Explainable Artificial Intelligence for Executive Decision-Making: Enhancing Trust and Transparency in Management Information Systems

Abstract

AI has increasingly found its place in Management Information Systems (MIS), allowing organizations to process a huge amount of information and analyze it to find trends, predict business results, and enable decision-making. Yet, most AI technologies function as almost "black boxes," generating results without explaining the underlying logic behind them. This may lead to concerns about accountability, fairness, and ownership of decisions, hampering trust in the technology and limiting its acceptance in the company. Explainable Artificial Intelligence (XAI) solves these challenges through the provision of clear and intelligible accounts of the predictions and recommendations made by AI. This study investigates the significance of XAI in improving the executives’ decision-making process with greater transparency, trust, accountability, and the human–AI collaboration. By applying a conceptual and literature research methodology, the paper proposes a new MIS framework using XAI, where quality of data, transparency of model, relevance of the explanation, and human supervision advance the executives’ degree of trust and decision-making quality.The study claims that XAI must be regarded not only as a technical tool but as an organisational capability enabling managers to assess AI suggestions critically and act on them wisely. The research establishes the significance of user-centric explanations, governance systems, ongoing monitoring, and human responsibility. Findings indicate that explainable AI can boost managers’ confidence and enhance the quality, speed, and defensibility of strategic choices if the provided explanations are accurate, meaningful, easy to comprehend, and correlating with the goals of the organisation.