Automation and related technologies can now augment more tasks than ever and are being used to assist in the production of new knowledge. Although prior literature has started to explore the role of automation in knowledge production, our understanding of the mechanisms through which automation influences knowledge production remains incomplete. We address this gap by studying the impact of automation on gender bias in the evaluation of knowledge workers’ inputs. Building on the literature on rational inattention, we argue that automation can mitigate gender bias in the evaluation of knowledge by assisting evaluators in overcoming explicit costs and opportunity costs associated with information processing. Utilizing data on open source software project collaboration on GitHub, we find that automating the contribution evaluation process is correlated with a higher acceptance rate for contributions from female contributors. Moreover, this effect is magnified when the knowledge contribution contains numerous elements and when evaluators are evaluating multiple contributions simultaneously.
