Bias is part of the workplace. Strive as we try as humans; bias is there – built into the way our brains work. As we’ve become more aware of bias in the workplace, we’ve taken steps to reduce it. Legislation around protected classes has started organizations in the right direction, but change has been slow in many cases. Gender bias is a classic example. The term “glass ceiling” dates back to the late 1970s and describes gender bias against women in business leadership roles. Forty years later, McKinsey reveals in its 2020 Women in the Workplace Report that women remain underrepresented at all corporate America management levels.
What about bias against unprotected classes? Take left-handers. A right-hand bias in the design of the iPhone 4, the benchmark for smartphones in the 2010 market, resulted in frequent loss of reception for those holding it in their left hand. Or tall people? In a study published in the Journal of Applied Psychology, researchers found a link between a person’s height and compensation in the workplace: Taller men earn more, about $800 per year for every inch they measure above average height.
There’s a bias against veterans too. Research by Modern Hire on barriers in hiring military veterans found that “Positive stereotypes of veterans (e.g., smart, loyal, strict) may help to boost veterans’ chances for field technician positions because they align with pre-employment assessment scores and match validated work styles; negative stereotypes of veterans (e.g., unstable, depressed, aggressive, angry), however, may significantly impede their ability to be viewed as a good ‘fit’ for interpersonally intensive roles, such as customer service representatives.”
So, what is new about solving this issue of bias in the workplace? In talent acquisition, advances in artificial intelligence (AI), integrated into assessments driven by industrial-organizations psychology (I-O), and analysis of enormous data sets, make it possible to eliminate bias in hiring and the adverse impact bias on protected classes in the hiring process.
Workplace bias and data-driven hiring
AI-powered pre-hire assessments are reducing bias in the workplace. Advances in AI techniques have enhanced the analysis of assessment data, uncovering complex and intricate patterns in the data that traditional analysis could not. As a result, AI-powered assessments are more predictive of candidates’ future performance in the role.
When the assessments are focused on the right kind of data – the core competencies needed for performance in a specific role – candidates’ scores on the assessments are only about job performance. This eliminates bias for all candidates, including but not limited to those in protected classes. The data can inform hiring decisions that, in the past, would have been based on the hiring team’s gut instincts or a candidate’s prior experience in a role.
Candidate perceptions of bias in hiring
There is some mistrust in the market about the use of AI in hiring and AI’s potential to introduce bias in the process. Some of that concern likely stems from the use of AI practices known to be unreliable or potentially unfair, such as AI used for facial recognition or scraping social media profiles. However, a majority of candidates who have experienced bias in the workplace believe the use of AI will drive down bias in hiring:
- 56% believe AI may be less biased than human recruiters
- 49% believe AI may improve their chances of getting hired
Read more about this study. It’s clear that candidates are optimistic about reducing workplace bias with AI.
Ensuring your AI reduces workplace bias
As indicated above, AI can be misapplied. To ensure your AI-enabled hiring and HR platforms reduce bias in the workplace, look to your technology partner’s policies on AI use.
Ensuring that AI is bias-free is part of the standards at Modern Hire for the ethical use of AI. More broadly, Modern Hire’s standards have four tenets:
- Benefit the individual
- Operate transparently
- Verify the outcomes
- Publish findings
Standards like these are vital if organizations are to realize the benefits of equity and fairness that AI can bring to hiring. To learn more about these industry-leading standards, download the white paper.