EXPLAINABLE AND ETHICAL ARTIFICIAL INTELLIGENCE IN SOFTWARE ENGINEERING: A REVIEW OF RECENT ADVANCES

Abstract

As AI continues its integration into software development, it has made software development more automatic, intelligent and data-driven throughout the Software Development Life Cycle (SDLC). With the increasing use of AI-based methods, however, there are significant concerns of transparency, trust, fairness, and accountability. Explainable Artificial Intelligence (XAI) and Ethical AI have thus become vital pillars to build on for trustworthy AI software engineering. In this review paper, we provide a structured synthesis of literature on explainable and ethical Artificial Intelligence in Software Engineering (AISE) from 2020 to 2025. AI in software engineering literature is explored from two main perspectives: (i) Explainable AI in Software Engineering and (ii) ethical frameworks for AI and how they are applied in software engineering. It highlights the key areas of dominance such as defect prediction, testing, maintenance, etc., and key limitations like the lack of consistent evaluation of explanations, lack of coverage of early SDLC phases, and gaps between ethical principles and their operational implementation. The results underscore the important relationship between explainability and ethical AI and suggest developer-centric, integrated, lifecycle-aware approaches. This review discusses the trends, challenges, and gaps in the current research and proposes future directions for trustworthy and responsible AI in software engineering.