Four steps to reducing hiring bias, improved financial outcomes, and a $5 trillion U.S. economic stimulus

While championing diversity initiatives over the past 20 years, I have always emphasized how these actions will improve financial outcomes for companies. Now there is mounting evidence that by overcoming the racial wealth gap, which includes hiring biases, the U.S. could empower itself with a $5 trillion stimulus over the next five years—without an act of congress and presidential signature.

Evidence from McKinsey and others is overwhelming. McKinsey estimated that the racial wealth gap has a severe dampening effect on consumption and investment. How severe? It is estimated it will cost the US economy somewhere between $1 trillion and $1.5 trillion between 2019 and 2028. That figure is four to six percent of 2028’s projected GDP.

Altarum and the WH Kellogg Foundation in 2018 estimated that by 2050, our country stands to realize an $8 trillion gain in GDP by closing the U.S. racial equity gap.

Now, Dana Peterson an economist formerly with Citigroup and currently with the Conference Board shows in a September 2020 report how disparities along racial fault lines in housing, education, policing, and voting all feed into one another to restrict the access of Black Americans and other minorities to employment, higher incomes, and the ability to build wealth. Housing segregation, for example, leads to unequal access to quality education. Growing up poor, meanwhile, boosts the likelihood that incarceration will diminish job opportunities.

An additional 6.1 million jobs a year and $13 trillion in business revenue could have been generated over the last two decades if Black entrepreneurs had fair and equitable access to credit, according to Dana Peterson’s calculations. “At all these different levels of society and achievement, there are these roadblocks,” Peterson says in a recent Bloomberg Businessweek article. “It’s pyramidal.”

Peterson calculated that by closing the various gaps between Blacks and Whites, the U.S. could stand to gain an additional $5 trillion in economic activity over the next five years—not an insignificant number as the U.S. struggles to recover from the downturn caused by the Covid-19 pandemic.

The precautions we take to address racial barriers to hiring Blacks from human and AI screening are essential to eliminating racial, economic disparities, as this graphic from the Citigroup 2020 report illustrates.

Hiring bias is part of the problem.

My area of expertise among these “disparities along racial fault lines,” as Dana Peterson puts it, is recruiting and talent strategy. The evidence of racial and gender bias by hiring managers and now with some AI platforms is becoming increasingly clear. I first wrote about this last year in my blog, Preventing human and AI bias when recruiting and my book, Hack Recruiting: The Best of Empirical Research, Methodology and process, and Digitization.

In a concise article in the Harvard Business Review, Michael Li summarizes the scandals of AI platforms used to improve recruiting. Here is a synopsis of what he found:

  • Sources of bias in hiring abound. Some of this comes from AI. Amazon famously had to scrap its AI recruiting bot when the company discovered it was biased against women.
  • Using innovative field experiments, university researchers have shown that resume screeners discriminate on the basis of racereligionnational originsexsexual orientation, and age.
  • Discrimination is so prevalent that minorities often actively whiten resumes(and are subsequently more successful in the job market).
  • Scanning resumes, whether by computer or human, is an archaic practice best relegated to the dustbin of history. At best, it measures a candidate’s ability to tactfully boast about their accomplishments and, at worse, provides all the right ingredients for either intentional or unintentional discrimination.

What are recruiters and hiring managers to do to reduce human centric or technology centric bias? Below are four steps companies can take.

No. 1. Project-based assessments. Li recommends that companies deemphasize traditional resume screening and go to project-based assessments which are more fair and accurate: Li writes:

An unlikely parallel exists in — of all places — the field of classical music. In the 1970s and 1980s, historically male-dominated orchestras began changing their procedures for hiring. Auditions were conducted blind — placing a screen between the candidate and their judging committee so that the identity of the auditioner could not be discerned — only their music was being judged. The effects of this change were astounding: Harvard researchers found that women were passing 1.6 times more in blind auditions than in non-blind ones, and the number of female players in the orchestras increased by 20 to 30 percentage points.

Smart companies are starting to embrace more objective interviewing techniques, Li observes. Chief among these are project-based assessments. While the exact parameters vary, project-based assessments in AI and data science typically ask a candidate to clean and analyze some real-world data and write a short report of their findings. Some are more directed assessments, Li writes, while others are more open-ended. Some are take-home, while others are administered during an interview onsite. Regardless of their style, they ask candidates to demonstrate their own abilities, rather than just claim them.

I have used project-based assessments in hiring sales and marketing leaders. With the former, we gave candidates a real-world situation of a poor performing sales region, complete with facts and stories, and then asked them for a 90- and 180-day plan to turn around the sales territory. With marketing, we gave candidates data and then asked them to create a marketing presentation and present it to the hiring team, who would play prospective customers.

At Li’s firm, The Data Incubator, he found that more than 60 percent of firms now provide take-home data assessments for their candidates. Another roughly 20 percent require onsite interview data projects, where candidates analyze datasets as a part of the interview process.

Li also recommends that for companies to combat bias in AI, they need more diverse AI talent.

I have other suggestions for companies wanting to take bias out of their recruiting.

No. 2. Look at your job postings. Your job postings may be your worst nightmare, scaring off job applicants with jargon and needless clutter and driving away female applicants. If your job posting is more than 250 words and uses gender biased wording, it is time for an overhaul. Learn more here.

No. 3 Use structured interviews. Despite 100 years of empirical evidence (yes, it is 100 years.), many companies do not put the time and effort into setting up effective interviewing techniques to improve their ability to hire top talent significantly. Instead, they rely on casual, poorly prepared interviews (academic researchers call them “unstructured interviews”) that are heavily affected by first impressions and unconscious bias. Not a good way to hire.

A structured interview is based on the knowledge, skills, abilities, and competencies to perform on the job and align with company values. With structured interviews, the company creates questions to ask each candidate to determine if they have the education, experience, technical skills, social skills, and emotional intelligence to do well on the job. They can significantly remove the bias out of interviewing. Learn more here.

No. 4. Use AI technology de-biases candidate search and screening: AI technology exists to find job candidates based on objective criteria and blind candidates’ identifies, race, ethnicity and gender to prevent human bias during resume screening. AI powered tools, such as ThisWay Global, can find job candidates on the internet using objective criteria and present the top 200 job candidates blinded from the recruiter, so the recruiter cannot tell their race, ethnicity, or gender, enabling the recruiter to choose candidates based on matching job criteria, similar to the orchestra example above. Moreover, after 15 trillion matches, ThisWay Global has trained its AI to remove bias so you can benefit from a diverse talent pool. Learn more here.

How corporations work to eliminate bias in their hiring process, as discussed in this article (and how corporations’ endeavor to reduce biases in performance, pay, and promotions) can go a long way to providing the U.S. with a $5 trillion economic stimulus over five years. It is up to us in company leadership. And it does not require an act of congress and presidential signature.

 

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