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International Journal of Modern Computer Science and IT Innovations

Open Access Peer Review International
Open Access

A Comprehensive Review of Responsible Artificial Intelligence: Ethics, Fairness, and Explainability

DOI https://doi.org/10.55640/ijmcsit-v03i07-05a
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Dr. Rohan Mehta 1 ,
4 Department of Artificial Intelligence and Data Science Advanced Computing Research Institute, Hyderabad, India

Abstract

The rapid advancement of Artificial Intelligence (AI) technologies has transformed decision-making processes across healthcare, finance, education, governance, cybersecurity, and enterprise systems. However, increasing dependence on AI-driven systems has introduced significant ethical concerns related to transparency, algorithmic bias, accountability, privacy, and human oversight. Responsible Artificial Intelligence (Responsible AI) has emerged as a multidisciplinary framework aimed at ensuring that AI systems operate according to principles of fairness, explainability, reliability, and social responsibility. This review paper examines the conceptual foundations, technical mechanisms, and practical challenges associated with responsible AI implementation. The study adopts a structured analytical review methodology to investigate major dimensions of ethical AI development, including fairness-aware algorithms, explainable AI approaches, governance frameworks, and accountability mechanisms. Furthermore, the research explores how responsible AI principles can be integrated into complex technological infrastructures where automation, scalability, and security requirements interact. The findings indicate that responsible AI requires a balanced integration of technical innovation, organizational governance, and ethical evaluation throughout the AI lifecycle. The analysis also highlights that explainability and fairness mechanisms are not independent components but interconnected elements necessary for building trustworthy intelligent systems. The paper contributes a comprehensive framework for understanding responsible AI adoption and identifies future research directions for developing transparent, equitable, and accountable AI ecosystems.

Keywords

Responsible Artificial Intelligence, AI Ethics, Algorithmic Fairness, Explainable AI

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