Responsible AI: Portraits with Intelligent Bibliometrics

Responsible AI remains poorly defined despite growing attention: The paper identifies that responsible AI is still in its "nascent stages" and grapples with "nebulous concepts," suggesting that despite widespread discussion across academia, industry, and society, there is no clear consensus on what responsible AI actually means in practice.

Three core concepts anchor responsible AI: The study distills responsible AI into three prevailing frameworks—explainable AI, trustworthy AI, and ethical AI—providing a structured foundation for understanding what practitioners and researchers mean when they discuss responsibility in AI systems.

The field is rapidly expanding with significant research output: Analysis of 17,799 research articles since 2015 demonstrates that responsible AI has become a major focus area for the AI research community, with the most recent work captured in a detailed examination of 380 articles across multiple disciplines.

Responsible AI principles must connect to actual AI techniques: The paper reveals important "interplay between responsibility principles and primary AI techniques," indicating that responsible AI cannot remain purely theoretical—it must be grounded in how specific technical approaches are actually implemented and deployed.

This research supports AI governance and regulation efforts: By providing "comprehensive macro-level insights" and mapping the responsible AI landscape, this bibliometric analysis offers evidence-based knowledge to inform policy decisions and regulatory frameworks for AI systems.