As artificial intelligence continues to grow and gain a unique presence, many people will choose to embrace the new technology while others may feel turned off by the idea. Like the saying goes: if it isn’t broken, don’t fix it. With that in mind, it is common for people to reject technological advancements that they feel are foreign to them or otherwise “unnecessary”. But think of it this way–AI should not be thought of as the sole solution to a problem at hand, but rather it acts as an aid to the solution. In the case of hiring practices, AI has the potential to break down the natural biases that employers have when finding candidates.
The argument against artificial intelligence in hiring is that it takes away from the human element of interacting with candidates. The ability for employers to build relationships and directly network with potential candidates is advantageous, specifically when receiving referrals. Machine learning is unable to connect with candidates on this level but it is able to sort and predict quality candidates without any preconceived biases. As a result, AI coupled with a strong hiring team can result in more consistent and accurate placements.
In order for AI to learn to function without biases, it is imperative to have great data. AI is only as strong as its data so weak data can cause error and malfunction. We’ve seen AI malfunction in the way self-driving cars have made traffic infractions or even caused accidents. Additionally, it’s important to have strong data because AI is capable of actually learning human biases and implementing them in a more intense fashion. For AI to be effective in hiring, it must be given data that is diverse so that it does not always produce the same type of result (i.e. the same pool of candidates).
Overall, AI has a lot to offer in hiring and is a worthy resource for employers to adopt into their own practices. Although there is risk involved in using this technology, the advantages outweigh the disadvantages when using the right data. After all, AI can learn to remove bias when operating unlike humans.