ARTIFICIAL INTELLIGENCE IN TAX ADMINISTRATION

Approved

Relevance and Originality:

The Research Article is highly relevant as it explores the transformative impact of Artificial Intelligence (AI) on tax administration. The focus on AI technologies like machine learning and natural language processing to enhance efficiency, accuracy, and taxpayer services addresses a significant and contemporary issue in tax management. The originality of the study lies in its comprehensive overview of AI applications, such as fraud detection, automated compliance, and predictive analytics, which highlight the innovative ways AI can address longstanding challenges in tax administration.

Methodology:

The summary does not provide detailed information about the research methodology used in the study. To fully understand the research, it would be helpful to know how the impact of AI was assessed—whether through case studies, quantitative data analysis, or comparative evaluations of pre- and post-AI implementation. Details on the data sources, analytical techniques, and criteria for measuring the effectiveness of AI applications in tax administration would enhance the understanding of the study's methodology.

Validity & Reliability:

While the summary outlines several benefits and applications of AI in tax administration, it lacks specifics on how the validity and reliability of the findings were ensured. Information on how the data were validated, measures taken to ensure accuracy, and the consistency of AI tools across different scenarios would strengthen the study's credibility. Details on how representative the findings are of broader tax administration practices and how potential biases were addressed would also be beneficial.

Clarity and Structure:

The Research Article summary is clear and logically structured, effectively presenting the key applications and benefits of AI in tax administration. The description of how AI enhances various aspects of tax management, from fraud detection to personalized taxpayer services, is coherent. For improved clarity, the summary could include a more detailed breakdown of each AI application, including specific examples or case studies. Additionally, a summary of the research findings and their implications for tax authorities would enhance the overall structure.

Result Analysis:

The summary provides a general overview of the benefits of AI in tax administration but lacks specific results or empirical data. Including quantitative metrics, such as improvements in fraud detection rates, efficiency gains, or user satisfaction levels, would offer a more detailed result analysis. Discussing how AI-driven tools have been tested or validated in real-world tax administration scenarios and providing insights into their practical impact would strengthen the analysis of the research outcomes.