We’ve seen how HR is already mismanaging hiring by using primitive automation tools for screening, and how future progressive regulations may make the situation even worse. Meanwhile, social media and online profiles are providing more honest data on candidates than ever before, but HR is warning hiring managers not to look at it.
The good news may be that AI in smarter screening programs may be able to use online searches and carefully-designed online questionnaires to do a much better job of identifying possible great hires and screening out the deadwood. Meanwhile, leading-edge employers like Google have discovered overly-specific degree and experience qualifications can actually screen out some of the most productive people in the applicant pool. If any company can apply data analytics and AI to hiring and performance management, it would be Google. How did Google do when they tried? The New York Times interviewed senior VP of people operations (Google’s name for HR, apparently) Laszlo Bock in 2013:
Years ago, we did a study to determine whether anyone at Google is particularly good at hiring. We looked at tens of thousands of interviews, and everyone who had done the interviews and what they scored the candidate, and how that person ultimately performed in their job. We found zero relationship. It’s a complete random mess, except for one guy who was highly predictive because he only interviewed people for a very specialized area, where he happened to be the world’s leading expert….
On the hiring side, we found that brainteasers are a complete waste of time. How many golf balls can you fit into an airplane? How many gas stations in Manhattan? A complete waste of time. They don’t predict anything. They serve primarily to make the interviewer feel smart.
Instead, what works well are structured behavioral interviews, where you have a consistent rubric for how you assess people, rather than having each interviewer just make stuff up.
Behavioral interviewing also works—where you’re not giving someone a hypothetical, but you’re starting with a question like, “Give me an example of a time when you solved an analytically difficult problem.” The interesting thing about the behavioral interview is that when you ask somebody to speak to their own experience, and you drill into that, you get two kinds of information. One is you get to see how they actually interacted in a real-world situation, and the valuable “meta” information you get about the candidate is a sense of what they consider to be difficult.
Google used to be known for hiring only people under 30, using those brainteasers to identify top programming talent and relying on academic qualifications, favoring degrees from prestigious universities. That’s no longer true:
One of the things we’ve seen from all our data crunching is that G.P.A.’s are worthless as a criteria for hiring, and test scores are worthless—no correlation at all except for brand-new college grads, where there’s a slight correlation. Google famously used to ask everyone for a transcript and G.P.A.’s and test scores, but we don’t anymore, unless you’re just a few years out of school. We found that they don’t predict anything.
What’s interesting is the proportion of people without any college education at Google has increased over time as well. So we have teams where you have 14 percent of the team made up of people who’ve never gone to college…. academic environments are artificial environments. People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment. One of my own frustrations when I was in college and grad school is that you knew the professor was looking for a specific answer. You could figure that out, but it’s much more interesting to solve problems where there isn’t an obvious answer. You want people who like figuring out stuff where there is no obvious answer.
So how are they applying their famous data analytics to hiring for Google? Very methodically, as you would expect. According to an Atlantic story:
In the summer of 2006, Todd Carlisle, a Google analyst with a doctorate in organizational psychology, designed a 300-question survey for every Google employee to fill out… Some questions were straightforward: Have you ever set a world record? Other queries had employees plot themselves on a spectrum: Please indicate your working style preference on a scale of 1 (work alone) to 5 (work in a team). Other questions were frivolous: What kind of pets do you own?
Carlisle crunched the data and compared it to measures of employee performance. He was looking for patterns to understand what attributes made a good Google worker. This was strongly related to another question that interested his boss, Laszlo Bock, senior vice president of People Operations: What attributes could predict the perfect Google hire?
…Google was essentially trying to Google the human-resources process: It wanted a search algorithm that could sift through tens of thousands of people—Google’s acceptance rate is about 0.2 percent, or 1/25th that of Harvard University—and return a list of the top candidates. But after a great deal of question-asking and number-crunching, it turned out that the best performance predictor wasn’t grade-point average, or type of pets, or an answer to the question, “How many times a day does a clock’s hands overlap?” The single best predictor was: absolutely nothing.
Much research shows referrals to be the most reliable source of better hires, so Google’s early emphasis on ties to computer science professors to recruit the best students for their early programming teams was a good if limited strategy. Referrals are more likely to be “good fits” because the skills needed for good teamwork are more likely to get someone referred:
The study found that referrals produce “substantially higher profits per worker” who are “less likely to quit,” “more innovative,” and “have fewer accidents”—all this, even after controlling for factors like college, SAT scores, and IQ. Team-based companies require openness, compatibility, and a willingness to cooperate. Referral programs work because great employees pass along workers who similarly match the company culture.
