Summary of Responsible Ai: Portraits with Intelligent Bibliometrics, by Yi Zhang et al.
Responsible AI: Portraits with Intelligent Bibliometrics
by Yi Zhang, Mengjia Wu, Guangquan Zhang, Jie Lu
First submitted to arxiv on: 5 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the concept of responsible artificial intelligence (AI) by analyzing three prevailing concepts: explainable AI, trustworthy AI, and ethical AI. The study defines responsible AI and identifies its core principles. It also demonstrates the implementation of leveraging AI’s capabilities into bibliometrics for enhanced knowledge discovery. Empirically, the study investigates 17,799 research articles contributed by the AI community since 2015, recognizing key technological players and their relationships, and unveiling the topical landscape and hierarchy of responsible AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Responsible artificial intelligence (AI) is a new field that’s getting attention from experts, companies, and society. This study helps us understand what makes AI responsible and how it works. It looks at three important ideas: explainable AI, trustworthy AI, and ethical AI. The study shows how to use AI to find new knowledge and check if models are working correctly. It also studied 17,799 research articles about AI from the past five years. This helps us understand what’s going on in the field of responsible AI. |
Keywords
» Artificial intelligence » Attention
