Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Nanjundan, Preethi; Thomas, Lijo; Indu, P.V.; Eslamian, Saied Title A name given to the resource Challenges and opportunities in multimodal learning research Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Multimodal Learning Using Heterogeneous Data;pp.239-246 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00022-X" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00022-X</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032983384?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032983384?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nanjundan P., Department of Data Science, CHRIST University, Lavasa Campus, Maharashtra, Pune, India; Thomas L., Department of Data Science, CHRIST University, Karnataka, Bengaluru, India, Department of Management, CHRIST University, Karnataka, Bengaluru, India; Indu P.V., Department of Data Science, CHRIST University, Karnataka, Bengaluru, India; Eslamian S., Department of Water Science and Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran Description An account of the resource The trend of multimodal learning, which involves processing and interpreting data through multiple modes such as text, images, and audio, is one aspect that highlights a great frontier in the artificial intelligence (AI) and machine learning (ML) domains. This project explores the technical, practical, and ethical considerations of research studies on multimodality. It starts with preliminary ethical considerations that should drive progress in AI and ML technologies, which include ideas of transparency, accountability, equality, and privacy. Analysis of this paper holds prime importance for moral concerns, as it discusses the issues of bias in AI algorithms and gives strategies that may reduce the level of bias in multimodal patterns. This technology part of the research focuses on technical challenges concerning accountability and transparency in multimodal machine decision-making methodologies. Privacy concerns regarding extensive use of AI and ML have been brought forward, along with the strategy for defensive personal statistics. At each step, an opportunity for innovation and development will be sought and mapped through the complex ethical landscapes of multimodal knowledge research. Through these considerations, this observation attempts to provide a close analysis in which future recommendations and discussions in the realm of AI and multimodal learning are addressed. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource artificial intelligence; bias mitigation; machine learning ethical frameworks; Multimodal learning; privacy protection Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044327528-9; 978-044327529-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online