Although they account for only six percent of total applications, referrals now result in more than a quarter of all hires at large companies, according to a recent paper from the Federal Reserve Bank of New York and MIT….
Google, which depends on referrals, once administered up to 25 interviews for each job candidate. Todd Carlisle, the organizational psychology doctorate who administered the company’s surveys in 2006, thought this might be overkill. He tested exactly how many interviews were necessary to be confident about a new hire. The right number of interviews per candidate, he discovered, was four. This new policy, which Google calls the Rule of Four, “shaved median time to hire to 47 days, compared to 90 to 180 days,” Laszlo Bock wrote in his book Work Rules.
But Carlisle’s research revealed something deeper about the hiring process, which has resonance for every industry: No one manager at Google was very good, alone, at predicting who would make a good worker.
Four meticulously orchestrated Google interviews could identify successful hires with 86 percent confidence, and nobody at the company—no matter how long they had been at the company or how many candidates they had interviewed—could do any better than the aggregated wisdom of four interviewers.
It turns out that a single Google hiring manager, at least, is often not that good at judging candidates — but when four of their judgments are combined, the result is as good as it’s going to get. This convinced the company to drop their over-interviewing policies, which took much candidate and staff time and delayed hiring by months.
So what are the prospects for automating hiring? Aptitude test scores have considerable predictive value in many cognitive jobs, but could one automate the emotional intelligence and teamwork skills testing needed to find good team workers? Google has tried and (at least as far as they’ve disclosed their practices) failed to find anything better than referrals and face-to-face interviews.
But software companies keep trying to improve ATS (Applicant Tracking Systems) functions to do a better job:
Companies such as Facebook, GE, IBM, Hilton Worldwide, SAP and many others have been slowly adding data analytics into their recruitment practices. A few years ago, it was unheard of to scan candidate resumes for data, but now it’s commonplace. Machine intelligence is being used to scan through other aspects of candidate information, such as their social media content, their facial expressions, even their work samples to identify top candidates – and weed out the undesirables.
“Such practices raise questions about accuracy and privacy, but proponents argue that harnessing AI for hiring could lead to more diverse, empathetic, and dynamic workplaces,” says Sean Captain, a journalist with Fast Company.
…“corporate recruiting is broken” as a system. It’s filled with inaccuracies and black holes where candidates disappear…. “85% of job applicants never hear back after submitting an application.” This indicates that some recruiters are still not able to stay on top of recruitment processes, and the candidate experience has a long way to go towards being a positive one.
Perhaps there is room for more automation and AI in recruitment if it can restore better recruitment practices from the human side of things. Kibben mentions that AI will improve the candidate experience and is a winning proposition for recruiters who will be able to strategically partner with hiring managers instead of simply filling job requests.
Lots of buzzwords and promises, few real advances. One semi-useful tool now becoming popular is the automated interview system — imagine an online interviewing system where the applicant answers preset questions in front of their PC, laptop, or phone camera, with the video uploaded for later replay by HR staff and hiring managers. This certainly cuts down the overhead of doing interviews — no more paying to fly candidates out and take them to dinner, just video dating-style files to pick up those subtle clues about the candidate normally gleaned from a face-to-face interview.
How does that work out in practice? A company called HireVue claims to analyze video interviews using AI tools:
The deep dive into a candidate’s mind isn’t a new idea, says Mark Newman, founder and CEO of HireVue. Founded in 2004, it was one of the pioneers in using AI for hiring. Its specialty is analyzing video interviews for personal attributes including engagement, motivation, and empathy. (Although it also uses written evaluations.) The company analyzes data such as word choice, rate of speech, and even microexpressions (fleeting facial expressions).
But most users of their systems are just looking for a cost-effective substitute for face-to-face interviews, with only a few using “AI” to evaluate the candidate videos. HireVue is increasingly important:
HireVue Inc., which provides video interviewing software for Goldman Sachs and 600 other firms, said it hosted nearly three million video interviews last year, up from 13,000 five years ago….
Most video-interviewing programs require applicants to click a link or install an app. Interviews begin with a prompt such as “Tell us about a time you had to deal with a conflict” that stays on-screen for about 30 seconds. Then, the camera turns on and the candidate has anywhere from 30 seconds to 5 minutes to respond before the next question pops up.
Human-resources staff then review the videos and pass along promising applicants to managers for consideration. Applicants who make the cut are typically invited to a one-on-one interview. That doesn’t always mean it will be in-person, though. Varsha Paidi, a software engineer hired by IBM last year, had subsequent online interviews and eventually received her job offer via text message.
