The Future of Hiring: Understanding Bias
Hiring Trends

The Future of Hiring: Understanding Bias

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Kristin Elliott
January 24, 2024

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Recent U.S. Equal Employment Opportunity Commission (EEOC) data shows that the top 5 charges filed against employers in 2022, after Retaliation, were related to:

  • Disability: 34%
  • Race: 28.6%
  • Sex: 27%
  • Religion: 18.8%
  • Age: 15%

While the total number of charges filed in 2022 (73,485) was down 26% from a high of 99,922 in 2011, they were still up from the 2021 low of 61,331. And these were just the claims that were reported to the EEOC.

It’s a hopeful sign that the downward trend may indicate significant gains in DEIA initiatives creating a more welcoming work environment. That doesn’t mean bias isn’t still taking place, including during the hiring process. Both conscious and unconscious bias can still come into play in hiring. Knowing what they are, especially unconscious bias, is key to finding solutions that mitigate their impact.

Conscious Bias in Hiring

Conscious Bias is a bit simpler to understand as they are typically biases people are aware they have. 

According to The Harvard T.H. Chan School of Public Health, conscious or explicit bias is defined as “Attitudes and beliefs we have that we are fully aware of based on what’s being perceived.” 

These can be manifested in several of the “isms” the EEOC sees complaints on (ableism, racism, sexism, etc.). They can also appear in other ways, from favoring people with Ivy League degrees (a form of elitism) to passing over people from a particular city or state, often seen as geographical discrimination.

Unconscious Bias in Hiring

Unconscious biases are trickier to mitigate because, as the name implies, people are not consciously aware they have them, or do not realize the impact they have in the hiring process. 

Talent Select AI’s Director of Product, Marc Fogel, MS, broke down several of the unconscious biases that plague hiring and how they can impact candidates during the interview process.

Common Interviewer Biases

First-Impression Bias

Tendency to place too much emphasis on, and heavily weighing one’s initial impression of the candidate, which is generally formed within the first few seconds of the interview.

Example: Candidate’s smile, eye contact, physical appearance, background, etc., are weighted too heavily and continue influencing the interviewer’s evaluation of the candidate throughout the entire interview.

Confirmation Bias

Tendency to search for, interpret, or prioritize information in a way that confirms one’s initial or existing beliefs about a candidate.

Example: Candidate projects confidence and gives the appearance of being knowledgeable and capable. We may give more weight to the strong points in the candidate’s responses and overlook weak responses to some questions and gaps in knowledge.

Cloning / Similar-to-Me Bias

Tendency to look for candidates who are just like oneself (including one’s own strengths and weaknesses), rather than matching the candidate with the requirements of the job.

Example: Preferring candidates who have a similar race or gender is obviously illegal, but people also fall victim to searching for other similarities, such as similar attitudes, beliefs, life experiences, or traits that are unrelated to the job requirements.

Group Membership Stereotype

Tendency to judge a candidate based on their group members, rather than their individual characteristics.

Example: Preferring a person of Asian descent for a computer programming job because Asians are ‘really good with computers’. Rejecting a woman for a position with a high travel requirement because she probably has kids and won’t want to travel.

Idealized Candidate Stereotype

Tendency to compare candidates to an historical image of the “ideal candidate”.

Example: While we’d like to believe this bias is much less common today, many roles have historically been stereotyped. Nurses were ‘caring females,’ airline pilots were ‘assertive / commanding white males,’ and executives were ‘tall, well-dressed males.’

Leniency, Severity, or Central Tendency

Tendency to use only the highest, lowest, or middle portion of the rating scale when candidates’ capabilities are truly more variable.  

Example: Think of teachers that are easy graders or hard graders. Another example is when an interviewer won’t give any candidate the highest or lowest rating because they say that “no one is really that good or that bad.”

Halo Effect

Tendency to rate candidates as outstanding (or poor) in several categories due to their being truly outstanding (or poor) in only one category.

Example: A really great person at making sales might be assumed to perform other aspects of the job well, like customer service or completing paperwork.

Contrast Bias

Tendency to rate candidates inappropriately because they are biased by the performance of previous candidates.

Example: After interviewing several poor candidates, an average candidate looks like a star in comparison.

Non-Verbal Behaviors Bias

Tendency to misread, misinterpret or place too much emphasis on non-verbal behaviors that have nothing to do with the candidate’s ability to do the job.

Example: Too much weight is placed on candidates’ non-verbal behaviors, such as eye contact, whether and when they smile, how they are dressed, their attractiveness, weight, etc.

Combating Bias in Hiring

To combat bias in hiring, many companies are now turning to pre-hire assessments. These can include competency tests, personality tests, psychometric games and puzzles, and even the adoption of Artificial Intelligence. 

The benefit of assessments over human judgment is that, unlike humans, they rate all candidates individually and in a consistent manner. There is no risk of judging one candidate against another or allowing a bad first impression to cloud how an interview is perceived.

“There are three main reasons to use assessments in hiring,” says Fogel. “The first is to gather information in a standardized way that’s otherwise hard to gather. The second reason is to enhance data-driven decision-making. And the third is really to save us from ourselves. Humans are biased. Our ‘gut’ decisions are biased. Having selection assessments help reduce that bias.”

Talent Select AI falls into the category of artificial intelligence-based assessments and was designed and validated to ensure the absence of bias or adverse impact. 

How Does Talent Select AI Help Remove Bias from the Hiring Process?

First, Talent Select AI analyzes the transcripts from a video interview using natural language processing, meaning it only reviews what is said by the interviewee. It doesn’t interpret facial expressions or vocal tone, which, for example, can negatively impact some neuro-divergent candidates. 

Second, it identifies key words and phrases that correlate to the Big Five personality traits, the Great Eight Competencies, and Talent Select AI’s proprietary Motivational Traits. It doesn’t rate candidates based on grammar or phrasing, which can often be detrimental to non-native English speakers.

Finally, Talent Select AI does not use historical data when assessing candidates; they are judged on their own merits and not in comparison to past hires. As past hiring decisions may have been biased, it’s imperative that those factors are not included in future assessment-based decisions. 

An important consideration when looking into any hiring assessment product is the extent to which it has been scientifically validated. Additionally, when it comes to AI-based assessments, the EEOC has set guidelines for the use of AI in hiring, and many states and local governments are adopting their own measures to ensure AI products remain unbiased in their hiring recommendations. 

“At Talent Select AI, we take great pride in offering a rigorously tested and validated product that conforms to the EEOC guidelines, and that also provides predictive measures of fit and performance that are proven to be free from bias,” says Will Rose, Chief Technology Officer at Talent Select AI.

Talent Select AI’s patent-pending conversational hiring assessment platform seamlessly delivers validated, predictive, and unbiased candidate personality, professional competency, and motivational trait analytics – all from your standard job interview – so you can make more data-driven hiring decisions.

Request a 1:1 solution overview today to see first-hand how Talent Select AI helps reduce bias in the hiring process and drive better hiring outcomes.

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