their pipeline. By screening only for the relevant and predictive traits, as mentioned above, you can expand your pipeline beyond your “target” school list in significantly reduced time.
Final Thoughts
In conclusion, when used correctly, AI can help recruiting teams make more accurate, equitable, and efficient hiring decisions. Firms shouldn’t be afraid to face and question their own biases, as the goal with any hiring technology should always be to make the most informed decision possible. AI offers exciting possibilities to advance how the legal industry approaches the world of talent.
