Human Centric Machine Learning Architectures for Enhancing Social Interaction Emotional Intelligence and Personalized Digital Experiences

DOI

10.2991/978-94-6463-718-2_57How to use a DOI?

Keywords

Humanoid AI; learning machine; empathetic AI; affection computing; sociocultural integration; tailored digital environment

Abstract

Human-centric machine learning architectures can potentially transform social interactions, emotional intelligence, and customized digital experiences. Specifically, while there has been much progress on affective computing and AI based social intelligence, the existing research primarily centers on parametric models with little to no correspondence with real-world phenomena. We developed a new machine learning framework revolved around empowering artificial intelligence to recognise, interpret, and respond to human emotions in real-time, with context-awareness and individuality (personalization) being functional to the use of emotion AI. In this study, we combine deep learning, multimodal emotion recognition, and reciprocal human-AI learning to build emotionally aware systems capable of adapting to user behaviors and social contexts in real-time. Moreover, the study engages in concepts that address cultural and ethical biases in mental health research, provide greater transparency in emotion recognition models, and create assistive technologies that include the otherwise marginalised populations. The proposed architecture seeks to leverage XAI techniques and diverse datasets that help elevate the role of AI in the digital world, making it more inclusive, socially conscious, and emotionally aware. As AI continues to advance, this research provides a foundation for the integration of emotional intelligence in real-world applications, from mental health care to human-computer collaboration, ultimately fostering a future where technology complements the nuances of human emotions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Thamaraiselvi
AU  - Vamsi Krishna Reddy Yerram
AU  - S. Prabakar
AU  - Om Prakash Yadav
AU  - K. Salma Khatoon
AU  - V. Naresh Kumar Reddy
PY  - 2025
DA  - 2025/05/23
TI  - Human Centric Machine Learning Architectures for Enhancing Social Interaction Emotional Intelligence and Personalized Digital Experiences
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 661
EP  - 670
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_57
DO  - 10.2991/978-94-6463-718-2_57
ID  - Thamaraiselvi2025
ER  -