A Human + Technology Approach For An Inclusive Hiring Process

Making tech hiring more equitable and inclusive

Interviews are incredibly intimate and vulnerable moments in the hiring process. They are stressful for candidates and time-consuming for hiring managers. And with today’s renewed focus on DEI initiatives, companies are looking for ways to reduce biases and make their hiring more inclusive.

It’s easy to look at technology as a silver bullet. By making interviews and recruiting processes less human, we can take out the human bias, right?

Not exactly. We can look to other industries like insurance or mortgage lending to see some of the pitfalls of blindly applying AI without a human-centered lens. Dropping data into a black box of AI algorithms have been problematic in industries like lending and insurance. If implemented without responsible human oversight, AI can actually codify bias, and bake centuries of systemic racism into the hiring process by amplifying pedigree bias. For example, this candidate X went to a top 10 computer science school and performed well, so we’ll optimize the hiring funnel by only recruiting developers from those schools.

Instead, it’s critical to identify the places where technology and people are most useful, and/or most prone to potential bias. A good place to start, especially for hiring technical talent, is to look at how a human interviewer’s presence impacts technical assessments’ inclusiveness.

Today’s research shows that the emphasis code tests place on absolute completeness and correctness fails to produce an inclusive or predictive hiring signal. Code tests disproportionately filter out underrepresented minority groups by “failing” qualified software engineers that could receive offers based on live technical interviews.
 

Last year, research revealed that code tests that require “absolute completeness and correctness” could be filtering out as many as 1 in 3 developer hires. Conversely, hiring processes that start with a live technical interview show 55% of offers go to candidates with incomplete solutions. Human-centered technical interviews represent a real-work environment and support candidates in a way that code tests, by their nature of being a solo-effort with a pass/fail outcome, cannot.
Digging deeper, the data showed that reliance on absolute completeness and correctness disproportionately impacts underrepresented minorities and women. This should be a red flag for every organization that aims to be inclusive, diverse, and free of systemic barriers to hiring software engineers.

As more companies build diverse talent pipelines from HBCUs, HSIs, Grace Hopper Conference, and organizations like the National Society of Black Engineers, hiring teams ought to ask: are code tests preventing the hiring process from being as inclusive as it ought to be? Should we be looking for alternative ways to measure talent during the hiring process?

The incompleteness of a solution doesn’t mean a candidate can’t code.  Over the course of a 60-minute technical interview, candidates have opportunities to demonstrate problem-solving and coding skills relevant to a role and hiring bar in various ways. In our experience, less than a third of candidates complete all three problems in the Karat technical interview.

Completeness is just one factor that needs to be considered but becomes a binary pass/fail element in the context of a code test. However, in live interviews, more than half of all job offers go to candidates with incomplete solutions. For underrepresented minorities (URM) and women candidates, that is slightly higher at 57% and 59%, respectively.
 

 

 *For analysis purposes, URM candidates include those who identify as Black or African American, Hispanic or Latino, Native Hawaiian or Pacific Islander, and Two or More Races

 

Who hasn’t made a simple spelling mistake while coding? Or gotten stuck while solving a problem simply because, let’s face it, interviews tend to be nerve-wracking? Code tests are solo-efforts. They don’t allow candidates the opportunity to ask clarifying questions and receive guidance from an engineer experienced in conducting interviews. Yet, 19% of job offers to women started with a technical interview that included some form of guidance, as did 23% of job offers to URM candidates.

 

 

In the short term, organizations can analyze their hiring process and look for places where underrepresented minority and women candidates may be falling through the cracks due to false negatives and intentionally find ways to help them succeed. Evaluating the hiring process by examining how the company builds a talent pipeline and assessing candidates’ skills is an excellent place to start.

Preparing candidates for the hiring process is another critical step. Make sure they understand what is being assessed and how so they are familiar with the process. This prevents someone who has a connection working within a company from having an unfair advantage by knowing what kind of questions to practice, while a candidate from a nontraditional background is left not knowing what will be assessed. Programs like Brilliant Black Minds help to unlock opportunities for underrepresented software engineers by providing free practice interviews, feedback, and professional development opportunities. This levels the playing field and delivers on our belief that real-life human technical interviews are a step towards reducing biases regarding inclusive hiring efforts, especially for underrepresented communities.

A human + technology approach is essential to make tech hiring more equitable and inclusive. However, keep in mind there is no silver bullet for building an inclusive hiring process. It takes trials and complex analysis to understand why organizations may not be achieving its diversity goals. By acknowledging that diverse software engineering talent does exist, and taking a more holistic approach by implementing live interviews, hiring teams can achieve a more complete picture of each candidate beyond pass/fail.