Speeding up the hiring process allows recruiters to look at more applicants than before, giving companies wider reach, said Obed Louissaint, the human-resources lead for IBM’s Watson division.
Applicants, however, say that computer-guided interviews take some getting used to. Amy Hall was never the type to get nervous during job interviews, but when the 29-year-old had to complete a video interview last year for an internal job switch at Cigna-Healthspring, she recalled feeling apprehensive and camera-shy. She waited until after work hours and used a computer in the IT department. With the door closed, she clicked a link to Cigna’s video-interviewing site….
Companies say they seek similar traits in video interviews as they do in traditional interviews. Recruiters at IBM and Cigna said they evaluate candidates based on how well the person communicates his/her thought process, whether the person answers all parts of the question—and whether he/she makes eye contact…
Video interviews might also present some problems because managers cannot ask follow-up questions or engage candidates further on a point, said Carol Miaskoff, assistant legal counsel for the Equal Employment Opportunity Commission. In letters to vendors, Ms. Miaskoff has suggested that companies assign more than one person to review individual videos to ensure hiring decisions aren’t made hastily.
Taking robo-recruiting one step further, some HireVue customers have an algorithm review the video interviews for them. Using data about the skills and attributes companies are seeking for a given role, a program called HireVue Insights scans videos for verbal and facial cues that match those skills then ranks the top 100 applicants.
Given that in-person interviews by staff tend to wander and often turn into staff evaluations of whether the candidate will be enjoyable company or not, a fixed format with questions set in advance does actually promise to reduce the element of good-old-boyism. Everyone has experienced the job interview that turns quickly to discussion of sports or hobbies in common — the interviewer pays less attention to skills and attitudes than shared cultural enthusiasms, tending to favor cultural clones of themselves whose company they will enjoy. But notice that most companies still rely on human HR staff judgement to screen the resulting videos, which saves time for hiring managers but still introduces an element of HR prejudice. If your HR staff are primarily left-leaning New England-educated feminists, a white male candidate with a Southern accent and stereotypically male mannerisms will likely be screened out. For once the EEOC advice is reasonable — this type of screening will be more effective if more than one person reviews each video, making it more difficult for prejudice to prevail.
Giving HR staff veto power over candidates seems unwise. Practical considerations require obviously unqualified candidates to be weeded out early when a position attracts large numbers of applicants, but hiring managers and team members should invest the relatively minor time it takes to review these types of video responses themselves, as they are likely to be the best judges of culture fit and attitudes revealed by video.
Applicants encounter HireVue and similar video interviewing systems frequently now, and not everyone is happy—they find the idea insulting and intrusive. One question at Ask the Headhunter:
[The questioner’s wife] landed two job interviews with hiring managers within three weeks. Suddenly, a personnel jockey injected himself into the ongoing discussions with the hiring manager. The recruiter insisted that my wife submit herself to a one-way, online digital video taping, answer a series of pre-selected “screening questions,” and upload it to who knows where for “further review and screening” by who knows whom.
She found the request creepy, impersonal, presumptuous, Orwellian, exploitative, voyeuristic, unprofessional, and perhaps even unethical. She declined, instantly prompting an automated “Do Not Reply” rejection e-mail. She was not worthy because she wouldn’t subject herself to a dehumanizing “HireVue Digital Video Interview.”
This new wrinkle in HR practices seems like the most unsettling and counterproductive yet. It not only removes access to the hiring manager, but also live, human interaction. It sounds like “HR pornography,” where perverted personnel jockeys huddle around a monitor to gawk at videos of “virtual job candidates,” picking apart perceived blunders while they screen you out.
The Headhunter, Nick Corcodillos, suggested the candidate respond in this situation by expressing a willingness to do a Skype interview with the hiring manager, cheaper (no payment to HireVue) and more personal. He suggests HR has an agenda in using such impersonal services: “What they mean is, we don’t want you to see the personalities of our personnel jockeys because, face it, they’re a bunch of data diddlers that we don’t want talking to anyone.” I’d say that is correct. In this case the applicant already spoke to the hiring manager, but HR is trying to force use of its process using HireVue for bureaucratic control reasons. If it should come to an EEOC complaint, having anyone escape their uniform process would be seen as evidence of favoritism having disparate impact on minorities.
There’s nothing wrong with these video interviewing services — ideally they substitute for expensive and time-consuming travel to meet with HR staff and hiring managers. But in practice, some companies now use them along with ATS screening techniques to completely depersonalize all but the last stages of hiring — the candidate does a lot of work, but no one at the company spends any time on their application at all until pre-screened and pre-interviewed. Meanwhile, candidates who contact hiring managers directly or run into them at professional functions or through work at companies in the same industry get the further advantage of being personally known in advance.
