Responsible Artificial Intelligence: A Structured Literature Review

Responsible AI requires a human-centric approach: The research emphasizes that AI development must prioritize human values and interests, rather than treating ethics and safety as afterthoughts. This means centering human needs throughout the design and deployment process.

Explainability and transparency are critical pillars: The paper identifies model explainability as a key component of responsible AI, suggesting that AI systems should be understandable to users and stakeholders—not "black boxes" that make decisions without explanation.

Privacy, security, and trust form the foundation: These three interconnected elements are essential for responsible AI. The research indicates that without robust protections for user data and system security, public trust in AI systems cannot be established or maintained.

International regulation is urgently needed: The paper highlights that AI presents both significant benefits and potential risks, creating a dual nature that demands coordinated global governance frameworks rather than fragmented national approaches.

Companies need practical compliance frameworks: Beyond regulation, the research calls for actionable guidance to help organizations implement responsible AI principles in practice, bridging the gap between policy requirements and real-world development processes.