Multimodal AI for Business Insights: Integrating Structured Data with Text, Images, and Voice
DOI:
https://doi.org/10.22399/ijcesen.3760Keywords:
Multimodal AI, Business Intelligence, Structured Data Integration, Text Analytics, Image Processing, Voice Analysis, Fusion Models, Deep Learning, Enterprise AI, Explainable AIAbstract
The present review has identified the urgency and multi-dimensionality of the issue of integrating ethical principles into AI systems in enterprises. With the further integration of AI into machinery in business and commerce in general, the threats of algorithmic discrimination, information privacy, and non-transparency should no longer be considered secondary issues. They are core to organizational success and social responsibility. A conceptual model was also suggested with ethical core elements like fairness, privacy, and transparency being correlated to the enterprise goals like innovation, trust, and ROI. The review understands empirical evidence indicating that ethical AI practices do not just improve the levels of stakeholder trust, but also match long-term strategic value. Still, even being extremely progressive, the sphere is fragmented and in demand of normalization. The majority of organizations still have a problem with translating abstract ideas into practical implementation plans. Evaluations have shown that the path forward is to build strong frameworks, scalable tools, and inclusive governance systems that would enable to operationalise of ethics throughout the AI lifecycle. In this way, enterprises are able to create AI systems that can be smart, along with being fair, reliable, and sustainable.
References
[1] Gao, J., Galley, M., & Li, L. (2019). Neural approaches to conversational AI: Question answering, task-oriented dialogues and social chatbots. Now Foundations and Trends. DOI: https://doi.org/10.1561/9781680835533
[2] Atrey, P. K., Hossain, M. A., El Saddik, A., & Kankanhalli, M. S. (2010). Multimodal fusion for multimedia analysis: a survey. Multimedia systems, 16(6), 345-379. DOI: https://doi.org/10.1007/s00530-010-0182-0
[3] Daskalakis, E., Remoundou, K., Peppes, N., Alexakis, T., Demestichas, K., Adamopoulou, E., & Sykas, E. (2022). Applications of fusion techniques in e-commerce environments: A literature review. Sensors, 22(11), 3998. DOI: https://doi.org/10.3390/s22113998
[4] Koksalmis, E., & Kabak, Ö. (2019). Deriving decision makers’ weights in group decision making: An overview of objective methods. Information Fusion, 49, 146-160. DOI: https://doi.org/10.1016/j.inffus.2018.11.009
[5] Ramachandram, D., & Taylor, G. W. (2017). Deep multimodal learning: A survey on recent advances and trends. IEEE signal processing magazine, 34(6), 96-108. DOI: https://doi.org/10.1109/MSP.2017.2738401
[6] Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. DOI: https://doi.org/10.1126/science.aaa8415
[7] Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011, June). Multimodal deep learning. In ICML (Vol. 11, pp. 689-696).
[8] Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM computing surveys (CSUR), 51(5), 1-42. DOI: https://doi.org/10.1145/3236009
[9] Raji, I. D., & Buolamwini, J. (2019, January). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 429-435). DOI: https://doi.org/10.1145/3306618.3314244
[10] Guo, W., Wang, J., & Wang, S. (2019). Deep multimodal representation learning: A survey. Ieee Access, 7, 63373-63394. DOI: https://doi.org/10.1109/ACCESS.2019.2916887
[11] Gou, X., Xu, Z., & Herrera, F. (2018). Consensus reaching process for large-scale group decision making with double hierarchy hesitant fuzzy linguistic preference relations. Knowledge-Based Systems, 157, 20-33. DOI: https://doi.org/10.1016/j.knosys.2018.05.008
[12] Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32.
[13] Guo, G., & Zhang, N. (2019). A survey on deep learning based face recognition. Computer vision and image understanding, 189, 102805. DOI: https://doi.org/10.1016/j.cviu.2019.102805
[14] Parimi, S. S. (2019). Automated Risk Assessment in SAP Financial Modules through Machine Learning. Available at SSRN 4934897.
[15] Gjerstad, P., Meyn, P. F., Molnár, P., & Næss, T. D. (2021). Do President Trump's tweets affect financial markets?. Decision Support Systems, 147, 113577. DOI: https://doi.org/10.1016/j.dss.2021.113577
[16] Zellers, R., Lu, X., Hessel, J., Yu, Y., Park, J. S., Cao, J., ... & Choi, Y. (2021). Merlot: Multimodal neural script knowledge models. Advances in neural information processing systems, 34, 23634-23651.
[17] Barlacchi, G., Nicosia, M., & Moschitti, A. (2014, June). Learning to rank answer candidates for automatic resolution of crossword puzzles. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning (pp. 39-48). DOI: https://doi.org/10.3115/v1/W14-1605
[18] Li, Y. G., & Yang, G. H. (2022). Optimal completely stealthy attacks against remote estimation in cyber-physical systems. Information Sciences, 590, 15-28. DOI: https://doi.org/10.1016/j.ins.2022.01.014
[19] Zheng, J. G. (2017). Data visualization in business intelligence. In Global business intelligence (pp. 67-81). Routledge. DOI: https://doi.org/10.4324/9781315471136-6
[20] Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., ... & Shah, H. (2016, September). Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems (pp. 7-10). DOI: https://doi.org/10.1145/2988450.2988454
[21] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019, June). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) (pp. 4171-4186).
[22] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). DOI: https://doi.org/10.1109/CVPR.2016.90
[23] Baevski, A., Zhou, Y., Mohamed, A., & Auli, M. (2020). wav2vec 2.0: A framework for self-supervised learning of speech representations. Advances in neural information processing systems, 33, 12449-12460.
[24] Poria, S., Hazarika, D., Majumder, N., & Mihalcea, R. (2020). Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research. IEEE transactions on affective computing, 14(1), 108-132. DOI: https://doi.org/10.1109/TAFFC.2020.3038167
[25] Tsai, Y. H. H., Bai, S., Liang, P. P., Kolter, J. Z., Morency, L. P., & Salakhutdinov, R. (2019, July). Multimodal transformer for unaligned multimodal language sequences. In Proceedings of the conference. Association for computational linguistics. Meeting (Vol. 2019, p. 6558). DOI: https://doi.org/10.18653/v1/P19-1656
[26] Ksieniewicz, P., Zyblewski, P., & Burduk, R. (2021). Fusion of linear base classifiers in geometric space. Knowledge-Based Systems, 227, 107231. DOI: https://doi.org/10.1016/j.knosys.2021.107231
[27] Veale, M., & Edwards, L. (2018). Clarity, surprises, and further questions in the Article 29 Working Party draft guidance on automated decision-making and profiling. Computer Law & Security Review, 34(2), 398-404. DOI: https://doi.org/10.1016/j.clsr.2017.12.002
[28] Ghosal, D., Akhtar, M. S., Chauhan, D., Poria, S., Ekbal, A., & Bhattacharyya, P. (2018). Contextual inter-modal attention for multi-modal sentiment analysis. In proceedings of the 2018 conference on empirical methods in natural language processing (pp. 3454-3466). DOI: https://doi.org/10.18653/v1/D18-1382
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
