Explainable AI for Compliance and Regulatory Models

Authors

  • Indra Reddy Mallela Scholar, Texas Tech University, Suryapet, Telangana, 508213,
  • Sneha Aravind University of Maryland, College Park, MD,
  • Vishwasrao Salunkhe Scholar, Savitribai Phule Pune University, Pune, India
  • Ojaswin Tharan Independent Researcher, Knowledgeum Academy, Karnataka, India,
  • Prof.(Dr) Punit Goel Research Supervisor , Maharaja Agrasen Himalayan Garhwal University, Uttarakhand,
  • Dr Satendra Pal Singh Ex-Dean, Gurukul Kangri University, Haridwar, Uttarakhand

DOI:

https://doi.org/10.36676/jrps.v11.i4.1584

Keywords:

Explainable AI, compliance models, regulatory frameworks, transparency, interpretability, accountability

Abstract

The increasing complexity of compliance and regulatory frameworks across industries demands innovative solutions for managing and interpreting large volumes of data. Explainable Artificial Intelligence (XAI) offers a promising approach by providing transparent and interpretable AI models that can be utilized for compliance and regulatory decision-making. Traditional AI systems, often viewed as "black boxes," have been met with scepticism due to their opacity, especially in high-stakes domains like finance, healthcare, and legal sectors, where accountability and trust are paramount. XAI addresses these challenges by making the decision-making process more transparent, enabling stakeholders to understand the logic behind AI-driven recommendations and actions.

In regulatory environments, XAI can be used to explain the rationale behind risk assessments, fraud detection, or legal interpretations, thus ensuring compliance with laws and policies. Moreover, the integration of XAI into compliance models enhances auditability and traceability, providing regulators and auditors with the tools to validate and verify the adherence to standards. This transparency is crucial for building trust in AI systems and fostering collaboration between human decision-makers and AI tools.

References

Alvarez-Melis, D., & Jaakkola, T. S. (2018). Towards robust interpretability with self-explaining neural networks. Proceedings of the 35th International Conference on Machine Learning (ICML), 80, 1-20.

Arrieta, A. B., Dinuzzo, F., M. R. C., & R. M. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities, and challenges toward responsible AI. Proceedings of the 2020 IEEE European Symposium on Security and Privacy (EuroS&P), 33-45.

Doshi-Velez, F., & Kim, P. (2017). Towards a rigorous science of interpretable machine learning. Proceedings of the 34th International Conference on Machine Learning (ICML), 70, 1-13.

Gilpin, L. H., Bau, D., Zhang, L., Bajcsy, P., & L. D. (2018). Explaining explanations: An approach to interpretability in machine learning. Proceedings of the 5th International Conference on Learning Representations (ICLR), 1-19. DOI: https://doi.org/10.1109/DSAA.2018.00018

Gunning, D. (2019). Explainable Artificial Intelligence (XAI). DARPA Program Update. Retrieved from DARPA website. DOI: https://doi.org/10.1145/3301275.3308446

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., & Giannotti, F. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1-42. DOI: https://doi.org/10.1145/3236009

Kirkpatrick, J., Pascanu, R., Ramalho, T., & D. J. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13), 3521-3526. DOI: https://doi.org/10.1073/pnas.1611835114

Lakkaraju, H., Gehrke, J., & M. A. (2017). Algorithmic decision making and the cost of fairness. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1235-1244.

Lipton, Z. C. (2016). The mythos of model interpretability. Communications of the ACM, 59(10), 36-43. DOI: https://doi.org/10.1145/3233231

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS), 4765-4774.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135-1144. DOI: https://doi.org/10.1145/2939672.2939778

Rudin, C. (2019). Stop explaining black box models for high-stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215. DOI: https://doi.org/10.1038/s42256-019-0048-x

Shrikumar, A., Greenside, P., & Kundaje, A. (2017). Learning important features through propagating activation differences. Proceedings of the 34th International Conference on Machine Learning (ICML), 70, 1-10.

Tjoa, E., & Guan, L. (2020). A survey on explainable artificial intelligence (XAI): Towards medical AI. Artificial Intelligence Review, 53(3), 1-33.

Weller, A. (2017). Challenges for AI ethics. Communications of the ACM, 60(7), 26-28. DOI: https://doi.org/10.1145/2983529

Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76-99. DOI: https://doi.org/10.1093/idpl/ipx005

Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. IEEE Transactions on Neural Networks and Learning Systems, 28(11), 1-11.

Goel, P. & Singh, S. P. (2009). Method and Process Labor Resource Management System. International Journal of Information Technology, 2(2), 506-512.

Singh, S. P. & Goel, P., (2010). Method and process to motivate the employee at performance appraisal system. International Journal of Computer Science & Communication, 1(2), 127-130.

