Fair AI in Government Tax Compliance
Keywords:
Responsible Artificial Intelligence, Tax Analytics Systems, Government AI Governance, Ethical AI Frameworks, Tax Compliance Automation, Big Data Analytics In Taxation, Fraud Detection Technologies, Pricing Optimization Analytics, Performance Assessment Models, Transparency And Explainability, Accountability In AI Systems, Regulatory Compliance In Taxation, Equitable Tax Regimes, Public Sector AI Applications, International Data Sharing Agreements, Tax Administrative Data Management, Bias Mitigation In AI, Data Ownership And Stewardship, Secure AI Outputs, Trustworthy Government Analytics.Abstract
Artificial intelligence (AI), mixed with advanced big data analytics methods, has the potential to play a pivotal role in enhancing efficiency and accuracy in multiple tax processes, from data management and fraud detection to pricing optimisation and performance assessments. However, the use of these new technologies must respect the ethical principles of transparency, accountability, and explainability in order to secure their acceptance by both internal and external stakeholders, ensure compliance with increasingly stringent financial regulations and well-founded tax decisions, and ultimately facilitate a more equitable tax regime. A Responsible AI framework is articulated for an area typically overlooked in the discussion of the ethical use of AI in other fields: its application in government analytics and compliance systems for tax agencies, natural and legal persons subjected to taxation, and internationally coordinated data-sharing agreements.
These institutions frequently combine their own datasets with information sourced externally through digital interceptions, companies and organisations mandated to carry out withholdings or disseminate internationally belonging to third parties, data of a fiscal nature of a different nature that guarantees compatibility in the absence of a tax treaty, and other types of financial data. Given the sensitive nature of tax-administrative data and the legal obligations in force for tax agencies, the AI systems developed should respond adequately to the three dimensions of Responsible AI: securing ownership of data and results; avoiding biases during the design stage and in the response stage; and guaranteeing control and security of the systems' outputs.
References
1. Levy, K., Chasalow, K. E., & Riley, S. (2021). Algorithms and decision-making in the public sector. Annual Review of Law and Social Science, 17, 309–334.
2. Mitchell, S., Potash, E., Barocas, S., D’Amour, A., & Lum, K. (2021). Algorithmic fairness: Choices, assumptions, and definitions. Annual Review of Statistics and Its Application, 8, 141–163.
3. Zuiderwijk, A., Chen, Y.-C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3), 101577.
4. Mangalampalli, B. M. (2024). Transparent Intelligence Explainability Frameworks for AI-Driven Clinical Decision Support in Healthcare Business Intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10566-10579.
5. Wirtz, B. W., Langer, P. F., & Fenner, C. (2021). Artificial intelligence in the public sector—A research agenda. International Journal of Public Administration, 44(13), 1103–1128.
6. Vetrò, A., Torchiano, M., & Mecati, M. (2021). A data quality approach to the identification of discrimination risk in automated decision-making systems. Government Information Quarterly, 38(4), 101619.
7. Schiff, D. S. (2021). Assessing public value failure in government adoption of artificial intelligence. Public Administration, 99(3), 498–516.
8. Pessach, D., & Shmueli, E. (2022). A review on fairness in machine learning. ACM Computing Surveys, 55(3), Article 51, 1–44.
9. Zhou, N. (2022). Bias, fairness and accountability with artificial intelligence and machine learning algorithms. International Statistical Review, 90(3), 468–480.
10. John-Mathews, J.-M., Cardon, D., & Balagué, C. (2022). From reality to world: A critical perspective on AI fairness. Journal of Business Ethics, 178, 945–959.
11. Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30 (4), 1011–1027.
12. Wang, X., Zhang, Y., & Zhu, R. (2022). A brief review on algorithmic fairness. Management System Engineering, 1, Article 7.
13. Ahn, M. J., & Chen, Y.-C. (2022). Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Government Information Quarterly, 39(2), 101664.
14. Gesk, T. S., & Leyer, M. (2022). Artificial intelligence in public services: When and why citizens accept its usage. Government Information Quarterly, 39(3), 101704.
15. van Noordt, C., & Misuraca, G. (2022). Artificial intelligence for the public sector: Results of landscaping the use of AI in government across the European Union. Government Information Quarterly, 39(3), 101714.
16. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. Zenodo.
17. Grimmelikhuijsen, S. (2023). Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making. Public Administration Review, 83(2), 241–262.
18. Alon-Barkat, S., & Busuioc, M. (2023). Human–AI interactions in public sector decision making: “Automation bias” and “selective adherence” to algorithmic advice. Journal of Public Administration Research and Theory, 33(1), 153–169.
19. Madan, R., & Ashok, M. (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 40(1), 101774.
20. Wang, G., Guo, Y., Zhang, W., Xie, S., & Chen, Q. (2023). What type of algorithm is perceived as fairer and more acceptable? A comparative analysis of rule-driven versus data-driven algorithmic decision-making in public affairs. Government Information Quarterly, 40(2), 101803.
21. DAVULURI, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking. Kurdish Studies.
22. Shi, B., Dong, B., Xu, Y., Wang, J., Wang, Y., & Zheng, Q. (2023). An edge feature aware heterogeneous graph neural network model to support tax evasion detection. Expert Systems with Applications, 213, 118903.
23. Belahouaoui, R., & Attak, E. H. (2024). Digital taxation, artificial intelligence and Tax Administration 3.0: Improving tax compliance behavior—A systematic literature review using textometry (2016–2023). Accounting Research Journal, 37(2), 172–191.
24. Aoki, N., Tatsumi, T., Naruse, G., & Maeda, K. (2024). Explainable AI for government: Does the type of explanation matter to the accuracy, fairness, and trustworthiness of an algorithmic decision as perceived by those who are affected? Government Information Quarterly, 41(4), 101965.
25. Haesevoets, T., Verschuere, B., Van Severen, R., & Roets, A. (2024). How do citizens perceive the use of artificial intelligence in public sector decisions? Government Information Quarterly, 41(1), 101906.
26. Kolla, S. K. (2024). Clinical Knowledge Intelligence through Deep Learning and Natural Language Understanding in Healthcare Platforms. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(3), 14088.
27. Straub, V. J., Morgan, D., Bright, J., & Margetts, H. (2023). Artificial intelligence in government: Concepts, standards, and a unified framework. Government Information Quarterly, 40(4), 101881.
28. Mökander, J., & Schroeder, R. (2024). Artificial intelligence, rationalization, and the limits of control in the public sector: The case of tax policy optimization. Social Media + Society, 10(2).
29. Kolla, S. H. (2024). Retrieval-Augmented Enterprise Intelligence: Enhancing Accuracy, Trust, and Operational Decision-Making. International Journal of Future Innovative Science and Technology (IJFIST), 7(2), 12425.
30. Pang, S., & Hua, G. (2024). How does digital tax administration affect R&D manipulation? Evidence from dual machine learning. Technological Forecasting and Social Change, 208, 123691.
31. Hirvonen, H. (2024). Just accountability structures—A way to promote the safe use of automated decision-making in the public sector. AI & Society, 39, 155–167.
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