Events

Mr. Jong Sig (Sik) Chung
Ph.D. Candidate
McCombs School of Business
The University of Texas at Austin

Human-algorithm augmentation is becoming increasingly common in organizations, where algorithmic assistance during mundane tasks frees up humans to perform other high-level tasks. While augmentation literature generally predicts a synergistic integration between humans and algorithms, this perspective often overlooks potential negative spillover effects of algorithmic assistance on human performance. Focusing on the autonomy of algorithms that operate without human involvement, this study argues that it may stifle interactions among humans, an essential source of human learning. Specifically, I examine the introduction of autonomous algorithms to assist humans in evaluating each other’s contributions in knowledge-intensive projects, a process traditionally conducted through human interaction and discussion. As manual evaluation is resource-intensive, many projects have adopted algorithms to assist human’s evaluation. Using a stacked cohort generalized Difference-in-Differences design and data about software development projects on GitHub, the study finds that although the adoption of Continuous Integration (CI) bot within a project—algorithms that assist evaluation—reduce the burden of developer’s evaluation within the project, it also decreases interaction among developers during evaluation. This leads to a decline in developer’s performance in two essential tasks: monitoring fatal bugs and searching for new features within the project. Plus, it increases total un-resolved fatal problems within the project, possibly due to reduced developer’s performance. Lastly, I find that the benefits and pitfalls of the bot adoption magnify when the project receives contributions from diverse knowledge domains. These findings challenge prevailing assumptions about human-algorithm augmentation, highlighting the need for a balanced approach to integrating algorithmic assistance.