Companies are relying on artificial intelligence–powered applicant tracking systems to keep up the evolving recruitment demands. Here is how.
Pictured: Glitchy Human Hand Connecting with Robot/Taylor Tieden for BioSpace
Applicant tracking systems are software applications that help recruiters and companies manage the hiring process. “ATS serve as the technology arm behind the recruiting process, helping with the drafting of job descriptions, candidate screening, interview scheduling and onboarding,” said Liz Nguyen, an HR advisor to emerging biotech and life sciences companies.
While traditional ATS systems were effectively used by hiring teams for decades to source and hire talent, the increasing availability and sophistication of machine-learning programs has substantially changed the recruitment process. Companies are increasingly incorporating AI technology—particularly machine learning—into their ATS platforms to automate and streamline the recruitment process, keep up the demands of the market, and reduce the burden on hiring teams.
Next-Level Candidate Screening
Traditional ATS platforms parse resumes for keywords indicating skills, qualifications and experience needed to be successful in the role. “ATS applications are trained or fed with keywords typically generated by the recruiter or hiring manager in charge drafting the job description,” said Nikita Gupta, co-founder of careerflow.ai, a start-up that helps candidates refine their resumes and find their ideal jobs. Such keyword-based searches typically reject resumes lacking the keywords, potentially eliminating suitable candidates from the application process, Gupta told BioSpace.
Newer AI-powered ATS applications incorporate semantic search algorithms to extend functionality beyond keyword search to identify suitable candidates. Semantic search uses natural language processing (NLP)—the same type of AI behind ChatGPT—to process the meaning behind words and phrases and extract relevant information from resumes about candidates’ qualifications and work experience and determine their fit for a role.
AI can also be trained on vast data sets, such as historical hiring and performance data within certain roles, to identify patterns associated with high performance. Such predictive analyses provide hiring teams with insights regarding a potential candidate’s likelihood of success.
Reducing Bias
AI technology analyzes data objectively using set criteria, and in so doing, can potentially prevent human biases from influencing hiring decisions, Gupta said. AI-powered ATS systems reduce hiring biases in a few different ways, including by helping hiring teams draft inclusive job descriptions, Nguyen noted. AI can make job descriptions more inclusive by identifying discriminatory patterns in job descriptions and suggesting alternatives. These patterns could include language that excludes people of specific demographics, such as gender, age and ethnicity. By using inclusive language in job descriptions, employers aim to encourage people from diverse backgrounds to apply, which in turn helps them to find the best talent.
Next, the AI training data can be masked to prevent inadvertent biases from entering the algorithms. “It is important for the data to be free from personal information such as age, gender, race,” Gupta wrote in an email to BioSpace. “Including such sensitive information can inadvertently introduce bias into the algorithm.” For example, she wrote, if the algorithm learns to associate certain attributes (such as a name or educational background) with a particular demographic group, it may wind up discriminating against candidates who do or don’t fall into that group.
Lastly, AI-powered ATS systems can help companies partner with platforms to ensure that the jobs are showing up on diversity-focused job boards such as those targeted to women or members of underrepresented groups, Nguyen said. However, partnering with external ATS platforms and job boards raises concerns about data privacy and security that need to be addressed, Nguyen and Gupta both agreed. “Companies must ensure the ATS platforms they interact with are credible and general data protection regulation (GDPR) compliant,” Nguyen added.
Streamlining Communications
AI-powered ATS platforms integrate with other HR software and communication platforms, connecting different teams involved in the recruitment process. By improving internal communications, AI technology may help improve transparency and lead to better hiring decisions.
The platforms can also improve external communications. For example, AI chatbots serve as virtual assistants and interact with candidates throughout the application process. AI chatbots serve a myriad of purposes, from answering general questions regarding the company and open positions to role specific questions, to scheduling interviews and keeping candidates appraised of their application status.
Some ATS systems can also be used to write offer letters and send them directly to the candidate, Nguyen noted. Automating aspects of the hiring process helps biopharma companies to have an optimally sized HR group and funnel more money into R&D, she said.
“AI has significantly enhanced the capabilities of ATS, making them indispensable tools for modern recruiters and HR professionals,” Gupta wrote. “Automation within ATS can handle repetitive tasks such as resume screening, scheduling interviews and sending follow-up emails. This frees up recruiters’ time, allowing them to focus on higher-value tasks such as relationship-building with candidates or strategic decision-making.”
However, for all its advantages, ATS is only a tool and cannot replace human intelligence when it comes to recruiting, both Nguyen and Gupta said. “Recruiting is all about people, and we should remember that people connection is a part of the whole hiring experience,” Nguyen concluded.
Sunitha Chari is a freelance science writer and academic editor based in Toronto. See more of her work at sunithachari.contently.com and reach her at sunithachari.02@gmail.com.
