The traditional method of assessing candidates, say for MBA admissions or jobs, involve résumé screenings, job interviews and psychometric tests. However, an MIT Sloan initiative has found that a new generation assessment tools, involving Artificial Intelligence (AI) and robotics make the process easier and more precise.
Josh Bersin, a global industry analyst & founder of Bersin by Deloitte and Tomas Chamorro-Premuzic, the chief talent scientist at ManpowerGroup and professor of business psychology at University College London and Columbia University, explores these aspects in an article appearing in the MIT Sloan Management Review.
Explaining how technology is reshaping the practice of management, the authors say the challenge of finding the right people who would be able to face the challenges in an atmosphere of rapid technological advancement, needs increasing use of digital tools.
The new methods involve gamified assessments, digital interviews and candidate data mining.
Gamified assessments use a game like features such as real-time feedback, interactive and immersive scenarios and shorter modules, which make the test-taking more enjoyable. The test taker’s choices and behaviours are mined by computer-generated algorithms to identify suitability for a given role.
HireVue’s MindX employs gamified cognitive-ability tests by asking users to play games resembling Nintendo’s Brain Age that predict IQ. Pymetrics says it applies proven neuroscience games and cutting edge AI to reinvent the way companies attract, select and retain talent.
Arctic Shores, which is often used for evaluating college graduates, puts candidates through what feels like a series of 1990s arcade games and correlates their choices to standard personality traits and competencies.
As these types of tools are used more widely in high-volume hiring environments, tool providers gather enough data to demonstrate significant links between candidates’ scores on the games and their job performance.
Several companies are also designing their own gamified assessments, which they position at the interface between hiring and marketing. Red Bull’s Wingfinder, available to the general public, is used to attract candidates through the drink company’s social media channels. Candidates are provided with an extensive report on their strengths and weaknesses, regardless of whether they are formally considered for a position.
However, this approach to talent identification suffers from two disadvantages. The Study found that the more interesting and enjoyable the assessment experience, the less predictive it tends to be. Longer testing time is needed to get a comprehensive picture of the candidate’s background and time is the enemy of fun.
Secondly, there could be a significant rise in costs. Unlike drafting a standard Q&A type of self-report, creating an immersive game-like experience for candidates cost more money putting constraints on talent acquisition budgets.
Digital Interviews are another major development in talent identification. While it may resemble video conferencing, the interviewers/hiring managers can post their questions on the platform to create a structured (consistent and repeatable) interview protocol for stakeholders to use in their conversations with candidates, which helps them make fair, accurate comparisons.
Algorithms could be used to flag and interpret relevant talent signals (facial expressions, the tone of voice, emotions such as anxiety and excitement, language, speed, focus and so on), replacing human observations and intuitive inferences with data-driven sorting and ranking.
Research has long suggested that job interviews are most predictive when they are highly standardized — that is, when they put all interviewees through the same process and have a predefined scoring key to make sense of the answers, the Study says.
Another option is to “train” algorithms to ignore the signals that predict human bias but not job performance (such as gender, age, social class, and race). Tool providers like HireVue say they are doing this to eliminate such biases.
Thus, video interviews could increase the accuracy of the job interview findings while reducing costs and enabling hiring organizations to operate at scale.
However, such platforms are not entirely free from the human tendency to replicate and reinforce biases that are inherent to any interviewing process. If the people responsible for making hiring decisions are themselves biased, AI cannot overcome that issue.
One way to resolve the problem would be to focus less on individual traits and shift more towards group outcomes such as productivity numbers and revenues. For managers, 360-degree reviews can also be useful, because they crowdsource performance evaluations, mitigating individual biases.
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Another option is to “train” algorithms to ignore the signals that predict human bias but not job performance (such as gender, age, social class, and race). Tool providers like HireVue say they are doing this to eliminate such biases.
Another fast-growing area is that of passively mining candidate data and analyzing people’s digital footprints. Online behaviour can reveal information about individuals’ interests, personalities, and abilities, which in turn predicts their suitability for particular jobs or careers. For example, many hiring managers now investigate candidates’ reputation, followership, and level of authority on networking websites such as LinkedIn and Facebook, and they use that information to rate and rank people.
However, this capability raises privacy concerns. Organizations must think about how they’ll respect people’s privacy while getting the information they need to make smart hiring choices. Even if the boundaries between private and public life have eroded, it is ethical to ensure that people are aware of how their data is used.
