Even in its current state, AI is more precise and less biased than most human interviewers – but it is a low bar.
Job interview with futuristic Cyborg. It is an entirely 3D generated image.
Although it would be almost unthinkable For anyone who obtains a job without first being subject to a job interview, science on this subject is quite clear: there is very little valid and unique information that the maintenance provides, which could not be obtained through others, more reliable and predictive means (For example, scientific evaluations, IQ and Personality tests, past performance and even the IA crampon of people’s digital imprints).
In addition to that, there is too much Not relevant information and contrary to ethics The interview provides, even when humans are determined to ignore or claim Ignore such data: socioeconomic or social class of candidates, sex, ethnic and general appearance and physical attractiveness (One of the strongest biases, the most omnipresent, but rarely discussed).
This is why employers and job managers believe that it would be ridiculous to get rid of the interview: it provides them with so much information that they are supposed to ignore But “love” has, even when they ignore their prejudices.
Unsurprisingly, academic studies show that typical maintenance, which is not structured (more like an informal cat than a rigorous standardized interaction) and the amateur (conducted and evaluated by people with little or no technical training, and their own personal program or political interest), rarely explains for more than 9% variability in future professional performance. In fact, it is probably a overestimate: When the candidates’ hiring managers are in charge of both the selection interview, as well as ratings or assessments or assessments of candidates of candidates once they are hired in the role, there are a lot of invisible or invisible biases in the model.
For example, most managers will be reluctant to accept that they have chosen the bad candidate, so the best way to camouflage or hide their error is to positively assess their performances even when they are doing terribly (so it thinks about their choices). Likewise, even when managers do not know that they hired the bad person (because the candidate learned to Printing them or “false good” Once in the role), managers will always see them in a positive vein. In other words, the same biases that positively distorted the manager’s opinion during the interview will probably always be at stake in months or years later, especially if the manager is not excellent to assess the candidate’s real performance, and distracted by performative aspects of their professional performance (pretending to work, do politics, succeed and whistle skills with charisma and confidence, etc.).
Enter AI, which is far from perfect, especially with regard to evaluation and hiring, at least in its current state (we would expect it to improve as in most tasks and areas of subjects). That said, AI algorithms have been successfully deployed and sought after as a test and selection tools for candidates for years, long before AI is spreading with the generator and the Chatppt.
Certainly, on this date, the value of the AI in Evaluation, hiring, selection and identification of talents is mainly around improving speed, efficiency, consistency and cost, rather than precision. In this sense, its main contribution is in pre-deplery and high volume screening, since The quantity ends up increasing the quality (If you can examine 1 million candidates, you are more likely to end up with 10 incredible candidates than if you can only examine 1000), as well as the relief of recruiters from low value, boring and standardized routine: find keywords in a CV or CV, all job descriptions, or tons of cold candidates.
The more repetitive, boring and low value activities are outsourced at AI, The more time ago Human recruiters and job managers must really connect with candidates (in humans to humans). However, it is questionable to know if part of this time should really be devoted to human interviews to humans, or if the interview must simply be left to AI, including algorithmic deselection and the preselection of candidates. Consider some of the main advantages of well -designed IA -based interviews, by which I mean digital or fully automated video interviews and AI scores:
(1) Unlike humans, AI is very good to focus on relevant signals While ignoring unrelevant signals (including noise related to social class, demographic status and any information likely to reduce equity and candidates outside the group).
(2) Unlike humans, AI is much better to learn and unlearn, it can therefore continue to improve and refine its models, which makes them more predictive (especially if better quality performance data is used to refine them).
(3) Although AI can be biased, these biases Reflect human prejudices (When AI is “taught” to what an alleged performance looks like, it will reproduce or emulate prejudicial and biased preferences from humans). In fact, AI can never be biased in a really human way. In other words, unlike humans, AI will never have any fragile self -esteem it needs to protect or swell by dropping other humans (or AI). Perhaps in the future, we will see the AI evolving towards this type of neurotic or unsafe, but not at the moment.
(4) AI can predict more predictable and reliable and offer the same treatment and evaluation to all candidates. This is never the case for humans, even when the same panel of human interviewers is formed to assess or assess the same group of people, and experimental conditions are set up. As in a jury, human investigators have their own mood swings, preferences, bias and most are difficult to detect and manage.
(5) While we often attack AI to be “black box” (In the sense that even predictive models can be difficult to understand or decipher), regulations have done a good job by reducing the application of black box models, and most AI / algorithmic rating tools in recruitment and maintenance models are now “Blow Box” (in the sense that you can retro-engineer, models to understand why certain scores have been allocated or decisions “).
(6) Unlike AI, the human brain is Really a black box. Consider that even when human investigators really believe that a candidate is better than the others, they will never know why they really preferred them to others. They can have a story, including a story they tell each other, but we will never know if this story is true, or simply BS that they have told themselves (since each human is biased, and the mother of all prejudices is not aware of our prejudices, and treats them as facts when they are purely feelings about the facts at best).
(7) Many studies show that despite the low acceptance and popularity of AI, candidates often prefer AI to human interviewers, especially when they have had bad experiences with human interviewers. These may include (without limiting themselves): micro-aggressions, “macro” attacks (manifests), discrimination, harassment, arrogant or hostile treatment and pure indifference. This explains why, even in its current state, AI is a better alternative to many human interviewers, even if we measure this only in terms of Acceptance of candidates or user experience.
(8) If there is really a model binding the interview activity (verbal and non -verbal communication, language, speech, manners, social skills and content and style that make one candidate differs from others) with future professional performance, there is no doubt that AI will be better able to identify and detect this model than humans. In addition, he will also do so in a more coherent, cheaper and evolving way than humans. Note that even the most competent human interviewer (perhaps a very experienced clinical psychologist with decades of training and experience) will have days of leave, will feel from top to bottom due to personal events and will be affected by conscious and unconscious prejudices. In addition, it is not easy to find such experts and they can only manage a small number of interviews per day. Admittedly, a more important problem is that too many people think they are this expert when in fact they have no skills or expertise in the interviews.
(9) Meta-analytical data show that the only interviews that are in a coherent and substantial correlation with future professional performance and considerably assess the potential are strongly structured and standardized: In other words, they look much more like psychometric assessments than a typical conversation or to discuss between a human interviewer and a candidate. Again, this makes the interview perfectly suited to AI and computer intelligence rather than unpredictability and erratic human personality.
(10) Admittedly, the candidates earn a lot by meeting their recruitment manager, asking them questions and having an idea of their future bosses and the culture of the company (even if this is also a way to release or activate their own personal prejudices). There is no need to eliminate this, even if AI could do a better and more sincere job to describe a given culture (and soon also the boss) to candidates! However, this human cat to human and this opportunity for candidates to choose the right job, the boss and the right company could be offered once the candidates are already offered a job, and without any human intervention.
If all of this seems frightening or Orwellian, think of this analogy: AI interviews can be the recruitment equivalent of autonomous cars. They may not be entirely ready for mass consumption and adoption, and always produce accidents and errors in pilot mode (especially if there are other human drivers on the road); However, like autonomous cars or autonomous vehicles, it is unlikely that they will produce 1.3 million human deaths per year, as human drivers do today. And just as drivers tend to think that they are better than AI when they are not, most human interviewers think they are better than AI when they are not. AI does not need to be perfect to represent an improvement in relation to human intelligence, and in particular human stupidity.
