eISSN: 2791-173X editor@grpublishing.org
All Journals
Submit your article

Global Multidisciplinary Journal

Open Access Peer Review International
Open Access

Unsupervised Feature Alignment: Ethical and Explainable Contrastive Approaches in Multimodal Artificial Intelligence Systems

DOI
pdf
Dr. Elias Thorne 1 , Dr. Sarah Vance 1 ,
4 Department of Computational Sciences, Meridian University
4 Institute for Ethical Artificial Intelligence

Abstract

Background: The advent of Multimodal Artificial Intelligence has been accelerated by contrastive approaches to self-supervised learning, enabling systems to learn rich, robust feature representations without the need for expensive manual labeling. However, these "black box" models often produce high-dimensional latent spaces that are opaque to human interpretation, posing significant risks in high-stakes environments such as healthcare and criminal justice.

Methods: This study proposes a theoretical framework that bridges the gap between unsupervised contrastive learning and Explainable AI (XAI). We integrate principles of Granular Computing and Fuzzy Set Theory to impose interpretable structures upon the latent feature spaces generated by contrastive losses. Furthermore, we apply the National Institute of Standards and Technology (NIST) principles of explainability to evaluate the ethical standing of these systems.

Results: Our analysis demonstrates that while contrastive methods maximize feature richness, they often sacrifice semantic clarity. By applying granular modeling, we show that continuous feature vectors can be discretized into interpretable "information granules," thereby allowing for post-hoc explainability without retraining the foundational model. We further analyze the impact of confidence calibration on user trust.

Conclusions: We conclude that learning rich features without labels is viable for critical systems only when paired with robust XAI mechanisms. The integration of granular computing provides a mathematical foundation for extracting meaning from unlabeled data. We advocate for a "human-in-the-loop" governance model to ensure that contrastive AI systems remain ethical, transparent, and socially responsible.

Keywords

Contrastive Learning, Explainable AI, Multimodal Systems, Granular Computing

References

📄 Arrieta, A.B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; García, S.; Gil-López, S.; Molina, D.; Benjamins, R. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 2020, 58, 82–115.
📄 Phillips, P.J.; Hahn, C.A.; Fontana, P.C.; Broniatowski, D.A.; Przybocki, M.A. Four Principles of Explainable Artificial Intelligence; National Institute of Standards and Technology: Gaithersburg, MD, USA, 2020; Volume 18.
📄 Vale, D.; El-Sharif, A.; Ali, M. Explainable artificial intelligence (XAI) post-hoc explainability methods: Risks and limitations in non-discrimination law. AI Ethics 2022, 2, 815–826.
📄 Bhattacharya, S.; Pradhan, K.B.; Bashar, M.A.; Tripathi, S.; Semwal, J.; Marzo, R.R.; Bhattacharya, S.; Singh, A. Artificial intelligence enabled healthcare: A hype, hope or harm. J. Fam. Med. Prim. Care 2019, 8, 3461–3464.
📄 Zhang, Y.; Liao, Q.V.; Bellamy, R.K.E. Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, 27–30 January 2020; pp. 295–305.
📄 Antoniadi, A.M.; Du, Y.; Guendouz, Y.; Wei, L.; Mazo, C.; Becker, B.A.; Mooney, C. Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review. Appl. Sci. 2021, 11, 5088.
📄 Amann, J.; Blasimme, A.; Vayena, E.; Frey, D.; Madai, V.I.; the Precise, Q.c. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med. Inform. Decis. Mak. 2020, 20, 310.
📄 Durán, J.M. Dissecting scientific explanation in AI (sXAI): A case for medicine and healthcare. Artif. Intell. 2021, 297, 103498.
📄 Cabitza, F.; Campagner, A.; Malgieri, G.; Natali, C.; Schneeberger, D.; Stoeger, K.; Holzinger, A. Quod erat demonstrandum?—Towards a typology of the concept of explanation for the design of explainable AI. Expert Syst. Appl. 2023, 213, 118888.
📄 Holzinger, A.; Saranti, A.; Molnar, C.; Biecek, P.; Samek, W. Explainable AI methods—A brief overview. In Proceedings of the xxAI—Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, Vienna, Austria, 12–18 July 2020; Holzinger, A., Goebel, R., Fong, R., Moon, T., Müller, K.-R., Samek, W., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 13–38.
📄 Bargiela, A.; Pedrycz, W. Human-Centric Information Processing through Granular Modelling; Springer Science & Business Media: Dordrecht, The Netherlands, 2009; Volume 182.
📄 Zadeh, L.A. Fuzzy sets and information granularity. In Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers; World Scientific: Singapore, 1979; pp. 433–448.
📄 Keet, C.M. Granular computing. In Encyclopedia of Systems Biology; Dubitzky, W., Wolkenhauer, O., Cho, K.-H., Yokota, H., Eds.; Springer: New York, NY, USA, 2013; p. 849.
📄 Novák, V.; Perfilieva, I.; Dvoˇrák, A. What is fuzzy modeling. In Insight into Fuzzy Modeling; John Wiley & Sons: Hoboken, NJ, USA, 2016; pp. 3–10.
📄 Brendel, A.B., Mirbabaie, M., Lembcke, T.B. and Hofeditz, L., 2021. Ethical management of artificial intelligence. Sustainability, 13(4), p.1974.
📄 Yashika Vipulbhai Shankheshwaria, & Dip Bharatbhai Patel. (2025). Explainable AI in Machine Learning: Building Transparent Models for Business Applications. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(08), 08–15.
📄 Burr, C. and Leslie, D., 2023. Ethical assurance: a practical approach to the responsible design, development, and deployment of data-driven technologies. AI and Ethics, 3(1), pp.73-98.
📄 Camilleri, M.A., 2023. Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Systems, p.e13406.
📄 Char, D.S., Abràmoff, M.D. and Feudtner, C., 2020. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics, 20(11), pp.7-17.
📄 Chi, N., Lurie, E. and Mulligan, D.K., 2021, July. Reconfiguring diversity and inclusion for AI ethics. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 447-457).

How to Cite

Dr. Elias Thorne, & Dr. Sarah Vance. (2025). Unsupervised Feature Alignment: Ethical and Explainable Contrastive Approaches in Multimodal Artificial Intelligence Systems. Global Multidisciplinary Journal, 4(09), 21-29. https://www.grpublishing.org/journals/index.php/gmj/article/view/205

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >> 

Similar Articles

81-88 of 88

You may also start an advanced similarity search for this article.