The gender gap in the workplace is a complicated (and often depressing) phenomenon to unpack. The World Economic Forum reported in 2017 that for the first time in over a decade the global gender gap was actually increasing rather than decreasing.
For women in tech the picture is even gloomier. Women make up only 25% of the workforce in the science, technology, engineering, and math (STEM) disciplines and just 9% of STEM executive positions. While the underlying reasons for the gender gap are myriad – including everything from a lack of mentorship to hostile workplaces – eliminating bias at the top of the funnel where hiring begins could help dramatically shrink the gender gap over the next 10 years, particularly at the management level. AI, automation and the new age of HR tech should help fast forward this process, but only when used with intent and mindfulness.
According to a McKinsey study on women in the workplace, hiring and promotion are the two biggest drivers disadvantaging women today. Inequality in hiring affects everything from talent pipelines to the rate of promotions. McKinsey estimates that over the next 10 years – at the current rate of hiring – the number of women in management will increase by only 1%. Yet, applying advanced technologies to current hiring practices the study suggests, could help drive gender parity in management over that same 10 year period. While AI, automation and HR software are no magic bullet when it comes to shrinking the gender gap, if employed thoughtfully they can help improve hiring practices and, in turn, begin to reverse the disconcerting trends around gender inequality in tech and the modern workplace.
Tools to remove bias
Advanced technologies that now dominate the landscape in HR represent fast, cheap and efficient tools that help drive diversity at the top of the funnel. Software programs, such as Textio and tapRecruit, can help eliminate biased language in job postings that may be inadvertently turning off potential applicants. AI tools for recruitment and outreach allow for significantly larger and broader candidate pipelines, before recruiters even begin to analyze CVs. AI can find patterns that align with successful applicants instead of the bias-ridden proxies (specific college degrees, brand name employers, etc.) that drive traditional recruitment strategies. Software programs, like Hundred5, enable blind screening of candidates allowing HR departments to make initial evaluations based upon merit alone. Using assessment sites like HackerRank that employ tests and neuroscience-backed games (Pymetrics) can also help match candidates to positions more equitably. Implementing personality assessments is also an effective and useful method to promote and achieve greater diversity.
Of course, all of this comes with a caveat. With AI or any of these other platforms or tools, the danger is always that certain applications will simply replicate, or (more concerningly), exacerbate previous hiring biases if used incorrectly or casually. To guard against bias, software and AI-based hiring platforms need an enlightened human safeguard and the recruiters that use them must be trained themselves on how to use these tools.
The best way to ensure this begins with intentionality. Intentionality is all about defining the qualities that will drive a successful hiring decision before embarking on the process. This is particularly true when “training” machine learning systems and the HR professionals who utilize them. Clearly defining qualities that speak to diversity and gender parity are the foundation of solid intentionality in hiring. Ideally, this kind of intentionality starts early on for businesses, as well. The significance of making diversity a priority during a company’s initial life cycle is hugely important as the biggest “diversity characteristic” driving the attraction and retention of diverse talent is simply having more representation to begin with.
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Intentionality, in this sense, also means being mindful of the unintended biases associated with certain proxies for competency. These biases are partially to blame for perpetuating negative recruiting practices, particularly when introduced into automated technologies that drive candidate pipelines. Qualification requirements and fixed mindset attributes can eliminate huge swaths of potential candidates and exacerbate hidden biases. By simply removing or relaxing the “required years” experience and education requirements, a company can significantly broaden their candidate pipeline.
Of course, targeting and reaching out to diverse candidates is only part of the gender gap/hiring dilemma. Employing best practices during the interview process, such as having a diverse interview team or having more than one female candidate in the final round of consideration the “two in the pool” effect, is also vital.
Beyond that, companies must continue to work hard to create gender-positive workplaces as diversity-hiring initiatives on their own will not be enough. Companies must be intentional about retention. That means creating a work environment that is not only diverse, but also inclusive. Only then will companies reap the rewards and truly benefit from a gender diverse workplace in the global economy. Companies must take bold steps if they are to create inclusive and equitable work cultures. Starting at the top of the funnel there are obvious and achievable ways to address the gender gap. AI, automation and a little human touch represent the best and fastest ways to get there.