Automation as a future direction for recruitment?

Disruption demands innovation. Nevertheless, many HR players have so far shied away from using AI tools in recruitment. A high existing level of datafication in the recruiting process facilitates the introduction of emerging tech in theory and practice. The reluctance of HR management to experiment can, at worst, cost companies capable candidates. After all, when it comes to attracting talent, the professional application process is an underestimated linchpin. AI helps companies with high employee requirements in particular to quickly check applications and provide qualified feedback to job candidates. In times when talent is scarce on the job market, companies must not save resources by using AI at the expense of the candidate experience - the human factor remains an essential part of the application process. The following listing explains at which points in the recruitment process AI tools offer added value and which hurdles those in charge need to overcome.

Active Sourcing

Companies of all sizes are competing for skilled workers and young talent. Companies that simply publish job advertisements according to the "post-and-pray principle" and hope for applications will quickly be left behind by the competition. Medium-sized companies in particular activate enormous potential by approaching passive candidates. Here, sparring partner AI makes it easier for recruiting to identify the right people for current and future vacancies or to expand the talent pool.

Using CV parsing, artificial intelligence reads key data from online profiles. AI-based matching applications use specific search criteria to suggest talented individuals who largely match the requirements of a job profile. "Many of the conventional application platforms no longer deliver the desired results. Social platforms such as LinkedIn, on the other hand, quickly provide high-quality contacts. The potential pool of applicants in such networks is very large - AI filters the profiles and writes to people individually, tailored to their preferences," explains Edgar Ehlers, founder and managing director of the agile strategy consultancy ee factor. "The immense reach also brings growth to your own network as a positive side effect."

Initial contact with candidates

A chatbot generates the initial contact with candidates. Finding promising profiles and making real contacts through individual contact is easy work for the corresponding AI tools. Nevertheless, artificial intelligence still requires close monitoring: "Most AI solutions are new to the market and still prone to errors. I recommend that every user checks the results of the systems for accuracy in order to evaluate and eliminate errors as quickly as possible," says Ehlers. Many applicants have so far been critical of the use of AI in recruiting.

Data protection requirements

When it comes to personal data of job candidates, there are strict regulations that go beyond the usual data protection provisions. If artificial intelligence supports and simplifies the recruiting process, there should be clarity about the legal situation. This allows recruiters to recognize and avoid potential pitfalls at an early stage. Sound information on the function and training of the AI solution is necessary in order to assess legal certainty in advance. If HR teams process personal data with the help of AI, there must be a data protection authorization standard for each data processing operation. In addition, recruiters may only collect and process personal data that is necessary for the fulfillment of specific purposes in accordance with the principle of data minimization.

Risk of discrimination

Artificial intelligence has a positive impact on equal opportunities in the application process. Intelligent systems generally make decisions objectively and free of prejudice. However, in the increasingly dense jungle of algorithms, the self-learning systems hardly allow any conclusions to be drawn about the source of their results. Data analysis in the recruitment process, carried out by AI applications, therefore raises ethical questions. How did the result of a data processing procedure come about? Anyone who does not critically scrutinize the quality of the training data risks so-called vicarious discrimination. "If a group was overrepresented in previous data sets, the AI will continue to favor it in the future," explains agilist Ehlers. "If a company used to fill vacancies primarily with men, the system learns from the database to preferentially hire men - and consequently puts female candidates at a disadvantage," says the author of his first book "Digital Ethics", explaining the existing problem.

Source: www.ee-factor.de