Journal of Exceptional Multidisciplinary Research
Published December 13, 2024 | Version v1
Journal article Open

The Role of Metadata in Promoting Explainability and Interoperability of AI-based Prediction Models

Authors/Creators

  • 1. Department of Information Communication Technology, Federal Polytechnic Kaltungo, Gombe State, Nigeria
  • 2. Department of Computer Science Education, Federal College of Education, Gombe State, Nigeria
  • 3. Department of Computer and Mathematical Sciences, Abubakar Tafawa Balewa University Bauchi, Nigeria
  • 4. Department of Management and Information Technology, Abubakar Tafawa Balewa University Bauchi, Nigeria

Description

This systematic literature review delves into the critical role of metadata in enhancing the explainability and interoperability of AI-based prediction models. As AI technologies permeate various sectors, the need for transparent and coherent predictive models becomes increasingly vital. Metadata, which refers to structured information that contextualizes data, serves as a pivotal component in achieving this transparency and coherence. This review synthesizes 50 existing literature to evaluate how different types of metadata—such as descriptive, structural, and administrative metadata—contribute to the understanding and integration of AI models, thereby facilitating better decision-making and trust in AI systems.  The review identifies key themes related to the challenges of explainability, including the complexity of AI algorithms and the opacity of model outputs, and discusses how robust metadata frameworks can mitigate these issues by providing essential context and clarity about model decisions. Furthermore, it examines the significance of interoperability in AI applications, highlighting how standardized metadata can enable seamless integration across diverse systems and platforms. The findings underscore the necessity of developing comprehensive metadata strategies to enhance both the interpretability of AI predictions and the compatibility of AI systems across different environments. Ultimately, the review calls for continued research into metadata standards and practices that can promote a more reliable and user-friendly AI landscape.

