Impact of Artificial Intelligence on Social Science and Human’s Behavior: The Review of Computer Vision Development and Impact on Interdisciplinary Applications and Social Platform

Dong  He; Dalaria  Jatel

doi:10.54691/epgcyy04

Authors

Dong He
Dalaria Jatel

DOI:

https://doi.org/10.54691/epgcyy04

Keywords:

Social science, Computer Vision, Pattern Recognition, Interdisciplinary Innovation.

Abstract

Artificial Intelligence (AI) is increasingly influencing social science and human behavior by analyzing vast amounts of data, identifying patterns, and predicting outcomes in various societal contexts. As one of the most important technique, computer vision has progressed significantly beyond the foundational stage of data capture, now enabling advanced systems that interpret, analyze, and transform digital image in many social applications. AI enhances research in psychology, sociology, and economics by modeling human decision-making, emotions, and interactions. This fast development has fostered substantial interdisciplinary engagement, seamlessly integrating machine learning, and computer graphics into vision-based solutions to social problems. This paper offers a comprehensive review of recent developments in the impact of computer vision on social science, with particular emphasis on image processing techniques and their deployment in diverse application domains. It delves into the theoretical principles, algorithms, and enabling technologies that support automated visual data interpretation—including object detection, facial recognition, scene analysis and forecasting. By delivering data-driven insights across fields such as healthcare, autonomous navigation, surveillance, and entertainment, computer vision has become indispensable to modern innovation and change human behaviors. To provide structure to this rapidly evolving technology, this work categorizes the field into four primary pillars: image processing, object recognition, machine learning, and computer graphics—each contributing essential functions such as quality enhancement, feature extraction, adaptive learning, and realistic visualization which plays important role in different social situation. By examining contemporary methodologies, assessing performance benchmarks, and highlighting emerging trends, this work not only captures the current state of the art but also points toward promising avenues for future research in social science. At last, these advancements underline the key role of artificial intelligence in automatic driving and addressing complex challenges in modern society.

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