Diversity & Inclusion, Recruiting

Sources of Bias in Your Current Hiring Process and How to Mitigate Them

Something that makes us uniquely human is taking mental shortcuts. When making decisions, our brains use both our past experiences and the norms and expectations of the communities, cultures, and systems we live within, with both positive and negative impacts.

While we understand this is human nature, it is also the root of both implicit and predictive bias. Implicit bias is subconscious; it is when we have specific feelings or attitudes toward people, often with stereotypes embedded in our judgments of them. Implicit bias is, in effect, a shortcut—and often one that leaves significant room for inaccuracy.

Predictive bias occurs when an assessment or interview is used to predict a specific outcome for a particular group of people—or candidates in this case—but is found to provide different predictions for subgroups of that same group of candidates.

In tech hiring, both implicit and predictive bias are often unintentionally embedded into talent acquisition processes, which leads to inequitable, inconsistent, and discriminatory hiring practices. To remedy this issue, we must first understand the sources of bias. Below we describe two of the most common sources of bias that occur in hiring processes; why they occur; and, most importantly, how to mitigate them.

Source of Bias 1: Pattern Matching

In psychology and neuroscience, pattern matching is the cognitive process by which our brains connect current sensory stimulation with past experiences and other information stored in long-term memory. Neuroscience research points to pattern matching as “the essence of the evolved human brain.” It underlies nearly all unique features of the human brain (intelligence, language, imagination, invention) and has been vital to our species’ evolution and survival.

The tech industry, and science, technology, engineering, and math (STEM) fields more generally, has long presented barriers to entry for women and members of traditionally underrepresented populations based on race and ethnicity. This could be due, in part, to pattern matching. Repeated exposure to individuals of a certain gender or race (e.g., white males) in a specific occupation fortifies neural connections and patterns that our brains then use to match new stimuli. Because gender and race are some of the first things we notice, this pattern matching sets in early. It influences perceptions of “hireability” and potential before we even have a chance to explore more relevant, job-related signals. It may lead us to discount or ignore signals that are inconsistent with our pattern but that may be highly predictive of effective job performance.

Researchers have recently identified robust links between pattern matching and stereotyping; they find that people who excel in pattern matching are also quick to learn and apply social stereotypes. So, hiring based primarily on “fit” or the “gut feeling” many managers have about certain job candidates is often fueled by pattern matching, which can ultimately lead (consciously or unconsciously) to bias, profiling, and discrimination.

When interacting with candidates during a hiring process—either directly through an interview or indirectly through reviewing résumés and educational and professional credentials—try to hold space for them to make their own first impression. This takes practice and time because it requires brain rewiring and new habit formation.

In the meantime, it can also be helpful to remove any personally identifiable information and other information that is not strictly related to job-relevant signals from application materials. This helps us avoid triggering pattern matching based on the wrong inputs, fostering a more inclusive and accurate hiring process.

Source of Bias 2: Fundamental Attribution Error

The fundamental attribution error (FAE) describes our tendency to overemphasize dispositional factors and downplay situational factors when we evaluate other people’s behavior. In other words, our brains assume that no matter the circumstances, people’s behavior generally reflects consistent traits, such as their personality. Examples of FAE could include passing on a candidate with a history of short-term jobs because you think the person may not stay long, making a judgment based on a candidate’s social media profile content, or assuming the candidate who sent a thank-you e-mail is the best and most motivated one.

Another surprising factor that leads to FAE? The hiring manager’s mood. Research has demonstrated that a good mood increases the chances of relying on FAE, while a bad mood increases vigilance and can mitigate FAE’s influence on perceptions.

Addressing FAE begins with building emotional intelligence—and empathy in particular. Ask yourself what you would do if you were in this person’s shoes. What broader situational factors might be impacting this candidate’s behavior today? We all have good days and bad days, and on bad days, we hope we are given the benefit of the doubt. Make sure you’re doing that as you go through a hiring process or candidate interview.

Mitigating Bias in Hiring

From the smallest start-ups to the largest corporations, hiring practices can vary both across and within organizations. Often, a basic interview process simply involves a list of questions driven directly by the requirements outlined in the job description or a quick technical assessment. Without an evidence-based approach to scoring and evaluating candidates that is implemented consistently over time, your hiring process will not be equitable and will be vulnerable to bias.

A quickly growing company may skip over the need to design and implement consistent, fair, and equitable hiring practices simply because it needs to scale, and expediency takes precedence over all else. In contrast, larger organizations’ broader reach may translate to decentralized, nonstandard practices. That is, different teams and leaders may set their own standards and practices that end up varying significantly across business units or geographic regions.

If consistent methods and metrics aren’t used for evaluation, it is likely that hiring managers will default to biased thought patterns such as pattern matching and FAE. They will lean into a “gut feeling” rather than data. In short, a lack of equitable, evidence-based scoring and evaluation opens the door for many subjective and irrelevant influences on hiring decisions.

Fair, Equitable, and Data-Driven Assessments Are Vital

It is vital to invest in a fair, equitable, and data-driven assessment and hiring process for every candidate you interview. Assessments should be designed and implemented consistently and calibrated frequently over time as job requirements shift and change. Valid employment tests, particularly in tech, allow an organization’s leaders to make more human-centered, evidence-based decisions that support the goals of the business and result in happier, more satisfied, and more productive talent.

Recognizing and mitigating bias begins with awareness that bias exists. The brain’s inclination to make quick decisions and preserve cognitive capacity can be a powerful force, but it is one we can become curious about and educated on in both our personal and our professional lives.

Once we understand what bias is and where it comes from, we will be better equipped to identify it in hiring processes and ensure we are treating candidates in an equitable way, ultimately leading to inclusive organizations made up of diverse talent and skill and where employees are respected, valued, and fulfilled.

Taylor Sullivan, Ph.D, is a Senior Staff I-O Psychologist at Codility.

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