INTEGRATING MACHINE LEARNING AND HUMAN FACTORS: TOWARD ENHANCED USER-CENTRIC COMPUTING SYSTEMS

Abstract

The integration of Machine Learning (ML) and Human Factors
Engineering (HFE) is becoming increasingly critical in designing user-centric
computing systems. This paper explores the symbiotic relationship between these two
domains and presents a comprehensive framework for enhancing the usability and
performance of computing systems through ML algorithms, user behavior analysis,
and human-centered design principles. It examines how ML models can be employed
to adapt systems to diverse user preferences and cognitive abilities, thus improving
overall user experience and engagement. The paper also identifies key challenges
and opportunities in this integration, such as ensuring data privacy, managing
cognitive load, and addressing biases in ML models. Additionally, the role of HFE in
shaping the design of user interfaces and interaction mechanisms is discussed, with a
focus on creating adaptive, accessible, and efficient computing environments. The
proposed approach aims to foster systems that are not only technically robust but
also intuitive, personalized, and sensitive to human needs.