Linking Artificial Intelligence Principles
Not on the map. Why?
Abstract (arXiv)
Artificial Intelligence principles define social and ethical considerations to develop future AI. They come from research institutes, government organizations and industries. All versions of AI principles are with different considerations covering different perspectives and making different emphasis. None of them can be considered as complete and can cover the rest AI principle proposals. Here we introduce LAIP, an effort and platform for linking and analyzing different Artificial Intelligence Principles. We want to explicitly establish the common topics and links among AI Principles proposed by different organizations and investigate on their uniqueness. Based on these efforts, for the long-term future of AI, instead of directly adopting any of the AI principles, we argue for the necessity of incorporating various AI Principles into a comprehensive framework and focusing on how they can interact and complete each other.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Dec 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 1; 2025: 1; 2026: 1.
8 papers.
- 2022Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governancecited by 28
- 2023A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Modelscited by 23
- 2024Evaluating Large Language Models with Psychometricscited by 22
- 2021How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impactcited by 18
- 2025Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systemscited by 1
- 2023Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholderscited by 0
- 2023Towards a Feminist Metaethics of AIcited by 0
- 2026A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasmscited by 0
- 2026A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasms16 Jul 2026
- 2025Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems28 Jun 2025
- 2024Evaluating Large Language Models with Psychometrics25 Jun 2024
- 2023A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Models28 Nov 2023
- 2023Towards a Feminist Metaethics of AI10 Nov 2023
- 2023Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders22 Mar 2023
- 2022Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance23 Jun 2022
- 2021How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact4 Jun 2021