How many times, 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 completely opposite? 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. the ways through which our brain, in a more or less conscious way, distorts reality. These judgments or prejudices are even more important for those who deal with Human Resources and must evaluate people and situations, which are often decisive. Let’s try to better understand what biases are, what their impact is on personnel selection and how Artificial Intelligence can help us to avoid certain judgments or prejudices.
As we have already mentioned, biases are distortions of our mind. Actually it seems that there are 200 types of bias due to the fact that, despite what we think, we are not rational beings but we tend to rationalize. And in doing so we do not always follow processes suitable to really understand reality and especially to make correct decisions. We talk about cognitive bias because these errors are very much related to thought processes and therefore to our knowledge.
A cognitive bias is for example the one defined 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 go on and on, but even from these first hints you can see how these cognitive biases can have a huge impact on the recruitment process.
As we know, the search and selection of personnel is a decision-making process that leads to choose some people instead of others, to put in place certain activities, to act 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, in fact, 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 as well as the way of 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 large 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, physical abilities, while secondary biases have to do with our attitude, income, education, geographic location, family background and so on. 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 related to cognitive bias in the recruitment world. It is precisely because of these biases, which also affect HR professionals, that blind recruiting and the use of 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 the way data is collected, implemented and used to avoid perpetuating the same judgments and leading to the same errors.
In fact, 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, for example related to 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 being 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 as well as allow for the evaluation of the entire pipeline of candidates and this because it can process a large amount of data. Its use can avoid that whoever is in charge of HR suspends 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 in fact automate the CV screening and selection process. This has a double effect, both in terms of time – recruiters have more time to devote to empathic assessment, paying attention to what has already been selected – and to reduce cognitive bias.
All this is possible thanks to Inda, which allows to analyze and interpret data, thus optimizing the recruiting process. And that’s not all: Inda also has the ability to self-learn which leads to limit errors and specialize according to the needs of different organizations.
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