Empirical Study on The Role of Explainable AI (XAI) in Improving Customer Trust in AI-Powered Products
Year: 2025
Type: article
Abstract: Explainable Artificial Intelligence (XAI) has emerged as a crucial element in fostering customer trust in AI powered products. As AI systems become increasingly embedded in daily life, the need for transparency, interpretability, and fairness in decision-making processes has gained prominence. This empirical study explores the role of XAI in enhancing customer trust across various industries, including healthcare, finance, and retail. By providing understandable explanations of AI decisions, XAI enables users to comprehend AI behavior, thus reducing skepticism and promoting acceptance. The research examines secondary data to analyze the correlation between XAI implementation and customer trust levels. Additionally, it discusses the challenges and opportunities in measuring trust and the emerging trends and future trajectories of XAI in AI product development. Key findings suggest that the integration of XAI significantly improves perceived control and user understanding, which in turn fosters a more positive relationship with AI systems. Despite challenges such as technological complexities and the need for standardized solutions, XAI holds the potential to build a more transparent and ethical AI landscape. This research emphasizes the importance of continued innovation in XAI technologies to address trust related concerns and facilitate broader adoption of AI-driven products. Future research should focus on developing standardized, universally accepted frameworks for XAI implementation to further enhance trust in AI applications.
Authors: Devendra Singh Parmar, Hemlatha Kaur Saran
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4408638269