Kumar, Manikanta Rajendra and Rongali, Sateesh Kumar (2025) Balancing AI and human collaboration. World Journal of Advanced Research and Reviews, 25 (3). pp. 562-567. ISSN 2581-9615
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Abstract
AI is going to mark a new revolution in human life, from healthcare and customer service to other basic dimensions of human functioning. While unlocking unparalleled opportunities for efficiency and innovation, critical challenges remain in striking a delicate balance between artificial intelligence-driven automation and much-needed human empathy. That balance is all the more important for sectors that demand emotional quotient, such as healthcare, senior living, and customer relationship engagement. Therefore, deals with methodologies and systems through which the most productive interplay between AI technologies and a human-centric approach can take place; discusses a set of ethical considerations, including bias in algorithms, transparency, and accountability. It produces some actionable solutions to integrate into real-world applications. Drawing from case studies across various industries, this research underlines that artificial intelligence and human capability should be complementary rather than competing ideas. Key strategies entail human-in-the-loop systems, ethics AI training, hybrid team models, and continuous monitoring. This paper, in that regard, discusses some of the regulatory and industry-recognized standards that advance equity and build trust in AI application development. The study underlines the fact that by leveraging strengths from AI and human interaction, experiences are augmented and improved to benefit users and societal welfare in a symbiotic relationship. This includes, for each of them, ways organizations would realize their potential with AI, in a responsible way. Confidence in the direction of technology development, in harmony with a more ethical approach and aligned human values.
Item Type: | Article |
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Official URL: | https://doi.org/10.30574/wjarr.2025.25.3.0556 |
Uncontrolled Keywords: | Accountability; Algorithmic Bias; Artificial Intelligence; Collaboration; Customer Engagement; Data Fairness; Diversity; Emotional Intelligence; Ethical AI; Fairness; Human-In-The-Loop Systems; Hybrid Models; Machine Learning; Oversight; Regulation; Societal Values; Symbiosis; Transparency; Trust; User Experience |
Depositing User: | Editor WJARR |
Date Deposited: | 17 Jul 2025 16:20 |
Related URLs: | |
URI: | https://eprint.scholarsrepository.com/id/eprint/1153 |