How AI and Machine Learning are Transforming the Hiring Process
Last Updated March 28, 2024
These days, simply landing an interview for a job often requires a job applicant to think like a computer—passing an algorithm-powered applicant tracking system (ATS) before even receiving a chance to impress the hiring manager and land that dream position. For job seekers, knowing that tracking systems are taking on the first right of refusal is important for their application strategy, but understanding the nuances, like how data analytics, artificial intelligence and machine learning are being used throughout the hiring process may be less clear. What exactly occurs from the initial application that “disappears” into the tracking system to screening, interviewing, and hiring? For HR professionals, understanding the best uses and ramifications of these applications is critical for making smarter hiring decisions that impact organizations, job applicants and even the future of the HR profession.
Using AI and ML: How HR is Applying the Technology
Most companies are swamped with applications for open positions, particularly in an age where jobs are posted online, and job seekers across the country can apply for a position with just a few button clicks. This bevy of resumes presents a challenge for organizations hoping to find quality candidates among the noise, and applicant tracking systems (ATS) are regularly applied to the recruiting field to help with the sorting process. Through artificial intelligence, organizations are making their ATS smarter, establishing algorithms that help recommend best fit based on segmentation analysis. An algorithm can limit geographies if the position is a local hire only, require attendance at a specific school, or look for specific previous positions, filtering the applications based on the set parameters. While the AI will not select a single individual, it will compile a much more manageable list of recommendations for an HR professional to sort through.
“As an HR professional you’re not reading applications; you’re getting recommendations from the machine,” says Keith Niblett, Assistant Director of Executive Development Programs at Michigan State University’s Eli Broad College of Business. “It’s all machine learning.”
These recommendations can include predicting turnover rates or profiling a first day at work. For example, the AI software Interviewed can predict a candidate’s behavior on their first day based on a constructed profile – and assess fit within the company culture. SkillSurvey uses AI to predict turnover and performance by asking a set of questions rooted in behavioral science, helping companies determine if the prospective employee that looks good on paper will live up to the set expectations.
Artificial intelligence powered by machine learning can also analyze massive amounts of data to help refine existing processes. For example, the recruiting field applied an approach already in use in the healthcare field called survival analysis, which analyzes time-to-an-event based on existing data. Instead of predicting time before a disease reoccurs or a patient death, the HR field is using this same technique to assess length of time to fill positions based on the specific job opening. This is valuable for organizations, as they can staff to recruit and interview more heavily for challenging roles, reducing the time, productivity and revenue lost on an open position.
While AI can take on the heavy lifting for sorting, “HR professionals need to understand how the machine is doing their job,” Keith Niblett advises.
That also means understanding the risks. AI recommendations are only as good as the algorithm that powers them, and that algorithm can change over time as a result of the machine learning. While there are high hopes that artificial intelligence can overcome human bias and discrimination in hiring, Amazon found that its recruiting system built with machine-learning algorithms was beginning to downgrade a disproportionate number of women’s resumes, as the algorithm was loaded with resumes from over a 10-year period in which more male candidates had applied and were hired than were women. Therefore, the AI had “learned” that keywords and features from the male-dominated data were what it needed to look for in all applicant resumes.
As AI and machine learning continue to be fine turned for human resource management applications, it remains crucial that HR professionals retain that human role, and human oversight, in the process.
Taking Advantage of AI in the Job Search
For job seekers, it may feel like “beating” these systems is the best way to land a job, but this machine intelligence can help in their job search. A working understanding of how AI operates can arm job seekers to capitalize on the system. For example, recommendations, both for job openings and for candidates, are often powered by keywords found in the job description. Instead of relying on the same resume for every position applied for, job seekers can develop multiple versions that highlight different strengths or customize resumes to mirror keywords for an individual job posting, making it more likely an organization’s interest will be peaked by triggering their specific algorithm.
Emerging applications are putting the power of AI and even augmented reality into job seekers’ hands. With a relaunched mobile app, CareerBuilder has gone from an online job search site to a personalized headhunter, automatically applying to jobs that match a user’s parameters and using hyper-local search and geotargeting functionalities to show job openings at companies in specific areas in real time. A job seeker can simply open the app as they stroll through that new trendy urban district, retail complex, or even drive through a business park to view and compare the area’s open positions through this map-based targeting. Staying in tune with – and leveraging – these new capabilities can help job seekers enjoy the same convenience and efficiency the HR recruiters have on the other side of the job posting.
Human Resources for the Future
As with any new technology, the application and use of AI and machine learning will determine they are a benefit or a detriment. Manoj Saxena, Michigan State University alum and CEO of augmented intelligence software maker CognitiveScale, has advised fellow business leaders to ensure the technology is both understood and applied “in a way that is responsible and ethical,” ensuring AI systems are “secure, transparent, explainable and accountable.”
Artificial intelligence and machine learning will continue to present both opportunities and challenges for HR professionals and job applicants alike. As these technologies increasingly permeate organizations, professionals can equip themselves for this new landscape with enhanced skills for applying data analytics, data mining and management. Harnessing these technologies for efficient and effective use will require a firm understanding of these tools and the data and analytics that powers them, both for the workers of the future and the people who hire them.
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