Groundbreaking Talenya Research Highlights Underrepresentation of Skills on Minorities and Female Resumes When Compared to Majority Class:
Announces Board Advisor Appointment of Tamika Curry Smith, President of TCS Group
New York, NY – Talenya, the world’s most advanced, AI-powered talent sourcing and diversity solution – released the findings from a groundbreaking study entitled Exploring The Pipeline Fallacy: Resume Skill Gaps Bury Underrepresented Minorities. Analyzing over 10 million candidate profiles, Talenya’s researchers found that minorities and females tend to share significantly less skills on their resumes when compared to their white and male counterparts. It is the first study of its kind to show that the so-called “diversity pipeline problem,” is a fallacy – in fact, there has never been a more talented, diverse population of recruitable talent across both race and gender. The problem is that tools and search processes recruiters are using are based on an antiquated technology (specifically, keyword or “Boolean” search) that is ill-equipped to find and surface these qualified, diverse candidates. To help fix this problem, Talenya is today launching its proprietary Diversity AI™ tool – built to empower recruiting teams to uncover and engage with significantly (2-3x) more qualified and diverse talent. Talenya levels the playing field for all talent regardless of race, gender, age, identity or disability – allowing them to be visible in the pipeline.
“All great talent deserves to shine. The groundbreaking Exploring The Pipeline Fallacy survey shows we are at a place in our society where people – especially women and minorities – are missing life-changing opportunities simply because a keyword is missing from their profile,” said Gal Almog, Co-Founder and CEO of Talenya. “This skill discrepancy is not necessarily a fair representation of talent capabilities. Talenya revolutionizes the job search for both candidates and hiring managers by adding wisdom to the search. We’re putting all the benefits of true AI in the hands of recruiters to give all talent the opportunity to be discovered based on merit, and empower talent acquisition teams to uncover and engage with significantly more qualified and diverse talent than any other tool. Most importantly, we’re hoping to change the behavior and perceptions of both candidates and hiring managers long-term.”
Talenya instituted the study to figure out why current recruiting systems weren’t surfacing qualified, diverse candidates to the hundreds of thousands of recruiters who are so actively searching for them. Talenya’s researchers scrubbed millions of public candidate profiles across LinkedIn, GitHub and other major professional networking platforms. Researchers found quantifiable skill deficits in public profiles of diverse talent vis a vis their white/male counterparts, specifically:
- White males list, on average, 77 skills on their profiles, compared to 68 for Asian, 63 for women, 47 for Black/African American and 37 for Hispanic candidates.
- Women also tend to write less text on their profiles, describing their career and achievements. The Talenya researchers found that women write, on average, 34.2% less text on their public profiles on social sites like LinkedIn. (Recruiters are likely to consider sparse text on a profile as a reason to overlook or simply ignore candidates.)
- Black Candidate Profiles Have 26% Less Photos than their White counterparts (People without photos on their profiles are significantly less likely to be viewed and contacted by recruiters.)
The findings were shown to be consistent across industries and job seniority – meaning the discrepancy is not necessarily a fair representation of talent capabilities but merely of the way diverse talents describe themselves. This reality causes diverse talent to get a lower prioritization on keyword-based (Boolean) search results and therefore getting missed.
Bringing Underrepresented Talent to the Forefront of Job Searches
To solve the issues of underrepresentation, Talenya announced the launch of its proprietary Diversity AI product, which empowers talent acquisition teams with a robust way to uncover and engage with three times more qualified and diverse talent, as well as two times more female talent, than any other tool – ultimately reducing time and money spent filling open roles.
Rooted in deep machine learning, Talenya’s Diversity AI™ removes intrinsic biases automatically in a way that Boolean searches and traditional tools cannot. This includes automatically creating thousands of permutations of any job search search and recommending minor changes that maximize diverse talent representation in the search results. The result: AI “removing” pent up biases and thus providing diverse talent a much greater chance to be found, typically increasing pipeline diversity by 2-3X.
Talenya employs statistical models to integrate online sources and leverage more than 900 million profiles to determine skills that applicants actually have, versus what they say they have. The results are clear; female applicants see a 10% increase in skills they actually have based on the derived skills added by Talenya, compared with the skills they say they have on their resume. Black applicants see a 13% increase, followed by Asian and Hispanic applicants, who see an 11% percent increase. Talenya’s Diversity AI™ generates the highest increase in skills for Black applicants across the accounting, marketing and sales fields, where they see up to a 17% increase in derived skills.
Enhancing the Hiring Process to Yield Increased Workforce Diversity
Talenya’s Diversity AI™ software interacts with recruiters to learn from their preferences, rather than relying on a one-sided search. The technology aims to remove friction from the hiring process by collecting fresh data from hundreds of sources to build rich, updated talent profiles. Organizations employing Diversity AI can also track improvements in workforce diversity through access to a real-time report demonstrating the increase in the percentage of diverse talent across all jobs, recruiters and stages of the sourcing funnel.
“Growing Diversity in our workforce is a major focus of Xerox and a greater diversity in the hiring pipeline is key to that. In 2020, we began piloting Talenya’s AI technology to find more diverse talent. Within a few months, we have been able to grow Black/African American candidate representation in our hiring pipeline by 95% and Female representation by 132%,” said XXXX, XXXX at Xerox, a Fortune 100 Company.
Appointing New DEI Talent to the Talenya Advisory Board
Talenya is pleased to announce Tamika Curry Smith will join the Talenya Advisory Board effective immediately. Curry Smith, President of the TCS Group, an HR and Diversity, Equity, and Inclusion (DEI) consulting firm brings decades of experience leading global brands’ diversity and inclusion initiatives. Before joining the TCS Group she served as Vice President of Global Diversity and Inclusion at Nike, Inc. From past roles working with brands such as Mercedes Benz and Target, she brings firsthand experience with the issue’s organizations face when looking to hire diverse talent, including the discrepancy between the intent to hire more diverse candidates and the lack of solutions available to help them fulfill this vision.
“I got involved with Talenya because I see the potential for its technology to truly be a game changer. CEOs and hiring managers need to rethink the way they view talent and where they look for talent. Talent is equally distributed, but opportunity is not.” said Tamika Curry Smith. “There are qualified women and people of color in the pipeline. But the systems recruiters currently use tend to find and accept candidates who write resumes and professional bios in a certain way — the “standard” way, which reflects the majority profile (male and white). This often results in diverse talent getting a lower prioritization on keyword-based search results. Talenya addresses this issue by using artificial intelligence to find diverse talent that likely otherwise might be missed by recruiters using traditional methods.”
