How often, starting from an initial opinion of which you are particularly convinced, have you given space only to sources that confirm it without evaluating or even putting aside those opposites? We are sure that you have often acted this way, although you think you are a person with a critical spirit who can be objective and listen to everything. But you are not the only one: what we have just mentioned is an example of bias, confirmation bias, which is nothing more than a shortcut that our mind uses to validate what it believes (hence a judgment) instead of questioning it. Creating all the conditions for this to happen. Confirmation bias is just one of the many cognitive biases, i.e., how our brain distorts reality more or less consciously. These judgments or prejudices are even more critical for those who deal with Human Resources and must evaluate people and situations, which are often decisive. Let’s understand better biases, their impact on personnel selection, and how Artificial Intelligence can help us avoid certain judgments or prejudices.
As we have already mentioned, biases are distortions of our minds. There are 200 types of bias because, despite what we think, we are not rational beings but tend to rationalize. And in doing so, we do not always follow processes suitable to understand reality and make correct decisions. We talk about cognitive bias because these errors are related to thought processes and, therefore, to our knowledge.
A cognitive bias is, for example, the one defined by the Bandwagon Effect, whereby people tend to believe the most popular and common beliefs over the less popular ones. Another cognitive bias is the Courtesy Bias, for which when we express ourselves, we tend to limit our disappointment or displeasure to avoid offending the person with whom we are interacting. The list could continue, but even from these first hints, you can see how these cognitive biases can significantly impact the recruitment process.
As we know, the search and selection of personnel is a decision-making process that leads to choosing some people instead of others, putting in place certain activities, and acting in a certain way instead of another.
Making hasty choices can therefore prove counter productive not so much and not only for the candidate you bring on board but for the entire business.
We know how the HR function is increasingly strategic, especially in a world like the present one in which many certainties have crumbled, people have changed, and so has the way of working and conceiving work.
In all of this, bias can play an important, if not decisive, role and affect the success of the various recruitment actions. In addition, there is the fact that each selection process has a cost and involves a significant expenditure of time and resources.
What are some of the most common biases? There are several types: primary and secondary. Primary biases are more related to age, ethnicity, sexual orientation, gender, race, and physical abilities. In contrast, secondary biases concern our attitude, income, education, geographic location, family background, etc. All of this can then translate, even more concretely, into various errors of judgment.
Here are a few:
Those just mentioned are just some of the effects of cognitive bias in recruitment. It is precisely because of these biases, which also affect HR professionals, that blind recruiting and anonymous CVs can be a way of assessing candidates without any a priori considerations, and this leads us to another word, or rather two: Artificial Intelligence. What can Artificial Intelligence do in all this? And is it always helpful?
The opportunities are twofold: on the one hand, Artificial Intelligence can be used to identify and reduce the effect of bias; on the other hand, it can be used to improve the same Artificial Intelligence systems that, as we know, are always a creation of human beings and therefore fallible.
For example, one can exploit how data is collected, implemented and used to avoid perpetuating the same judgments and leading to the same errors.
AI can help reduce the subjective interpretation of data, and this is because machine learning algorithms learn to consider only those variables that improve their predictive accuracy. Algorithms can then help minimize those primary biases we mentioned, such as race or background.
Decisions that are made by AI can be evaluated, scrutinized and questioned whereas when people are the ones making them, the intrinsic motivations are often not visible. And figuring out why they came to do one thing instead of another involves a lot of analytical work that not everyone can afford to do.
From the recruiting point of view, if we can work on the equity of data collection and improve the process, the advantages can be many; it is understood that human intelligence must work alongside artificial intelligence because the latter can never replace the former.
AI in recruitment can, therefore, minimize unconscious biases, as happens with the aforementioned blind recruiting, and allow for the evaluation of the entire pipeline of candidates because it can process a large amount of data. Its use can prevent whoever is in charge of HR from suspending the selection process before what he had established because he thinks he can’t do the screening of all the CVs received. Applying Artificial Intelligence to ATSs (Applicant Tracking Systems) can automate the CV screening and selection process. This has a double effect in terms of time – recruiters have more time to devote to empathic assessment, paying attention to what has already been selected – and reducing cognitive bias.
All this is possible thanks to Inda, which allows data analysis and interpretation, thus optimizing the recruiting process. And that’s not all: Inda can self-learn, which leads to limited errors and specialize according to the needs of different organizations.