Building Ethical AI for Talent Management
Realistically, we have a greater ability to ensure both accuracy and fairness in AI systems than we do over recruiters and hiring managers. To do this, organizations using AI for talent management, at any stage, should start by taking the following steps. First, focus on designing systems designed to show causation over correlation. If a candidate has an attribute that is associated with success in a role, the organization needs to be able to explain the links between the two. Second, optimize for fairness and accuracy, instead of one over the other. Third, hold your company and developers accountable by allowing others to audit the tools being used to analyze their applications. Finally, follow the same laws — as well as data collection and usage practices — you use in traditional hiring.
Artificial intelligence has disrupted every area of our lives — from the curated shopping experiences we’ve come to expect from companies like Amazon and Alibaba to the personalized recommendations that channels like YouTube and Netflix use to market their latest content. But, when it comes to the workplace, in many ways, AI is still in its infancy. This is particularly true when we consider the ways it is beginning to change talent management. To use a familiar analogy: AI at work is in the dial-up mode. The 5G WiFi phase has yet to arrive, but we have no doubt that it will.
To be sure, there is much confusion around what AI can and cannot do, as well as different perspectives on how to define it. In the war for talent, however, AI plays a very specific role: to give organizations more accurate and more efficient predictions of a candidate’s work-related behaviors and performance potential. Unlike traditional recruitment methods, such as employee referrals, CV screening, and face-to-face interviews, AI is able to find patterns unseen by the human eye.
Many AI systems use real people as models for what success looks like in certain roles. This group of individuals is referred to as a “training data set” and often includes managers or staff who have been defined as “high performers.” AI systems process and compare the profiles of various job applicants to the “model” employee it has created based on the training set. Then, it gives the company a probabilistic estimate of how closely a candidate’s attributes match those of the ideal employee.
Theoretically, this method could be used to find the right person for the right role faster and more efficiently than ever before. But, as you may have realized, it has become a source of both promise and peril. If the training set is diverse, if demographically unbiased data is used to measure the people in it, and if the algorithms are also debiased, this technique can actually mitigate human prejudice and expand diversity and socioeconomic inclusion better than humans ever could. However, if the training set, the data, or both are biased, and algorithms are not sufficiently audited, AI will only exacerbate the problem of bias in hiring and homogeneity in organizations.
In order to rapidly improve talent management and take full advantage of the power and potential AI offers, then, we need to shift our focus from developing more ethical HR systems to developing more ethical AI. Of course, removing bias from AI is not easy. In fact, it is very hard. But our argument is based on our belief that it is far more feasible than removing it from humans themselves.
When it comes to identifying talent or potential, most organizations still play it by ear. Recruiters spend just a few seconds looking at a resume before deciding who to “weed out.” Hiring managers make quick judgments and call them “intuition” or overlook hard data and hire based on cultural fit — a problem made worse by the general absence of objective and rigorous performance measures.
