Conceptualizing Responsible Adoption of Artificial Agents in the Workplace: A Systems Thinking Perspective

Following recent technological developments, organizations and businesses seek to improve their effectiveness by increasing the use of artificial agents in the workplace. Previous research suggests that humans react to the adoption of artificial agents in three ways: 1) some humans appreciate algorithmic advice (algorithm appreciation); 2) some humans oppose algorithmic advice (algorithm aversion); and 3) some humans fully relinquish control to artificial agents (automation bias). Using tools and methods form the field of systems thinking, we analyze the existing literature on human-machine interactions in organizational settings and develop a conceptual model that provides an underlying structural explanation for the emergence of algorithm appreciation, algorithm aversion, and automation bias in various contexts. In doing so, we create a powerful visual tool that can be used to ground discussions about the responsible adoption of artificial agents in the workplace and the long-term impact they cause for organizations and humans within them. We use the model to hypothesize possible behavioral outcomes produced by the proposed structure.