Envisioning Explainable AI
K. Chakrapani, Mohamed Iqubal Safa, Saranya Gangadhara Moorthy, Meenakshi Kumaraswamy, George Parimala · 2025
Artificial intelligence (AI) has permeated diverse sectors necessitating transparency in its decision-making processes. Explainable AI (XAI) emerged to address this need providing clarity into AI systems’ operations and bolstering user trust. This chapter explores XAI's transformative influence across industries like healthcare, finance, and autonomous systems. By elucidating core XAI concepts and methodologies, we uncover how XAI fosters transparency, aids decision-making in medicine, drives policy alterations, promotes fairness in evaluations, and ensures the safety of autonomous technologies like vehicles and drones. Additionally, we scrutinize the regulatory and disciplinary mechanisms implemented by XAI to safeguard privacy and ensure accountability. Despite challenges, XAI signifies a shift toward a future where AI systems are not only accountable and transparent but also harmonized with societal norms. Its potential lies in reshaping regulations, refining user experiences, and mitigating biases. As XAI advances, it holds the promise of a more transparent, equitable, and responsible AI landscape, where human–AI collaboration flourishes bolstering trust and acceptance in AI technologies.