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Additional details

Dates

Submitted
2024-09-12
Accepted
2024-10-23
Available
2024-12-13

References

  • Abdulganiyu, J., Musa, M. N., Matayo, V. A., Saidu, A. A., & Ahmed, A. A. (2023). Integrating Business Intelligence and Small and Medium Enterprises for Shifting Africa from Consumption to a Production and Industrialised Economy. Spectrum International Journal of Multidisciplinary Research, 1(2), 1–9.
  • Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A. E., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
  • Alam, M., Haque, A., Khan, A. I., Kasim, S., Pasha, A. A., Zafar, A., Irshad, K., Chaudhary, A. A., & Azim, R. (2023). Metasurface-Based Solar Absorption Prediction System Using Artificial Intelligence. Journal of Mathematics, 2023(9489270), 1–18. https://doi.org/https://doi.org/10.1155/2023/9489270
  • Alomar, M. A. (2022). Performance Optimization of Industrial Supply Chain Using Artificial Intelligence. Computational Intelligence and Neuroscience, 2022(9306265), 1–10. https://doi.org/https://doi.org/10.1155/2022/9306265
  • Alomar, M. K., Hameed, M. M., Al-ansari, N., & Alsaadi, M. A. (2020). Data-Driven Model for the Prediction of Total Dissolved Gas : Robust Artificial Intelligence Approach. Advances in Civil Engineering, 2020(6618842), 1–120. https://doi.org/https://doi.org/10.1155/2020/6618842
  • Amellal, I., Amellal, A., Seghiouer, H., & Ech-charrat, M. R. (2024). An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction. Decision Science Letters, 13(2024), 237–248. https://doi.org/10.5267/dsl.2023.9.003
  • Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 23(3), 715–734. https://doi.org/10.1007/s00500-018-3102-4
  • Banu, J. F., Neelakandan, S., Geetha, B. T., Selvalakshmi, V., Umadevi, A., & Martinson, E. O. (2022). Artificial Intelligence Based Customer Churn Prediction Model for Business Markets. Computational Intelligence and Neuroscience, 2022(1703696), 1–14. https://doi.org/https://doi.org/10.1155/2022/1703696
  • Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202. https://doi.org/10.1080/00207543.2018.1530476
  • Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022). Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 60(14), 4487–4507. https://doi.org/10.1080/00207543.2021.1950935
  • Brdesee, H. S., Alsaggaf, W., Aljohani, N., & Hassan, S. U. (2022). Predictive model using a machine learning approach for enhancing the retention rate of students at-risk. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-21. https://doi.org/10.4018/IJSWIS.299859
  • Buczak, A. L., & Guven, E. (2016). A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Communications Surveys and Tutorials, 18(2), 1153–1176. https://doi.org/10.1109/COMST.2015.2494502
  • Cica, D., Sredanovic, B., & Tesic, S. (2020). Predictive modeling of turning operations under different cooling / lubricating conditions for sustainable manufacturing with machine learning techniques. Applied Computing and Informatics, 20(1), 162–180. https://doi.org/10.1016/j.aci.2020.02.001
  • Cotrufo, N., Saloux, E., Hardy, J. M., Candanedo, J. A., & Platon, R. (2019). A practical Artificial Intelligence-based approach for predictive control in commercial and. Energy & Buildings, 109563. https://doi.org/10.1016/j.enbuild.2019.109563
  • Dabbas, H., & Friedrich, B. (2022). Benchmarking machine learning algorithms by inferring transportation modes from unlabeled GPS data. Transportation Research Procedia, 62(Ewgt 2021), 383–392. https://doi.org/10.1016/j.trpro.2022.02.048
  • Deb, I., & Gupta, R. K. (2023). A genetic algorithm based heuristic optimization technique for solving balanced allocation problem involving overall shipping cost minimization with restriction to the number of serving units as well as customer hubs. Results in Control and Optimization, 11(June 2022), 100227. https://doi.org/10.1016/j.rico.2023.100227
  • Ding, S., Su, C., & Yu, J. (2011). An optimizing BP neural network algorithm based on genetic algorithm. Artificial Intelligence Review, 36(2), 153–162. https://doi.org/10.1007/s10462-011-9208-z
  • Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., & Cosar, A. (2019). A survey on new generation metaheuristic algorithms. Computers and Industrial Engineering, 137(May). https://doi.org/10.1016/j.cie.2019.106040
  • Du, M., Luo, J., Wang, S., & Liu, S. (2019). Genetic algorithm combined with BP neural network in hospital drug inventory management system. Neural Computing and Applications, 3. https://doi.org/10.1007/s00521-019-04379-3
  • Duc, D. N., & Nananukul, N. (2020). A Hybrid Methodology Based on Machine Learning for a Supply Chain Optimization Problem. Journal of Physics: Conference Series, 1624(052022). https://doi.org/10.1088/1742-6596/1624/5/052022
  • Dumitrascu, O., Dumitrascu, M., & Dobrotǎ, D. (2020). Performance evaluation for a sustainable supply chain management system in the automotive industry using artificial intelligence. Processes, 8(11), 1–20. https://doi.org/10.3390/pr8111384
  • Elsken, T., Metzen, J. H., & Hutter, F. (2019). Survey on neural architecture search. Journal of Machine Learning Research, 20(2019), 1–21. https://doi.org/10.11834/jig.200202
  • Fan, J., & Shen, P. (2022). Financial Stock Investment Management Using Deep Learning Algorithm in the Internet of Things. Computational Intelligence and Neuroscience, 2022(4514300), 1–10. https://doi.org/https://doi.org/10.1155/2022/4514300
  • Ghodousian, A. (2018). An Algorithm for Solving Linear Optimization Problems Subjected to the Intersection of Two Fuzzy Relational Inequalities Defined by Frank Family of T-Norms. International Journal in Foundations of Computer Science & Technology, 8(3), 01–20. https://doi.org/10.5121/ijfcst.2018.8301
