Trustworthy AI Through End-to-End Data Lineage and Governance in Utility Data Ecosystems

Authors

  • Aditya Laljibhai Patel

Keywords:

trustworthy AI, data governance, data lineage, data provenance, explainable AI, FAIR principles, utility data ecosystems, Internet of Things, blockchain, smart grid, water management, algorithmic accountability

Abstract

Water utilities and power companies using water distribution systems and electricity networks are increasingly turning to artificial intelligence (AI) to decipher data from sensors for various applications like leak detection, load forecasting, or anomaly detection. The integrity of data entering these systems is as important as model accuracy - from field sensing, to governance, provenance recording, model inference, to explainable reporting. This paper combines existing bodies of knowledge on data governance and the FAIR (Findable, Accessible, Interoperable, Reusable) data principles, explainable AI (XAI), and Internet of Things (IoT) utility monitoring to introduce a layered, multi-layered framework that connects end-to-end data lineage with trustworthy AI outcomes in utility environments. It covers centralized, distributed (blockchain-based) and hybrid governance models, discusses the importance of explainability both intrinsically and as it relates to the model, and explores the local vs. global and legacy supervisory control and data acquisition (SCADA) vs. modern analytics platform dimensions as barriers to implementation. The paper makes the case that tracking and governing the origin of AI models must be integrated into their architecture, not as a downstream compliance step, if AI for utilities is to be trusted by regulators and the public. The quantitative comparisons offered are conceptual and illustrative, and are not necessarily measures derived from a specific empirical study, but are provided as a heuristic tool for assessing governance architectures. A hybrid of centralized governance and provenance verification, which brings together centralized policy enforcement with distributed provenance checking, is the most defensible approach between accountability, scalability and cost, and the paper suggests potential avenues for further empirical testing.

Downloads

Download data is not yet available.

References

Abraham, R., Schneider, J., & vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, 424–438. https://doi.org/10.1016/j.ijinfomgt.2019.03.014

Al-Badi, A., Tarhini, A., & Khan, A. I. (2018). Exploring big data governance frameworks. Procedia Computer Science, 141, 271–277. https://doi.org/10.1016/j.procs.2018.10.181

Andrić, I., Al-Ghamdi, S. G., & Koç, M. (2022). IoT approach towards smart water usage. Journal of Cleaner Production, 367, Article 133065. https://doi.org/10.1016/j.jclepro.2022.133065

Ahmed, S. A., & Mohammed, N. (2022). A solution for water management and leakage detection problems using IoTs based approach. Internet of Things, 18, Article 100504. https://doi.org/10.1016/j.iot.2022.100504

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012

Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023). A review of trustworthy and explainable artificial intelligence (XAI). IEEE Access, 11, 78994–79015. https://doi.org/10.1109/ACCESS.2023.3294569

Di Vaio, A., Trujillo, L., D'Amore, G., & Palladino, R. (2021). Water governance models for meeting sustainable development goals: A structured literature review. Utilities Policy, 72, Article 101255. https://doi.org/10.1016/j.jup.2021.101255

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 26(3), 1771–1796. https://doi.org/10.1007/s11948-020-00213-5

Georgakopoulos, D., & Jayaraman, P. P. (2016). Internet of things: From internet scale sensing to smart services. Computing, 98(10), 1041–1058. https://doi.org/10.1007/s00607-016-0510-0

Hasan, M. K., Alkhalifah, A., Islam, S., Babiker, N. B. M., Habib, A. K. M. A., Aman, A. H. M., & Hossain, M. A. (2022). Blockchain technology on smart grid, energy trading, and big data: Security issues, challenges, and recommendations. Wireless Communications and Mobile Computing, 2022, Article 9065768. https://doi.org/10.1155/2022/9065768

Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy artificial intelligence. Government Information Quarterly, 37(3), Article 101493. https://doi.org/10.1016/j.giq.2020.101493

Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2022). Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55(2), Article 39. https://doi.org/10.1145/3491209

Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023). Trustworthy AI: From principles to practices. ACM Computing Surveys, 55(9), Article 177. https://doi.org/10.1145/3555803

Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2021). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), Article 18. https://doi.org/10.3390/e23010018

Persson, S., Gorton, I., & Ali, S. (2022). Impact of blockchain technology on smart grids. Energies, 15(19), Article 7189. https://doi.org/10.3390/en15197189

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

Ryś, M., Pincheira, M., Vecchio, M., Giaffreda, R., & Kanhere, S. S. (2021). Cost-effective IoT devices as trustworthy data sources for a blockchain-based water management system in precision agriculture. Computers and Electronics in Agriculture, 180, Article 105889. https://doi.org/10.1016/j.compag.2020.105889

Simmhan, Y. L., Plale, B., & Gannon, D. (2005). A survey of data provenance in e-science. ACM SIGMOD Record, 34(3), 31–36. https://doi.org/10.1145/1084805.1084812

Strobel, M., & Shokri, R. (2022). Data privacy and trustworthy machine learning. IEEE Security & Privacy, 20(5), 44–49. https://doi.org/10.1109/MSEC.2022.3178187

Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets, 31(2), 447–464. https://doi.org/10.1007/s12525-020-00441-4

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, Article 160018. https://doi.org/10.1038/sdata.2016.18

Downloads

Published

31.10.2024

How to Cite

Aditya Laljibhai Patel. (2024). Trustworthy AI Through End-to-End Data Lineage and Governance in Utility Data Ecosystems. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 4421 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8465

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.