PERSONALIZED EXPLAINABILITY REQUIREMENTS ANALYSIS FRAMEWORK FOR AI-ENABLED SYSTEMS

Abstract Artificial Intelligence (AI) has evolved into an indispensable technology that assists humans in making better decisions through predictive analysis and personalized recommendations in numerous sectors. However, complex machine learning (ML) models become less transparent and may recommend incorrect decisions, which leads to a loss of confidence and trust. Consequently, explainability is considered a key requirement of AI-enabled systems. Recent studies focus on implementing explainable AI (XAI) techniques to improve the transparency and trustworthiness of ML models. However, analyzing the explainability requirements of different stakeholders, especially non-technical stakeholders for AI-enabled systems, remains challenging. It lacks a comprehensive and personalized requirements analysis process that investigates the risk impact of outcomes produced by ML models and analyzes diverse stakeholder needs for explanations. This research proposes a framework with a requirement analysis that includes four key stages: (1) domain analysis, (2) stakeholder analysis, (3) explainability analysis, and (4) translation and prioritization, to analyze the personalized explainability needs of four types of stakeholders (i.e., development team, subject matter experts, decision makers and affected users) for AI-enabled systems. As demonstrated by the case study, it is feasible to apply the proposed framework to analyze diverse stakeholders' needs and define personalized explainability requirements for AI-enabled systems effectively.