The Role Explainable Artificial Intelligence in Enhancing Auditor Judgment Quality in Indonesia
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Abstract
This study explores the role of Explainable Artificial Intelligence (XAI) in improving the quality of auditors’ decision-making in Indonesia. As AI systems become more prevalent in auditing practices, concerns regarding transparency and interpretability are increasingly relevant. XAI offers a solution by making AI-driven insights more understandable, thereby supporting professional judgment and reducing reliance on black-box systems. A quantitative approach was used, involving 100 professional auditors who completed a structured questionnaire based on a 5-point Likert scale. Data were analyzed using SPSS version 25. The findings revealed that XAI significantly influences auditors' decision-making quality, particularly in enhancing decision accuracy, risk assessment, and confidence in professional judgments. Regression analysis showed a strong positive relationship between XAI and decision-making quality, with XAI explaining 46.2% of the variance. These results highlight the importance of implementing explainable AI technologies to foster trust, accountability, and effectiveness in auditing practices across Indonesia.
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