Challenges and opportunities in multimodal learning research

Title

Challenges and opportunities in multimodal learning research

Creator

Nanjundan, Preethi; Thomas, Lijo; Indu, P.V.; Eslamian, Saied

Description

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.

Source

Multimodal Learning Using Heterogeneous Data;pp.239-246

Date

01-01-2025

Publisher

Elsevier

Subject

artificial intelligence; bias mitigation; machine learning ethical frameworks; Multimodal learning; privacy protection

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 978-044327528-9; 978-044327529-6;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1016/B978-0-443-27528-9.00022-X

https://www.scopus.com/pages/publications/105032983384?origin=resultslist