Model Fairness is Constrained by Decision Making Strategy Design

Recent years have seen a marked rise in the use of machine learning and AI in hiring. Concurrently, debates on the ethics of AI-enabled tools ensued: can these decision aids alleviate the biases prevalent in human choices, or do they further exaggerate unfairness? On the one hand, statistical tools can make evaluations more standardized. On the other, these models can provide biased estimates when trained on data that are unbalanced with respect to personal characteristics. Here, I present evidence that even prior to the data coming into play, a model's utility is first constrained by how the decision problem is formulated. Consider two hypothetical approaches to deciding how to target job advertisements: Company A builds a model to identify the candidates most similar to their previous applicants with the goal of maximizing the click through rate; Company B has been collecting performance measures for their previous hires and aims to target job ads to those candidates that are predicted to be high achievers. How do these approaches compare on long-term fairness? I conducted a simulation study to mimic a hiring pipeline and evaluate the two approaches on the tendency to propagate the group bias and on the resulting performance of selected candidates. The results suggest that targeting candidates based on their similarity to previous applicants, as in Company A's case, leads to an increase in the categorical group bias concurrent with a decrease in performance. I further found that this approach specifically disadvantages high performers in the underrepresented group. In contrast, Company B's decision to predict performance fares better: it does not affect the bias or performance. This demonstrates that how a company designs their decision-making strategy affects fair candidate evaluation and the resulting performance. Hiring is effortful, fair hiring is even harder, but the fruits of the labor are also greater: in fairness, in performance, and in the bottom line.