Organizations are on an ongoing mission to create inclusive and diverse workplaces; however, many are still struggling to make this happen — and, as a result, missing out on whole pools of talent. Both consciously and unconsciously, many organizations’ hiring processes still suffer from the “comfortable clone syndrome,” i.e., selecting talent that looks the same, acts the same and thinks the same as the person hiring. This is negatively impacting diversity, with profound impacts on innovation, collaboration and even profitability.
Realizing this, top businesses are seeking new approaches to hiring that help reduce the chance of hiring bias. One example is Unilever. Rather than hiring entry-level workers only from elite universities — and going the traditional resume and phone interview route — Unilever now posts jobs on social media sites such as LinkedIn and invites candidates to play a few neuroscience-based games that test traits such as memory and focus. Based on those results, candidates are then asked to respond to questions on a video recording system that analyzes keywords, intonation and body language. The system algorithms then determine whether the candidate should move to the next stage, which involves a human recruiter.
Not only does Unilever claim to have hired its most diverse class to date, but it’s also seen a significant increase in hires of nonwhite applicants, achieved gender parity in new hires, and increased its talent outreach to a much wider range of universities.
Turning to Technology to Counteract Hiring Bias
Here are some additional ways for businesses to remove hiring bias throughout the entire recruitment value chain, from sourcing to onboarding, using artificial intelligence and other digital platforms:
- Removal of bias in job descriptions: Recruiters can fall at the first hurdle by unintentionally using biased wording in their job descriptions. Platforms such as Textio can score a job description on its gender and ethnicity neutrality and then suggest ways to improve it. The predictive analytics engine uncovers patterns within the language and highlights words or phrases that wouldn’t be attractive to diverse candidates (like “world-class ninja,” “rockstar” or even “amibitious”), and suggests ways to make the language more diversity friendly.
- Automated candidate selection: With the addition of machine learning, automated candidate selection platforms can generate benefits beyond simple cost reduction and speed to hire. Programs such as IBM’s Watson Recruitment, for example, can now identify the traits of top-performing individuals for specific roles and, autonomously and in an unbiased manner, move short-listed individuals to next-stage interviews. A potential pitfall of this system is that historical bias could lead to continued bias, such as an historically high correlation of rugby players being top-performing salespeople. With the majority of rugby players being male, this would immediately bias the outcome. These types of systems, then, should be directed to rely on qualifications, skills and behavioral characteristics that remove unintentional gender bias.
- Removal of bias in candidate reviews: Many organizations shy away from the concept of automated candidate selection, especially for more senior roles, relying instead on recruitment specialists to select a short list of candidates for interview. To remove bias from the human review process, businesses can use programs such as the Unbiasify Chrome extension to automatically remove gender and name from LinkedIn and other social sites. In addition, human capital management (HCM) suites such as SuccessFactors have implemented machine learning systems that remove gender and race information from applications.
- Onboarding and mentoring: Even if you’ve mastered unbiased hiring practices, you can’t assume that success will extend to your onboarding processes; an example is pairing new hires with mentors just because they’re similar and not by matching their skillsets. HCM systems, such as SuccessFactors’ Succession & Development platform, can match new hires with mentors based on skills, competencies and personality to make this process more equitable and inclusive.
While some businesses may be concerned that the introduction of AI, algorithms and automation could “dehumanize” HR, these systems are not meant as substitutes for recruiters and hiring managers. By shaking up age-old practices, in fact, they can help HR discover new hiring practices and processes that could elevate this function to a strategic level in the company — and help businesses realize their diversity and inclusion goals.
We’ve assembled some of Cognizant’s keenest minds to share their thoughts on how businesses can improve diversity and inclusion, both in an e-book, “Making Room: Reflections on Diversity & Inclusion in the Future of Work,” and a blog series.
In addition to our kick-off article on D&I in the tech industry, our upcoming blogs will cover an array of topics, grouped in four categories:
- The future of work (including blogs on moving beyond the D&I buzzword and why the future of work hinges on D&I).
- What makes us uniquely human in a machine age (including lessons from Beyoncé on authenticity, what global businesses can learn from small businesses and ensuring human centricity in a data-driven culture).
- Addressing bias (including overcoming ageism, dispelling working-mom myths, embracing adaptive technologies and using technology to tackle hiring bias).
- Working with community partners (including renovating youth development with the Lower Eastside Girls Club, empowering women through sponsorship, upskilling underrepresented talent and some bright lights of innovative D&I efforts that are actually making headway).
We invite you to read and welcome your comments to continue this vital discussion.
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