Nowadays, a human resource manager spends a lot of time selecting the right staff for their organization. To see if the candidates fit the job description, sufficient and trustworthy information needs to be evaluated (Dipboye 2014). Managers increasingly question the credibility of the provided information by candidates on their CVs (Weiss, Feldman 2006). Artificial intelligence can come into play assisting human resource managers and there is even a possibility of making them obsolete in the future.
Unilever, a transnational consumer goods company, already started experimenting with staff selection assisted by artificial intelligence making resumes superfluous. The company starts by scanning LinkedIn profile data using an algorithm which drops half of the candidates (Thibodaux 2017). Subsequently, several games using artificial intelligence to assess and match candidates to the company have to be played. Finally, less than the top third submits a video interview focusing on business challenges (Gee 2017). All these steps combined will accelerate the human resource pre-selection phase without the intervention of humans. Furthermore, according to Unilever the hiring process has become more accurate as 80 percent of the applicants in the final round are offered a job (Gee 2017).
Although these developments in the selection procedure may seem revolutionary in a sense that the process becomes more accurate. Of course, the other side of the coin should also be considered. The use of artificial intelligence in the process also raises some questions concerning the legitimacy of the selection. Artificial intelligence is not entirely unbiased as it is basing its decisions on data provided by humans. Besides, is it ethical to exclude people based on a decision made by artificial intelligence? As more people are selected through this process the companies behind it should measure the effects. Think of cases where too many people with the same background are selected or it influences company performance negatively. As artificial intelligence has already made its way to the pre-selection phase of personnel in a big company like Unilever, people should start thinking of the consequences of using it.
Dipboye, R.L., 2014. The role of communication in intuitive and analytical employee selection, Meeting the challenge of human resource management: A communication perspective 2014, Routledge New York, NY, pp. 40-51.
GEE, K., 2017. In Unilever’s Radical Hiring Experiment, Resumes Are Out, Algorithms Are In. Dow Jones Institutional News.
THIBODAUX And WANDA, June 28th, 2017-last update, Unilever Is Ditching Resumes in Favor of Algorithm-Based Sorting. Available: https://www-inc-com.eur.idm.oclc.org/wanda-thibodeaux/unilever-is-ditching-resumes-in-favor-of-algorithm-based-sortingunilever-is-di.html?cid=search [27-09-, 2018].
Weiss, B. and Feldman, R.S., 2006. Looking good and lying to do it: Deception as an impression management strategy in job interviews. Journal of Applied Social Psychology, 36(4), pp. 1070-1086.
Hi Stef, thanks for uploading your post, it’s really good!
Your blog really makes me think of how a lot of processes in companies everywhere can still be improved with new technologies. I have to admit, I once needed to interview candidates and in hindsight am sure there were many biases that came into play there. It’s good to see how companies are already developing and using technologies to streamline their hiring process. It’s interesting though how AI is now used to do a very human thing – looking for a connection. How do they capture this so a candidate is sure to fit into the culture of the company?
Here in Rotterdam there is also a start-up that’s trying to take away bias in the hiring process. It’s actually owned by an RSM student, and I think you will be very interested in checking them out. The company is called Equalture, find their site here: https://www.equalture.com/en/about-us
Interesting that Unilever is already applying AI in its selection process and your post just reminded me one of the biggest challenges of using AI algorithms to replace mechanisms where there are inherent biases.
I believe one of the most interesting consequences of AI can be the biases the algorithm develops. There are several cases in which AI with time has appeared to be racists, sexists as a result of the biased datasets. In general it is only a problem over large aggregated data where the biases inherent in human behaviour appear. In general AI is not yet able to differentiate between ethical and non-ethical behaviour and it becomes a problem when they are asked to perform jobs where highly ethical conduct is necessary.
In order to make these processes transparent and fair the black box of AI should be further investigated. It is a dangerous move to let AI decide for us, when we are unsure about the decision process behind it.
So I would not totally agree with Stefan. AI could potentially prevent biases in the selection process, however, companies need to make sure they have eliminated the threats and closely monitor how AI is choosing the candidates.
In case you are interested here is an article about biased AI and its implications: https://www.forbes.com/sites/parmyolson/2018/02/26/artificial-intelligence-ai-bias-google/#6ee566c61a01
and another one about Microsoft trying to correct unethical behaviour of their chatbot: https://qz.com/1340990/microsofts-politically-correct-chat-bot-is-even-worse-than-its-racist-one/