Machine learning and human capital complementarities: Experimental evidence on bias mitigation

Abstract Research Summary The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic behavior by agents. We theorize that domain expertise of users can complement ML by mitigating this bias. Our observational and experimental analyses in the patent examination context support this conjecture. In the face of “input incompleteness,” we find ML is biased toward finding prior art textually similar to focal claims and domain expertise is needed to find the most relevant prior art. We also document the importance of vintage‐specific skills, and discuss the implications for artificial intelligence and strategic management of human capital. Managerial Summary Unleashing the productivity benefits of machine learning (ML) technologies in the future of work requires managers to pay careful attention to mitigating potential biases from its use. One such bias occurs when there is input incompleteness to the ML tool, potentially because agents strategically provide information that may benefit them. We demonstrate that in such circumstances, ML tools can make worse predictions than the prior technology vintages. To ensure productivity benefits of ML in light of potentially strategic inputs, our research suggests that managers need to consider two attributes of human capital—domain expertise and vintage‐specific skills. Domain expertise complements ML by correcting for the (strategic) incompleteness of the input to the ML tool, while vintage‐specific skills ensure the ability to properly operate the technology.