Goel, P. (2012). Assessment of HR development framework. International Research Journal of Management Sociology & Humanities, 3(1), Article A1014348. https://doi.org/10.32804/irjmsh DOI: https://doi.org/10.32804/IRJMSH

Goel, P. (2016). Corporate world and gender discrimination. International Journal of Trends in Commerce and Economics, 3(6). Adhunik Institute of Productivity Management and Research, Ghaziabad.

Eeti, E. S., Jain, E. A., & Goel, P. (2020). Implementing data quality checks in ETL pipelines: Best practices and tools. International Journal of Computer Science and Information Technology, 10(1), 31-42. https://rjpn.org/ijcspub/papers/IJCSP20B1006.pdf

"Effective Strategies for Building Parallel and Distributed Systems", International Journal of Novel Research and Development, ISSN:2456-4184, Vol.5, Issue 1, page no.23-42, January-2020. http://www.ijnrd.org/papers/IJNRD2001005.pdf

"Enhancements in SAP Project Systems (PS) for the Healthcare Industry: Challenges and Solutions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 9, page no.96-108, September-2020, https://www.jetir.org/papers/JETIR2009478.pdf

Venkata Ramanaiah Chintha, Priyanshi, Prof.(Dr) Sangeet Vashishtha, "5G Networks: Optimization of Massive MIMO", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.7, Issue 1, Page No pp.389-406, February-2020. (http://www.ijrar.org/IJRAR19S1815.pdf )

Cherukuri, H., Pandey, P., & Siddharth, E. (2020). Containerized data analytics solutions in on-premise financial services. International Journal of Research and Analytical Reviews (IJRAR), 7(3), 481-491 https://www.ijrar.org/papers/IJRAR19D5684.pdf

Sumit Shekhar, SHALU JAIN, DR. POORNIMA TYAGI, "Advanced Strategies for Cloud Security and Compliance: A Comparative Study", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.7, Issue 1, Page No pp.396-407, January 2020. (http://www.ijrar.org/IJRAR19S1816.pdf )

"Comparative Analysis OF GRPC VS. ZeroMQ for Fast Communication", International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 2, page no.937-951, February-2020. (http://www.jetir.org/papers/JETIR2002540.pdf )

Eeti, E. S., Jain, E. A., & Goel, P. (2020). Implementing data quality checks in ETL pipelines: Best practices and tools. International Journal of Computer Science and Information Technology, 10(1), 31-42. https://rjpn.org/ijcspub/papers/IJCSP20B1006.pdf

"Effective Strategies for Building Parallel and Distributed Systems". International Journal of Novel Research and Development, Vol.5, Issue 1, page no.23-42, January 2020. http://www.ijnrd.org/papers/IJNRD2001005.pdf

"Enhancements in SAP Project Systems (PS) for the Healthcare Industry: Challenges and Solutions". International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 9, page no.96-108, September 2020. https://www.jetir.org/papers/JETIR2009478.pdf

Venkata Ramanaiah Chintha, Priyanshi, & Prof.(Dr) Sangeet Vashishtha (2020). "5G Networks: Optimization of Massive MIMO". International Journal of Research and Analytical Reviews (IJRAR), Volume.7, Issue 1, Page No pp.389-406, February 2020. (http://www.ijrar.org/IJRAR19S1815.pdf)

Cherukuri, H., Pandey, P., & Siddharth, E. (2020). Containerized data analytics solutions in on-premise financial services. International Journal of Research and Analytical Reviews (IJRAR), 7(3), 481-491. https://www.ijrar.org/papers/IJRAR19D5684.pdf

Sumit Shekhar, Shalu Jain, & Dr. Poornima Tyagi. "Advanced Strategies for Cloud Security and Compliance: A Comparative Study". International Journal of Research and Analytical Reviews (IJRAR), Volume.7, Issue 1, Page No pp.396-407, January 2020. (http://www.ijrar.org/IJRAR19S1816.pdf)

"Comparative Analysis of GRPC vs. ZeroMQ for Fast Communication". International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 2, page no.937-951, February 2020. (http://www.jetir.org/papers/JETIR2002540.pdf)

Eeti, E. S., Jain, E. A., & Goel, P. (2020). Implementing data quality checks in ETL pipelines: Best practices and tools. International Journal of Computer Science and Information Technology, 10(1), 31-42. Available at: http://www.ijcspub/papers/IJCSP20B1006.pdf

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Published

31-12-2020

How to Cite

Indra Reddy Mallela, Sneha Aravind, Vishwasrao Salunkhe, Ojaswin Tharan, Prof.(Dr) Punit Goel, & Dr Satendra Pal Singh. (2020). Explainable AI for Compliance and Regulatory Models. International Journal for Research Publication and Seminar, 11(4), 319–339. https://doi.org/10.36676/jrps.v11.i4.1584

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