Diversity of Perception in Human-AI Collaboration

Abstract

Two key approaches to building AI systems are Model-Centric AI (MC-AI) and Data-Centric AI (DC-AI). When AI systems are deployed in real-world environments, they become part of a socio-technical ecosystem, interacting with humans, processes, and other systems. This interaction often occurs in hybrid teams, where humans and AI collaborate to achieve shared objectives. However, human influences, at any stage, can lead to suboptimal outcomes, such as model drift or reduced performance. In fact, human introduces variability, as personal experience, biases, and decision-making approaches can significantly impact outcomes. Changing one human in the process can alter the results dramatically. This paper review processes involved into building, deploying, monitoring, and maintaining AI-systems and discusses human influences at each step, the potential risks that may arise and the main skills necessary to avoid human’s negative influences. By incorporating perception diversity and tolerating ambiguity, the computing-with-perception framework enhances human-AI collaboration, enabling systems to manage complexity and ambiguity in human-AI collaboration considering real-world problems.

Keywords: Human-AI, Collaboration, Perception, Diversity, Decision-Making

DOI: 10.54941/ahfe1005929