XAI4RE – Using Explainable AI for Responsible and Ethical AI
Avital Shulner Tal, Julia Sheidin · 2025
As Artificial Intelligence (AI) systems are increasingly integrated into high-stakes domains, the demand for transparency has become paramount. The opacity of "black-box" models poses significant challenges in trust, fairness, and accountability. Explainable AI (XAI) is a vital approach for addressing these concerns by enabling transparency, fostering trust, and ensuring ethical deployment across various sectors, including healthcare, human resources, finance, autonomous systems, and more. This paper explores how XAI methods can be used throughout the AI lifecycle for creating human-centered, ethical, and responsible AI systems by enhancing transparency, reducing bias, and protecting data privacy. Furthermore, the paper introduces XAI4RE, a theoretical framework that links XAI principles and purposes to concrete stages of the AI lifecycle, demonstrating how to address ethical considerations effectively. This approach involves engaging different stakeholders, such as developers, regulators, and users, at each stage. The framework highlights the critical role of XAI in promoting fairness, accountability, and human-centric design using general guidelines that discuss the relevant insights that can be drawn from XAI at each lifecycle stage. Ultimately, this paper underscores the importance of XAI in bridging the gap between technical advancements and ethical AI practices to foster societal trust and responsible systems.