Human Factors Engineering in Explainable AI: Putting People First

Abstract

This paper examines the integration of human factors engineering into Explainable Artificial Intelligence (XAI) to develop AI systems that are both human-centered and technically robust. The increasing use of AI technologies in high-stakes domains, such as healthcare, finance, and emergency response, underscores the urgent need for explainability, trust, and transparency. However, the field of XAI faces critical challenges, including the absence of standardized definitions and evaluation frameworks, which hinder the assessment and effectiveness of explainability techniques. Human factors engineering, an interdisciplinary field focused on optimizing human-system interactions, offers a comprehensive framework to address these challenges. By applying principles such as user-centered design, error management, and system adaptability, human factors engineering ensures AI systems align with human cognitive abilities and behavioral patterns. This alignment enhances usability, fosters trust, and reduces blind reliance on AI by ensuring explanations are clear, actionable, and tailored to diverse user needs. Additionally, human factors engineering emphasizes inclusivity and accessibility, promoting equitable AI systems that serve varied populations effectively. This paper explores the intersection of HFE and XAI, highlighting their complementary roles in bridging algorithmic complexity with actionable understanding. It further investigates how human factors engineering principles address sociotechnical challenges, including fairness, accountability, and inclusivity, in AI deployment. The findings demonstrate that the integration of human factors engineering and XAI advances the creation of AI systems that are not only technologically sophisticated but also ethically aligned and user-focused. This interdisciplinary synergy is a pathway to develop equitable, effective, and trustworthy AI solutions, fostering informed decision-making and enhancing user confidence across diverse applications.