Theory of Machine 2.0: Artificial Versus Artificial Intelligence

Most research on augmented judgment and decision-making is human-centered. Specifically, Theory of Machine is a conceptual framework for describing and analyzing people’s lay theories about how human and algorithmic judgment differ. Reminiscent of the Theory of Mind, it conceptualizes the idea of ascribing thought processes or mental states to algorithms. However, based on their own perceptions, past personal experiences, and shaped by public media, people may conceive of humans and algorithms as functionally distinct ontological entities. Therefore, research on augmented judgment and decision-making should also focus on the differences between various algorithms in terms of (cognitive) abilities and behavior. In this article, several agendas for future research are proposed that explicitly consider people’s diverse interactions with various decision-support and artificial intelligence systems in their daily lives. Such primarily algorithm-centric research will help to gain insights into a more fine-grained Theory of Machine that also distinguishes between different levels of algorithmic fairness, transparency, and explainability. Ideally, a better understanding of how people mentalize about algorithmic behavior can also be used to improve algorithmic augmentations of human judgment and decision-making.