Accountable Machine Intelligence for National Tax Governance
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), combined with advanced big data analytics, has the potential to significantly enhance efficiency and accuracy across a range of tax processes—from data management and fraud detection to pricing optimization and performance assessment. However, deploying these technologies responsibly requires adherence to core ethical principles: transparency, accountability, and explainability. Upholding these principles is essential to gaining acceptance from internal and external stakeholders, ensuring compliance with increasingly stringent financial regulations, supporting well-founded tax decisions, and ultimately advancing a fairer tax system.
This paper proposes a Responsible AI framework for a domain often overlooked in broader discussions of AI ethics: its application within government tax analytics and compliance systems. This includes tax agencies, the individuals and legal entities subject to taxation, and the international data-sharing agreements that connect them. These institutions routinely integrate their own datasets with externally sourced information—drawn from mandated third-party withholding and reporting entities, cross-border data exchanges, fiscal data of varying formats and origins used to ensure compatibility in the absence of formal tax treaties, and other categories of financial data.
Given the sensitive nature of tax-administrative data and the legal obligations tax agencies must meet, AI systems in this space need to address three core dimensions of Responsible AI: securing ownership over data and outputs, mitigating bias at both the design and deployment stages, and ensuring adequate control and security over system outputs.
References
1. Baghdasaryan, V., Davtyan, H., Sarikyan, A., & Navasardyan, Z. (2022). Improving tax audit efficiency using machine learning: The role of taxpayer’s network data in fraud detection. Applied Artificial Intelligence, 36(1).
2. Bevacqua, J. (2021). Unmasking the robots—An examination of the tax compliance implications of algorithmic secrecy. Australian Tax Forum, 36(4), 647–679.
3. Pandugula, C., & Yasmeen, Z. (2019). A Comprehensive Study of Proactive Cybersecurity Models in Cloud-Driven Retail Technology Architectures. Universal Journal of Computer Sciences and Communications, 1(1), 1253.
4. Chatzimparmpas, A., Martins, R. M., Jusufi, I., & Kerren, A. (2020). A survey of surveys on the use of visualization for interpreting machine learning models. Information Visualization, 19(3), 207–233.
5. Das, S., Agarwal, N., Venugopal, D., Sheldon, F. T., & Shiva, S. (2020). Taxonomy and survey of interpretable machine learning method. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 670–677). IEEE.
6. Faúndez-Ugalde, A., Mellado-Silva, R., & Aldunate-Lizana, E. (2020). Use of artificial intelligence by tax administrations: An analysis regarding taxpayers’ rights in Latin American countries. Computer Law & Security Review, 38, 105441.
7. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.
8. Pamisetty, A. (2019). Big Data Engineering for Real-Time Inventory Optimization in Wholesale Distribution Networks. Available at SSRN.
9. Mi, J.-X., Li, A.-D., Zhou, L.-F., & others. (2020). Review study of interpretation methods for future interpretable machine learning. IEEE Access, 8, 191969–191985.
10. Mojahedi, H. (2022). Towards tax evasion detection using improved particle swarm optimization algorithm. Mathematical Problems in Engineering, 2022, 1027518.
11. Mökander, J., & Floridi, L. (2022). From algorithmic accountability to digital governance. Nature Machine Intelligence, 4, 508–509.
12. Murorunkwere, B. F., Tuyishimire, O., Haughton, D., & Nzabanita, J. (2022). Fraud detection using neural networks: A case study of income tax. Future Internet, 14(6), 168.
13. Pessach, D., & Shmueli, E. (2022). A review on fairness in machine learning. ACM Computing Surveys, 55(3), 51, 1–44.
14. Kalisetty, S. (2020). Intelligent Supply Chain Ecosystems: Cloud-Native Architectures and Big Data Integration in Retail and Manufacturing Operations. Open Journal of Educational Research, 1(1), 1-19.
15. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). Association for Computing Machinery.
16. Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022). Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistics Surveys, 16, 1–85.
17. Savić, M., Atanasijević, J., Jakovetić, D., & Krejić, N. (2022). Tax evasion risk management using a hybrid unsupervised outlier detection method. Expert Systems with Applications, 193, 116409.
18. Scantamburlo, T. (2021). Non-empirical problems in fair machine learning. Ethics and Information Technology, 23, 703–712.
19. Pamisetty, V. (2019). Machine Learning Models for Real-Time Tax Fraud Detection and Risk Assessment in Digital Government Systems. Global Research Development (GRD) ISSN, 2455-5703.
20. Saragih, A. H., Reyhani, Q., Setyowati, M. S., & Hendrawan, A. (2022). The potential of an artificial intelligence (AI) application for the tax administration system’s modernization: The case of Indonesia. Artificial Intelligence and Law, 31(3), 491–514.
21. Shi, B., Dong, B., Xu, Y., Wang, J., Wang, Y., & Zheng, Q. (2022). An edge feature aware heterogeneous graph neural network model to support tax evasion detection. Expert Systems with Applications, 207, 118903.
22. Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2022). The ethics of algorithms: Key problems and solutions. AI and Ethics, 2, 215–230.
23. Varley, M., & Belle, V. (2021). Fairness in machine learning with tractable models. Knowledge-Based Systems, 215, 106715.
24. Agarwal, N., & Das, S. (2020). Interpretable machine learning tools: A survey. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE.
