Ted Sergott, EVP, Product Development atPRO Unlimited, is responsible for all aspects of PRO’s Vendor Management System, Wand.
A Fortune/Deloitte survey indicated that 96% of CEOs consider diversity, equity and inclusion (DEI) a "personal strategic priority," and Gartner, Inc. reported that DEI is the top talent management priority for CEOs.
As more organizations prioritize diversity, the challenge is figuring out how to achieve it. For starters, how can we block unconscious bias in the hiring process? How do we widen the talent pool to bring more candidates from historically marginalized groups to jobs, roles and locations where diversity has been a challenge? How can you target underrepresented groups in the most efficient, fairest way possible?
Technology is a critical part of addressing this, and new advancements in artificial intelligence (AI) are helping organizations achieve diversity objectives. Below, we'll look at how AI — including machine learning, neural networks and natural language processing — is driving exciting breakthroughs in DEI.
Blocking Unconscious Bias
In recent years, technology has helped block unconscious bias by masking the ethnicity and gender on resumes. Today, next-generation AI technologies are helping organizations take a bigger step in reducing unconscious bias by removing all potential pieces of bias so only objective data points remain.
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Here's how it works: An organization runs a job description through its AI platform, and the algorithm will suggest alternatives to biased, gendered or off-putting language, helping develop a more balanced job description. Then, when a resume is submitted, the technology strips out elements that might create bias — name, pronouns, address, educational institutions, extracurricular activities, etc.
Leading-edge technologies will then use AI to match candidates to the job and create a forced-rank list of candidates based on skills and experience. Essentially, AI provides hiring managers with an unbiased look at how candidates rank based on objective criteria.
Widening The Candidate Pool
In addition to helping remove bias, AI can help companies widen their talent pool by analyzing job descriptions, giving them an "inclusivity score" and alerting organizations to opportunities to adjust the description to attract a more diverse audience.
For example, imagine your company is hiring a data scientist and has a goal of widening the talent pool of female candidates. Behind the scenes, AI is looking at its "data ocean," finding common trends and using these learnings to suggest how you may want to tweak your job post to attract a more diverse population — in this case, more women.
The technology can analyze your job description and alert you to its low inclusivity score. Specifically, there are certain skills included that are possessed by a much higher percentage of male candidates than female candidates.
In this case, you've included the "Python" programming language as a requirement. However, your AI engine informs you that, based on its analysis, the vast majority of data scientists who know this language are male. By removing Python as a requirement and including a similar skill like the "R" programming language that a higher percentage of female data scientists possess, you can expand your talent pool and the share of qualified female candidates available in the market.
Identifying The Gaps
Widening the talent pool is a key element of improving representation throughout an organization. However, this may not be enough to achieve organizational objectives, as barriers to diversity exist beyond the initial resume review.
If selected for an interview, are candidates from underrepresented groups falling out after the first interview? Are they dropping out in the final evaluation stages? Is the issue department-specific or manager-specific? To answer these questions, you need to measure and track the different parts of the hiring process.
With the right technology, you can gain visibility into each step of the process and see where candidates from diverse groups are falling out of the pipeline. This enables targeted analysis, interventions and the ability to track impact. You can then implement training or make other adjustments to the hiring process to address the issue.
Three Steps You Can Take In Preparation For Leveraging AI
The most important step in increasing diversity via AI will be researching and selecting the best vendor to help your organization reach its objectives. AI is only as good as its inputs, so finding an organization that matches your specific needs is key. Beyond that, here are some steps companies can take in preparation for understanding and improving workforce diversity:
1. Revisit job titles and descriptions. As mentioned, skills and experience are critical to unbiased resume evaluation. While AI can help you pinpoint potential issues with job descriptions, you can accelerate this process by reviewing your current job titles and descriptions to ensure they accurately reflect the latest necessary requirements.
2. Audit the current state of your diversity data. Which diversity data points are you currently collecting on job candidates, and how are you doing so? Which additional points would you like to collect? Similarly, what diversity data do you have on existing employees, and what steps might you take to collect additional data?
3. Understand your current hiring stages and related data. Capturing data for specific parts of a hiring process can be complex. Has your organization identified the different steps in its process, and are you collecting any candidate metrics at these various stages? If not, what data would you like to collect, and at which stages? Think about how you will use the data to impact change.
Embracing The Next Generation Of Hiring
The business case for increasing diversity is stronger than ever. According to a McKinsey report, companies in the top quartile for "ethnic and cultural diversity ... outperformed those in the fourth one by 36% in profitability." In the same analysis, "companies in the top quartile for gender diversity on executive teams were 25% more likely to have above-average profitability than companies in the fourth quartile."
Whether you're looking to increase diversity by gender, ethnicity, age, neurodiversity, socioeconomic background or any other dimension, AI can help. With the right technology and human expertise, your organization should be well-positioned to reach its diversity goals.
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