Title: Transforming Multidisciplinary Research Through Artificial Intelligence
Chief Editors: Dr. Nagasudha R and Dr. Geetha V
Associate Editors: Dr. Aarti Sharma and Ms. P. Nithyashankari
Co-Editors: Dr. Madhumathi Reddim and Dr. Shishira Srinivasa
ISBN: 978-93-7183-019-5
Chapter: 19
DOI: https://doi.org/10.59646/804/19
Author: Esmita Gupta
Abstract
In recent years, there has been a growing realization that Explanatory Artificial Intelligence (XAI) plays a vital role in making enterprise decision-making systems transparent and accountable. The research paper highlights the importance of EAI in making decisions transparent in enterprise environments, focusing on interpretability, trust by employees and stakeholders, accountability, and the quality of organizational decisions. A mixed-method design is employed in the research, in which quantitative data on AI-supported decision-making and qualitative data from structured interviews are integrated. These findings suggest that organizations are increasingly implementing AI systems to support their recruitment processes, finances, risk management, customer management, and operational planning, despite concerns about the complexity of AI models, user understanding, data quality, and accountability for AI-driven decisions. Despite these difficulties, the Explainable AI approach helps to make the decisions made with AI more transparent, enabling users to understand the outputs, catch mistakes, and make informed choices. Furthermore, the acceptance and trust of the user are improved when they are informed of the “how” and “why” of the decisions made with the help of AI. The paper also introduces organizational strategies and governance mechanisms that can support the responsible implementation of Explainable AI such as training employees, documenting models, human oversight, suitable methods of explanation, and accountability mechanisms. The findings of this study demonstrate the value of Explainable AI in enhancing organizational decision-making processes, while maintaining transparency, fairness, and responsible use of AI technologies. The analysis is applicable to business managers, technology developers, policy makers, and organizational decision makers who want to deploy effective and comprehensible AI systems. The findings enhance the body of knowledge on responsible artificial intelligence by offering empirical knowledge of how to embed explainable AI into more extensive enterprise systems in a sustainable way.
Keywords: Explainable Artificial Intelligence, Transparent Decision-Making, Enterprise Systems, AI Explainability, Algorithmic Transparency, Trust in AI, Responsible AI, Interpretability, AI Governance, Organizational Decision-Making