4 Things to Consider Before You Start Using AI in Personnel Decisions
Which candidate should we hire? Who should be promoted? How should we choose which people get which shifts? In the hope of making better and fairer decisions about personnel matters such as these, companies have increasingly adopted AI tools only to discover that they may have biases as well. How can we decide whether to keep human managers or go with AI? This article offers four considerations.
The initial promise of artificial intelligence as a broad-based tool for solving business problems has given way to something much more limited but still quite useful: algorithms from data science that make predictions better than we have been able to do so far.
In contrast to standard statistical models that focus on one or two factors already known to be associated with an outcome like job performance, machine-learning algorithms are agnostic about which variables have worked before or why they work. The more the merrier: It throws them all together and produces one model to predict some outcome like who will be a good hire, giving each applicant a single, easy-to-interpret score as to how likely it is that they will perform well in a job.
No doubt because the promise of these algorithms was so great, the recognition of their limitations has also gotten a lot of attention, especially given the fact that if the initial data used to build the model is biased, then the algorithm generated from that data will perpetuate that bias. The best-known examples have been in organizations that discriminated against women in the past where job performance data is also biased, and that means algorithms based on that data will also be biased.
So how should employers proceed as they contemplate adopting AI to make personnel decisions? Here are four considerations:
1. The algorithm may be less biased than the existing practices that generate the data in the first place. Let’s not romanticize how poor human judgment is and how disorganized most of our people management practices are now. When we delegate hiring to individual supervisors, for example, it is quite likely that they may each have lots of biases in favor of and against candidates based on attributes that have nothing to do with good performance: Supervisor A may favor candidates who graduated from a particular college because she went there, while Supervisor B may do the reverse because he had a bad experience with some of its graduates. At least algorithms treat everyone with the same attributes equally, albeit not necessarily fairly.
2. We may not have good measures of all of the outcomes we would like to predict, and we may not know how to weight the various factors in making final decisions. For example, what makes for a “good employee”? They have to accomplish their tasks well, they also should get along with colleagues well, fit in with the “culture,” stay with us and not quit, and so forth. Focusing on just one aspect where we have measures will lead to a hiring algorithm that selects on that one aspect, often when it does not relate closely to other aspects, such as a salesperson who is great with customers but miserable with co-workers.
