The number of lies on resumes is increasing. Who is telling the truth and who is lying?

The number of lies on resumes is increasing and could be as high as 85%. An Office Team survey found that 46% of workers said they know someone who lied on a resume — a 25-point increase from its 2011 survey.[i] HireRight’s 2017 employment screening benchmark report (which surveyed nearly 4000 HR professionals) states that 85% of employers caught applicants lying on their resumes or applications, up from 66% five years ago.[ii]

Candidates for job interviews also lie. And some are very good at it. Can you determine when a job candidate is telling the truth? Are you better at identifying false statements than Artificial Intelligence?

I believe that candidates are telling me the truth when they give me straightforward answers, maintain eye contact, provide significant detail, are easy to follow, speak in the first person and do not use platitudes.

Now there is research that backs this up.

Research published in 2017 by Leadership IQ finds that high-rated applicants in their survey of 1,427 professionals used very different words than poorly-rated ones. For example, high performers used 21% more “I” language statements — such as such as “I,” “me,” or “my,” — than low performers. High performers also use 65% more “we” language — such as “we,” “us,” and “our.” High performers also use  38% more past-tense verbs. They typically tell simple, fact-focused stories that avoid distraction and detail. High performer answers contain 28% more words that describe positive emotions, such as “happy,” “thrilled,” and “excited.” High performer answers were 23% longer than low performer answers. They have more and better experience and attitudes, and they can give more detailed answers.

Leadership IQ learned that interview answers rated poorly by hiring managers contain very different words than interview answers rated highly.[iii]  For example, bad interview answers use the word “you” 392% more than good interview answers, and “they” 90% more.  Bad interview answers also contain 104% more present tense verbs, 40% more adverbs such as “really,” and “very,” 92% more negative emotions, and 103% more absolutes such as “always,” “absolutely,” and “unquestionably.”

In summary, if candidates are afraid to talk about themselves and their past experiences, it conveys the impression that they do not have the necessary experience to perform well on the job. When candidates use negative words, it can indicate a lack of self-control and an inability to positively resolve problems that arise. Absolutes can convey a lack of intellectual flexibility, the need to show off, or insecurity.

One caution about Leadership IQ’s study. It does not link interviewing performance to job performance.

What we do know from academic research is that structured interviews significantly improves interviewers’ ability to hire strong job performers because these interviews dig into past performance and skills.[iv] Empirically developed and validated assessment tests also have strong correlation to performance on the job.

Lazlo Bock, the former Chief Human Resources Officer at Google studied Google’s hiring practices and learned that there was NO correlation between Google’s interviews and job performance. It turns out Google during its early start up years was enamored with hypothetical problem solving questions such as brain teasers. They didn’t work. Bock convinced Google’s executives to stop the brain teasers, and introduced structured interviews and interviewer training to improve Google’s results[v].

I strongly recommend using structured interviews and assessment tests.

How about reading body language? Can you determine if a candidate is lying to you if they suddenly look at the floor or dart their eyes to the right? Change their tone of voice?

Research by the Economic and Social Research Council[vi] indicates that visual cues can be deceiving even to police officers who are trained in spotting dishonest behavior. While these visual cues may indicate lying, they are not 100% reliable. However, speech-related cues (such as changes in tone of voice, and the speed of speech) were more reliable.

One way I have learned to help with this is to ask a candidate about their strengths. They are happy to tell you.  Then ask them to tell you about their weaknesses. This question is always tougher to answer and is often nerve-racking as most people feel vulnerable about their weaknesses, especially during a job interview. Watch how their facial expressions, body language and speech-related cues change between the two questions. It will serve as a guide to you during the rest of the interview to alert you if a candidate is stressed out about a question, or potentially hiding the truth.

For example, with the weakness question, does the candidate, pause their speech, change their tone of voice, avoid eye contact? Does the candidate start to blink their eyes when before they didn’t? Do you observe other behaviors you did not see before such as moving of feet or wrapping them around the legs of the chair? Does the job candidate look away or turns his or her head away as if they are looking for something? Watch for the behavior displayed in the weakness question and see if it comes up in other questions. If it does, probe more.

Remember, it can be hard to determine if someone is lying based on visual cues. Research tells us that speech-related cues are more reliable. But some liars are pretty good at staying cool. That is why it is important to follow up on answers that you may doubt with more questions or in follow up interviews, especially after you have had time to further research the candidate’s work history. The difficulty in reliably spotting liars also reiterates the importance of doing background and reference checks.

