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About this paper
Sabrina Goellner, Marina Tropmann-Frick, Bostjan Brumen
This paper conducts a structured literature review to address the lack of consistent terminology in responsible AI discourse, proposing a unified definition and identifying its core pillars. It clarifies the relationships between ethical AI, explainable AI, privacy, security, and trustworthiness to assist lawmakers and practitioners.
Key insights
- A significant lack of a unified, agreed-upon definition for "Responsible AI" and related terms was found in both policy documents and academic literature.
- Many terms associated with responsible AI can be grouped into five core pillars: Ethics, Trustworthiness, Security, Privacy, and Explainability, with "Transparency" often used interchangeably with "Explainability."
- "Human-centered" is a crucial overarching aspect, and "Trustworthiness" is best understood as an outcome or requirement of a responsible AI system rather than the encompassing concept itself.
Summary by alphaXiv.
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