25. Gao, Y., Shi, B., Dong, B., Wang, Y., & other authors. (2021). Tax evasion detection with FBNE-PU algorithm based on PnCGCN and PU learning. IEEE Transactions on Knowledge and Data Engineering.
26. Mohun, J., & Roberts, A. (2020). Cracking the code: Rulemaking for AI-enabled government. OECD Working Papers on Public Governance. OECD Publishing.
27. Maguluri, K. K., Yasmeen, Z., & Nampalli, R. C. R. (2022). Big Data Solutions For Mapping Genetic Markers Associated With Lifestyle Diseases. Migration Letters, 19(6), 1188-1204.
28. Serrano Antón, F. (2021). Artificial intelligence and tax administration: Strategy, applications and implications, with special reference to the tax inspection procedure. World Tax Journal, 13(4), 575–608.
29. Decreasing Tax Evasion by Artificial Intelligence. (2021). IFAC-PapersOnLine, 54(13), 172–177.
30. Big data analytics in IRS audit procedures and its effects on tax compliance: A moderated mediation analysis. (2022). Journal of the American Taxation Association, 44(2), 97–113.
Additional Files
Published
Issue
Section
License
Copyright (c) 2023 Bhasker Katta (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The American Online Journal of Science and Engineering (AOJSE) is an open-access journal, and all published articles are made freely available to readers worldwide under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license governs the use, sharing, adaptation, and distribution of all content published in AOJSE and is designed to promote the broadest possible dissemination and reuse of scholarly research.
Creative Commons Attribution 4.0 International License (CC BY 4.0)
Under the Creative Commons Attribution 4.0 International License, any user is free to share, copy, and redistribute the published material in any medium or format, and to adapt, remix, transform, and build upon the material for any purpose, including commercial use, provided that appropriate credit is given to the original authors and source. The license cannot be revoked as long as the terms are followed.
When using or redistributing content published in AOJSE, users must provide proper attribution by citing the original authors' names, the article title, the journal name (AOJSE), the volume and issue number, the year of publication, and the DOI or URL of the article. Users must also indicate if any changes were made to the original work. Attribution must be provided in a reasonable manner but must not suggest that the authors or AOJSE endorse the user or their use of the material.
Author Rights and Copyright Retention
AOJSE respects and upholds the intellectual property rights of all contributing authors. Authors who publish in AOJSE retain full copyright over their work. By submitting a manuscript to AOJSE, authors grant the journal a non-exclusive, worldwide, royalty-free license to publish, reproduce, distribute, publicly display, and archive the article in print and electronic formats. This license allows AOJSE to make the article freely accessible to readers globally while ensuring that the authors remain the rightful owners of their work.
Authors are permitted to deposit their published articles in institutional repositories, personal websites, academic networking platforms, and preprint servers, provided that the original publication in AOJSE is acknowledged and properly cited. Authors may also reuse their published content in subsequent works, presentations, teaching materials, and grant applications without requiring prior permission from the journal.
Third-Party Content
Authors are solely responsible for obtaining written permission to reproduce any third-party material, including figures, tables, images, data, or excerpts from other publications, included in their manuscript. Evidence of such permissions must be provided to the editorial office upon request. AOJSE does not assume any responsibility for copyright infringement arising from the unauthorized inclusion of third-party content in published articles.
Permitted Uses
Under the CC BY 4.0 license, the following uses of AOJSE published content are freely permitted without prior written permission from the journal or authors, provided that proper attribution is given. Users may read, download, print, and distribute articles for personal, educational, or research purposes. Researchers may reuse data, figures, and findings for meta-analyses, systematic reviews, and secondary research. Educators may incorporate published articles into course materials, syllabi, and academic presentations. Journalists, policymakers, and practitioners may reference and cite published findings for professional and public interest purposes.
Prohibited Uses
While the CC BY 4.0 license is broad and permissive, users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Users may not misrepresent the original authorship of published content or imply endorsement by the original authors or AOJSE without explicit consent. Plagiarism, data fabrication, and any form of academic misconduct in the reuse of published material are strictly prohibited and are a violation of publication ethics standards.
Institutional and Repository Deposit Policy
AOJSE fully supports self-archiving and green open access. Authors are encouraged to deposit the final published version of their article, also known as the version of record, in institutional repositories, subject repositories, and open-access databases immediately upon publication. There is no embargo period. Authors should always link back to the original article on the AOJSE website and include the full citation and DOI when depositing their work in any repository.
Digital Preservation and Archiving
AOJSE is committed to the long-term digital preservation of all published content to ensure its permanent availability and accessibility. The journal utilizes the Open Journal Systems (OJS) platform, which supports internationally recognized digital archiving standards. AOJSE encourages participation in archiving networks such as LOCKSS (Lots of Copies Keep Stuff Safe) and CLOCKSS (Controlled LOCKSS) to safeguard published articles against data loss and ensure continued access for future generations of researchers and readers.
Disclaimer
The views, opinions, findings, and conclusions expressed in articles published in AOJSE are solely those of the authors and do not necessarily reflect the official position, policy, or views of the journal, its editorial board, or its affiliated organizations. AOJSE makes every effort to ensure the accuracy and integrity of published content but does not accept legal responsibility for any errors, omissions, or claims arising from the use of information published in the journal.
Contact for Licensing Inquiries
For any questions regarding the licensing terms, copyright, or permitted use of content published in AOJSE, please contact the editorial office through the journal website at https://aojse.org. The editorial team will be happy to assist authors, readers, and institutions with any licensing-related queries.
