There’s no question that the post-pandemic workplace needs to be diverse, equitable, and inclusive. It’s also clear that hiring bias has a long-term impact on workplace culture, employee engagement, and the ability for both employers and employees to thrive.
The good news is that recruiters and hiring managers now have access to tools like artificial intelligence (AI) and skills-based hiring via digital credentials that can help them make more objective hiring and recruiting decisions.
There are a couple of ways AI works to remove unconscious bias but first, let’s take a look at exactly what unconscious bias is:
Also known as implicit bias, unconscious bias is something all people are subject to –– we tend to make assumptions based on what we think we know, whether that’s based on our background, personal experiences, or personal preferences. It’s called unconscious because most of the time, people aren’t aware that they’re making a subjective assumption.
But as organizations take active steps to remove discrimination from their hiring processes, recruiters and hiring managers have a responsibility to be aware of their own unconscious biases.
Here are a few examples of unconscious bias in the hiring process:
Science Daily refers to confirmation bias as “a tendency to search for or interpret information in a way that confirms one’s preconceptions.”
This type of hiring bias rears its head when hiring managers base their opinions of candidates on where they’re from, their name, religion, race, color, sex, age, and disability (or another federally protected category). Confirmation bias, as well as the following types of bias, is present across industries, occupations, and employer sizes. In a study called “Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination,” researchers sent resumes with randomized African-American and White-sounding names to help-wanted ads in Boston and Chicago. The resumes with White names received 50% more callbacks for interviews.
Even with evidence that debunks those preconceptions, confirmation bias filters out the evidence to skew the way hiring managers and recruiters consider candidates.
Connections or similarities, like attending the same college, sharing career history, or liking and disliking similar hobbies can arise during recruitment. These similarities turn into affinity bias when they’re given greater importance than skills, experiences, and abilities.
One popular example of affinity bias is looking for “culture fit” rather than “culture add.” Workplace culture is no longer about fitting prospective candidates into the status quo, but about creating equitable pathways for diverse employees.
2.4 million women left the workforce due to the pandemic and in February 2021, women’s participation in the labor force was just 55.8%. That’s the same participation rate as April 1987.
Gender bias impacts women and individuals who identify as nonbinary and transgender. Its effects create sexism in the workplace and lead to men in more senior positions, preventing women and people who identify as a gender other than male or female from receiving specific roles or working in certain fields.
Gender bias affects more than how people perceive and promote non-male workers in the workplace –– it determines how likely recruiters are to consider them for an open position. Research shows that women are 30% less likely to receive a call for an interview than men with the same characteristics. Our whitepaper, “How Digital Credentials Can Help Dismantle Gender Bias in the Workplace”, offers solutions to organizations looking to redefine the pathway to promotion and re-entry into the workforce for women.
Skills-based hiring can help remove these types of unconscious bias so recruiters and hiring managers can make more objective hiring decisions.
Applying AI (like machine learning, sentiment analysis, and natural language processing) and digital credentials to the hiring process is a great solution for removing unconscious bias from recruitment. From vetting candidates to extending an offer, using automated screening tools and verified credentials can aid human interaction by using data to objectively screen, rank, and grade candidates.
One way AI is used to eliminate hiring bias is when it’s programmed to look at candidates “blindly:” AI can be programmed to ignore certain demographic information about candidates, including gender, race, age, and other factors that may be unconsciously discriminated against by recruiters and hiring managers. Then, hiring teams can evaluate candidates based on what matters: how their skills, talents, and capabilities are relevant to the position.
These applications of AI and automation software are promising for the future of the workforce. For example, Atlassian increased their number of female recruits from 10% to 57% with AI. For organizations focused on building diverse and inclusive workplaces and keeping DEI a top priority for recruiters and hiring managers, skills-based hiring is critical. (Hear more about that in our webinar: Unlock Diversity, Equity, and Inclusion (DEI) Program Success with Digital Credentials).
The working landscape in the United States is continually changing as workers demand their organizations address institutional racism and build equitable paths for all employees.
Companies in the technology and STEM industries, like Goldman Sachs and Microsoft, are taking steps to overhaul their largely white, male-dominated workforces, which is a starting point. It’s entirely possible for a forward-thinking organization to take steps to transform their culture to be one of inclusivity and belonging for individuals who are often discounted from advancement.
Credly by Pearson helps the world speak a common language about people’s knowledge, skills, and abilities. With verified, portable, digital credentials, Credly empowers organizations to attract, engage, develop, and retain talent with verified, portable digital credentials that focus on an individual’s skills and capabilities. See how Credly could work for you.