Does face recognition technology work better at identifying job candidates who are lying?

Face recognition technology is taking the security industry by storm. It is being deployed to identify criminals and potential terrorists. Apple now uses it to replace the four-digit security code on its phones. The retail industry is using it to identify high volume customers and to alert the shop clerk about their preferences.

Can it help recruiters hire better job candidates, hire for better culture fit, or tell if someone is lying?

HireVue is one company using Artificial Intelligence to develop face-recognition technology and to compare the facial expressions, word use, tone of voice, vocabularies, and body movements with your best workers.[vii] HireVue maintains on its website that it doesn’t want to replace recruiters; it’s aim is to make the job interview process more efficient. The company says that its customers are able to remove steps like resume reviews, phone screens, and traditional assessments from their recruiting processes.

But is face recognition technology reliable for recruiting? Yes, if the man in the image is white, at least according to M.I.T. Medic Lab. But the darker the skin of the individual the more errors rise, up to nearly 35% for images of darker skinned women.[viii]

According to a 2017 Talent Economy article, HireVue claims that its prehiring assessment considers the potential for bias.[ix] HireVue’s algorithm is programmed to factor out things like gender, age and ethnicity. For example, if all successful sales representatives had red hair, algorithms would be adjusted to neutralize that characteristic to ensure it wasn’t a criterion for success

HireVue customers have completed close to 1 million assessments since the company started selling the service four years ago. In a promising development, HireVue maintains that its own customer base has become more diverse thanks to AI, but the company hasn’t polled its entire user base to see if the same holds true across the board.

It appears AI is only as accurate as the data used to train it. If the data base is predominately white or male, it will be more accurate with white people and males.

Can you or AI determine if a job candidate is lying? Trained interviewers using structured interviewing techniques and who watch for speech-related as well as verbal cues, and then probe with more questionswill do better. Assessment tests and thorough background and reference checks helps verify the veracity of candidate claims. AI technology as it develops and looks to control existing biases will prove helpful. Stay tuned.

Victor Assad is the CEO of Victor Assad Strategic Human Resources Consulting and is a Managing Partner of InnovationOne. He consults and provides “hands-on” support for innovation, global talent strategies, developing agile leaders and teams, and other strategic initiatives. Visit https://victorhrconsultant.com/ to learn more.

 

[i] Meredith Lepore (August 18, 2017), “This is How Many People Are Lying on Their Resume,” LEVO. Found at https://www.levo.com/posts/this-is-how-many-people-are-lying-on-their-resume.

[ii] “2017 Employment Screening Benchmark Report,” HireRight. Found at https://www.hireright.com/benchmarking.

[iii] Mark Murphy (Sept. 9, 2017), “Study: Words That Cost You The Job Interview,” Leadership IQ. Found at https://www.leadershipiq.com/blogs/leadershipiq/study-words-that-cost-you-the-job-interview.

[iv] Frank L. Schmidt and John E. Hunter. (1988) “The Validity and Utility of Selection Methods in Personnel Psychology: Practical and Theoretical Implications of 85 Years of Research Findings.” Psychological Bulletin, Vol 124, No. 2, 262-274. Copyright 1998 by the American Psychological Association, Inc. 0033-2909/98/.

[v] Work Rules, Lazlo Bock, (2017)

[vi] “New Interview Technique Could Help Police Spot Deception,” Economic and Social Research Council. June 8, 2007. Science Daily. Found at https://www.sciencedaily.com/releases/2007/06/070607062917.htm.

[vii] Monica Torres (Aug. 25, 2017), “New app scans your face and tells companies whether you’re worth hiring,” Ladders. Found at https://www.theladders.com/career-advice/ai-screen-candidates-hirevue.

[viii] Steve Lohr (Feb. 9, 2018), “Facial Recognition Is Accurate, if You’re a White Guy,” The New York Times. https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html.

[ix] Michelle Rafter (Dec. 14, 2017), “For Better or Worse, Artificial Intelligence for Talent Management Has Arrived,” Talent Economy. Found at http://www.talenteconomy.io/2017/12/14/artificial-intelligence-talent-management/?utm_campaign=Talent%20Economy%20Q4%202017&utm_source=hs_email&utm_medium=email&utm_content=59514879&_hsenc=p2ANqtz-_jmygjC65vHcmP-ysohAN7i5hP0_dNlGQ0nENBlSYdLPPWU50M_CK7nlOFFdpFypqJLqOyAyX5zdV2sTRHaY639QfSAw&_hsmi=59535262.

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