It does cost a lot to hire through HR — the arms race of HR automation leads to candidates using automation to contact far more potential employers, leading to avalanches of applications, leading to more ineffective automation. Hopeful noises about AI assisting are so far just that. In principle, AI could do a good job of analyzing resumes and interview videos and deliver the best candidates to hiring managers. In practice, no one is delivering anything more than hype.
Typical of the hype: HiringSolved, a startup promising Siri-like hiring assistance:
HiringSolved will soon unveil what it considers “Siri for recruiting,” an artificial intelligence assistant for recruiters. His name will be RAI, pronounced like the name Ray, and standing for “Recruiting Artificial Intelligence.”
The company has been working on it for five years, and is still perfecting it. The gist of it is you’d ask recruiting questions to a Chatbot-like system. So, instead of checking off a bunch of boxes, you’d type something like, “I need to find 10 female developers with experience using WordPress, within 10 miles of Milwaukee.” Or, perhaps, “What was the most common previous title of a systems engineer at Raytheon?”
Perhaps later, like with Siri, you’d use voice, not typed, commands.
HiringSolved’s RAI tool could also ask you follow-up questions, not unlike a conversation between a recruiter and a manager. If you, say, want a mechanical engineer, it might ask you to narrow your searches. Nuclear? Petroleum? Aerospace?
The idea is that the artificial intelligence will make you a better recruiter/sourcer, guiding you through questions that very experienced sourcers ask themselves in order to chop through a database and hone in on who they want.
Chatbots and Siri, soon to save the day! Smart employers will pay the price to hire good, connected recruiters who have personal contacts in the industry. AI may one day allow applicants to prove themselves worthy without human intervention, but that day is a long way off.
 “In Head-Hunting, Big Data May Not Be Such a Big Deal,” by Adam Bryant, New York Times, June 19, 2013. http://www.nytimes.com/2013/06/20/business/in-head-hunting-big-data-may-not-be-such-a-big-deal.html
 “The Science of Smart Hiring,” by Derek Thompson, The Atlantic, April 10, 2016: http://www.theatlantic.com/business/archive/2016/04/the-science-of-smart-hiring/477561/
 “How AI and recruiters will work together in the near future,” by Tess Taylor, HRDive, September 15, 2016. http://www.hrdive.com/news/how-ai-and-recruiters-will-work-together-in-the-near-future/426291/
 “Can Using Artificial Intelligence Make Hiring Less Biased?” by Sean Captain, Fast Company, May 18, 2016. https://www.fastcompany.com/3059773/the-future-of-work/we-tested-artificial-intelligence-platforms-to-see-if-theyre-really-less-
 “Video Job Interviews: Hiring for the Selfie Age,” by Dahlia Bazzaz, Wall Street Journal, August 16,2016. http://www.wsj.com/articles/video-job-interviews-hiring-for-the-selfie-age-1471366013
 “HR Pornography: Interview videos,” by Nick Corcodillos, Ask the Headhunter®, October 14, 2014. http://www.asktheheadhunter.com/7537/hr-pornography-interview-videos
 “A Cousin Of Siri Is Coming To The Recruiting Field,” by Todd Raphael, ERE Recruiting Intelligence, September 8, 2016. http://www.eremedia.com/ere/a-cousin-of-siri-is-coming-to-the-recruiting-field/
Death by HR: How Affirmative Action Cripples Organizations
[From Death by HR: How Affirmative Action Cripples Organizations, available now in Kindle and trade paperback.]
The first review is in: by Elmer T. Jones, author of The Employment Game. Here’s the condensed version; view the entire review here.
Corporate HR Scrambles to Halt Publication of “Death by HR”
Nobody gets a job through HR. The purpose of HR is to protect their parent organization against lawsuits… HR kills companies by blanketing industry with onerous gender and race labor compliance rules and forcing companies to hire useless HR staff to process the associated paperwork… a tour de force… carefully explains to CEOs how HR poisons their companies and what steps they may take to marginalize this threat… It is time to turn the tide against this madness and Death by HR is an important research tool… All CEOs should read this book. If you are a mere worker drone but care about your company, you should forward an anonymous copy to him.
More reading on other topics:
The Justice is Too Damn High! – Gawker, the High Cost of Litigation, and the Weapon Shops of Isher
Regulation Strangling Innovation: Planes, Trains, and Hyperloop
Captain America and Progressive Infantilization
FDA Wants More Lung Cancer
Corrupt Feedback Loops: Public Employee Unions