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As talent acquisition evolves, artificial intelligence appears to be the real thing.
Illustration by Michael Korfhage for HR Magazine.
Whether analyzing the facial expressions of job candidates in video interviews, sorting through multitudes of online applications or keeping job prospects apprised of their hiring status, artificial intelligence (AI) is moving rapidly from experimentation to mainstream use in the talent acquisition world.
Many recruiters who get a firsthand look at this rapidly evolving technology are struck by how it can make their lives easier. While artificial intelligence typically conjures up images of futuristic human-like robots, the term can be broadly defined as any so-called smart software—that is, technology with the ability to learn and grow more effective over time.
"There’s a greater level of maturity in AI tools in the recruiting space than in any other area of HR," says Helen Poitevin, a Paris-based human capital management research director at Gartner, an information technology research firm.
Continued advances in AI will make tomorrow’s recruiting look much different from today’s, but for now recruiters are hailing the technology for its ability to reduce the "grunt work" in their jobs.
Rise of Artificial ‘Assistants’
Among the most evolved AI tools are artificial "assistants" that can improve the job seeker experience. One such tool is Mya, which automates much of the communication with candidates during the application phase. Mya was created by FirstJob, a San Francisco-based HR technology company. It uses natural language technology to ask questions of candidates based on job requirements and answers applicants’ questions about employers and keeps them apprised of their hiring status.
Mya also responds to queries about company policies, benefits and culture around-the-clock through SMS, Facebook, Skype, e-mail or a browser window called a chat client where people can chat instantly. This saves recruiters from having to field the same inquiries time and again. If Mya is stumped, it contacts a human recruiter and then responds to the candidate with an answer.
"This kind of AI facilitates engagement with applicants and can help improve the candidate experience," says Elaine Orler, founder and CEO of Talent Function, a recruiting consulting company in San Diego.
Other emerging tools promise to remove transactional tasks from recruiters’ plates, freeing them up to focus on interviewing and closing job offers. One application that may soon be part of recruiting platforms is X.ai, an artificial assistant that can schedule meetings or interviews. Such assistants are an extension of existing apps such as Reschedge, which help recruiters reduce time spent scheduling interviews through an automated process that manages multiple calendars simultaneously and makes updates if there are changes.
[SHRM members-only how-to guide: How to Target Passive Job Seekers]
Promise and Possibilities
Recruiters need look no further than the paragon of artificial intelligence, IBM Watson, to understand what’s possible today. Watson brings new efficiencies to HR through applications that derive insights from vast amounts of data, continually build knowledge and offer personalized recommendations.
Watson can help recruiters measure the degree of difficulty that will be required to fill certain jobs and prioritize positions, predict with accuracy the likelihood of candidates being successful, and perform social media "listening" to develop insights that help recruiters improve messaging to candidates.
Consider the process of triaging hiring. "Too often, it’s the squeaky wheel or the person pushing the hardest who gets his or her requisition addressed first," says Bob Schultz, general manager of IBM Talent Management Solutions in San Francisco. "What Watson does is look across the pipeline, determine what fill rates have been and perform analytics against requisitions in the system to help recruiters decide which [requisitions] to focus on first—and explain why they should do so."
Artificial intelligence has also made inroads in vendors’ video-interviewing platforms. For example, Boston-based Affectiva, an emotion recognition software company, helps gauge candidates’ emotional intelligence and truthfulness during video interviews by analyzing facial expressions, word choice, speech rate and vocal tones.
"Interpreting and inferring feelings based on candidates’ facial expressions is an intriguing use, but it is still early days for the technology," Poitevin says. "Voice analysis is more evolved as a technology partly because there are more indicators that come into play, like how often people hesitate and the tone of voice they use." Vendors such as HireIQ are using voice analytics to aid recruiters in hiring workers for jobs in customer contact positions.
Thomas Bayes develops framework for reasoning that becomes top approach in machine learning.
Nikola Tesla demonstrates the world’s first radio-controlled boat.
Czech writer Karel Capek introduces the word “robot” in his play R.U.R.
The movie “Metropolis” features a robot double of a peasant girl, the first robot depicted on film.
Alan Turing proposes the “imitation game” that later becomes “Turing test” of computer intelligence.
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Parsing Passive Candidates
New software, such as EngageTalent, combines news with workforce data to determine the odds that people holding jobs might be looking for greener pastures.
These "predictive availability signals" are based on factors such as recent company performance or personnel changes. For example, EngageTalent could allow recruiters to search for critical care nurses in Dallas with master’s degrees who work for health care systems that are closing locations.
"This is the kind of AI where a lot of investment and innovation is happening today," Poitevin says.
Other AI is expressly designed to make one particular aspect of recruiters’ lives easier: identifying the best prospects from the many resumes, online applications and LinkedIn profiles employers review for job openings.
Artificial intelligence assistant RAI from HiringSolved, a talent acquisition company in Chandler, Ariz., uses natural language to interact with recruiters (similar to Amazon’s Alexa, a voice-activated virtual assistant housed in the Amazon Echo smart speaker). RAI asks what kind of worker the company is looking to hire, searches for available candidates and then helps refine the search. RAI can also e-mail job seekers.
Context and Patterns
Data is the lifeblood of AI, and without large volumes of good historical information, software won’t produce the kind of insights or recommendations needed to elevate recruiters’ decision-making.
"The biggest barrier to true AI is lack of good data, and the biggest risk to organizations is not understanding the context of that data," says Stacey Harris, vice president of research and analytics at Alpharetta, Ga.-based Sierra-Cedar, which conducts an annual HR Systems Survey.
"That’s why IBM Watson is so effective, because it has been sucking up quality data for years," she explains.
However, because AI analyzes and learns from patterns, there is a danger of the software replicating biases in recruiting or promotion processes, Harris says. For example, if a company’s top performers historically have been identified as white males between 30 and 40 years old—because those individuals were frequently promoted into next-level jobs—that bias can inadvertently become built into algorithms that learn from talent management patterns.
"Artificial intelligence is only as good as the information it has been given to learn," Orler says. "Without conscious thought involved to ensure we’re not perpetuating bad behavior, there will still be a need for humans to make final hiring or promotion decisions."
Dave Zielinski is a freelance business journalist in Minneapolis.
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