  • Guan, Y., Huang, Y., & Qin, H. (2022). Inventory Management Optimization of Green Supply Chain Using IPSO-BPNN Algorithm under the Artificial Intelligence. 2022.
  • Hassouna, M., El-henawy, I., & Haggag, R. (2022). A Multi-Objective Optimization for supply chain management using Artificial Intelligence (AI). International Journal of Advanced Computer Science and Applications, 13(8). 13(8), 140–149.
  • Helo, P., & Hao, Y. (2022). Artificial intelligence in operations management and supply chain management: an exploratory case study. Production Planning and Control, 33(16), 1573–1590. https://doi.org/10.1080/09537287.2021.1882690
  • Kudama, G., Dangia, M., Wana, H., & Tadese, B. (2021). Artificial Intelligence in Agriculture Will digital solution transform Sub-Sahara African agriculture ? Artificial Intelligence in Agriculture, 5, 292–300. https://doi.org/10.1016/j.aiia.2021.12.001
  • Kusiak, J., Szeliga, D., & Sztangret, Ł. (2012). Modelling techniques for optimizing metal forming processes. In Microstructure Evolution in Metal Forming Processes. Woodhead Publishing Limited. https://doi.org/10.1533/9780857096340.1.35
  • Li, X., Ren, S., & Gu, F. (2021). Medical Internet of Things to Realize Elderly Stroke Prevention and Nursing Management. Journal of Healthcare Engineering, 2021(9989602), 1–12.
  • Liao, M., & Yao, Y. (2021). Applications of artificial intelligence- ­ based modeling for bioenergy systems : A review. GCB-Bioenergy, 2021(January), 774–802. https://doi.org/https://doi.org/10.1111/gcbb.12816
  • Lin, H., Lin, J., & Wang, F. (2022). An innovative machine learning model for supply chain management. Journal of Innovation & Knowledge, 7(4), 100276. https://doi.org/10.1016/j.jik.2022.100276
  • Lokare, V. T., & Jadhav, P. M. (2024). An AI-based learning style prediction model for personalized and effective learning. Thinking Skills and Creativity, 51(November 2023), 101421. https://doi.org/10.1016/j.tsc.2023.101421
  • Luo, S., Xing, M., & Zhao, J. (2022). Construction of Artificial Intelligence Application Model for Supply Chain Financial Risk Assessment. Scientific Programming, 2022(4194576), 1–8. https://doi.org/https://doi.org/10.1155/2022/4194576
  • Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Olsen, S., Yngvar, H., Gupta, M., & Niehaves, B. (2021). Enabling AI capabilities in government agencies: A study of determinants for European municipalities. Government Information Quarterly, June. https://doi.org/10.1016/j.giq.2021.101596
  • Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
  • Modesti, P., Ribeiro, J. K., & Borsato, M. (2024). Artificial intelligence-based method for forecasting flowtime in job shops. VINE Journal of Information and Knowledge Management Systems, 54(2), 452-472. https://doi.org/10.1108/VJIKMS-08-2021-0146
  • Mohsen, B. M. (2023). Developments of Digital Technologies Related to Supply Chain Management. Procedia Computer Science, 220, 788–795. https://doi.org/10.1016/j.procs.2023.03.105
  • Monsalve, M. O., Caron-Munoz, M., Galeano-vasco, L., & Medina-Sierra, M. (2023). Use of Machine Learning Models for Prediction of Organic Carbon and Nitrogen in Soil from Hyperspectral Imagery in Laboratory Manuela Ortega Monsalve , Mario Cer on-Muñoz. Journal of Spectroscopy, 2023(4389885), 1–8. https://doi.org/https://doi.org/10.1155/2023/4389885
  • Naz, F., Kumar, A., Majumdar, A., & Agrawal, R. (2022). Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research. Operations Management Research, 15(1–2), 378–398. https://doi.org/10.1007/s12063-021-00208-w
  • Sarker, I. H. (2022). AI ‑ Based Modeling : Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. SN Computer Science, 3(2), 1–20. https://doi.org/10.1007/s42979-022-01043-x
  • Song, R., Pang, F., Jiang, H., & Zhu, H. (2024). A machine learning based method for constructing group profiles of university students. Heliyon, 10(7), e29181. https://doi.org/10.1016/j.heliyon.2024.e29181
  • Šustrová, T. (2016). A Suitable Artificial Intelligence Model for Inventory Level Optimization. Trends Economics and Management, 8527(1), 48–55. https://doi.org/http://dx.doi.org/10.13164/trends.2016.25.48
  • Tiyasha, Tung, T. M., & Yaseen, Z. M. (2020). A survey on river water quality modelling using artificial intelligence models: 2000–2020. Journal of Hydrology, 585(October 2019), 124670. https://doi.org/10.1016/j.jhydrol.2020.124670
  • Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122(September 2020), 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009
  • Wang, G., Wei, J., & Yao, B. (2020). A Coalmine Water Inrush Prediction Model Based on Artificial Intelligence. International Journal of Safety and Security Engineering, 10(4), 501–508. https://doi.org/https://doi.org/10.18280/ijsse.100409 Received:
  • Wang, S. (2021). Artificial Intelligence Applications in the New Model of Logistics. Scientific Programming, 2021(5166993), 1–5. https://doi.org/https://doi.org/10.1155/2021/5166993
  • Wei, Y. (2022). A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management. International Transactions on Electrical Energy Systems, 2022(4766597), 1–11. https://doi.org/https://doi.org/10.1155/2022/4766597
  • Xu, A., Chang, H., Xu, Y., Li, R., Li, X., & Zhao, Y. (2021). Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review. Waste Management, 124, 385–402. https://doi.org/10.1016/j.wasman.2021.02.029
  • Xuan, P., Quoc, P., Mai, H., Hung, Q., & Pham, T. (2024). Enhancing educational evaluation through predictive student assessment modeling. Computers and Education: Artificial Intelligence, 6(May), 100244. https://doi.org/10.1016/j.caeai.2024.100244
  • Zhang, S., & Duan, C. (2022). Clustering Optimization Algorithm for Data Mining Based on. Wireless Communications and Mobile Computing, 2022(1304951), 1–16. https://doi.org/https://doi.org/10.1155/2022/1304951