BIASeD: Bringing Irrationality into Automated System Design
This is a theoretical proposal for enhancing AI-human interaction by addressing cognitive biases, but it is incremental as it builds on existing bias research without new empirical results.
The paper argues that AI systems should model and replicate human cognitive biases to improve human-machine collaboration, proposing a research agenda for integrating biases into AI design.
Human perception, memory and decision-making are impacted by tens of cognitive biases and heuristics that influence our actions and decisions. Despite the pervasiveness of such biases, they are generally not leveraged by today's Artificial Intelligence (AI) systems that model human behavior and interact with humans. In this theoretical paper, we claim that the future of human-machine collaboration will entail the development of AI systems that model, understand and possibly replicate human cognitive biases. We propose the need for a research agenda on the interplay between human cognitive biases and Artificial Intelligence. We categorize existing cognitive biases from the perspective of AI systems, identify three broad areas of interest and outline research directions for the design of AI systems that have a better understanding of our own biases.