Linking Artificial Intelligence Principles
By Yi Zeng, Enmeng Lu, Cunqing Huangfu. 2018
89 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Safety Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Daniel S. Schiff
- Inioluwa Deborah Raji
- Ben Hutchinson
- Margaret Mitchell
- Andrew Smart
- Timnit Gebru
- Arjan Durresi
- Thilo Hagendorff
In Safety Research
- Daniel S. Schiff
- Ben Hutchinson
- Inioluwa Deborah Raji
- Margaret Mitchell
- Timnit Gebru
- Andrew Smart
- Jamila Smith-Loud
- Daniel Theron
Citations in the corpus
- Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020, neutral): “[16] for examples of non-Western AI principles.”
- Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020, background): “Nevertheless, such principles are becoming common methods to define the ethical priorities of an organization and thus the operational goals for which to aim [34, 83].”
- Trustworthy Artificial Intelligence: A Review (2022, background): “Over the past few years, research institutes, private organizations, and government agencies have proposed various guidelines and frameworks to make AI trustworthy [60, 68, 69, 154, 198, 224].”
- Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies (2021, background): “…review of six high-profile initiatives, bears substantial similarities with other scholarly efforts to AI create ethics typologies (Fjeld et al. 2020; Jobin et al. 2019; Zeng et al. 2018), and is highly familiar given its overlap with traditional bioethics principles (Beauchamp and Childress 1979).”
- The Ethics of AI Ethics: An Evaluation of Guidelines (2019, background): “To the best of my knowledge, a few preprints and papers are currently available, which also deal with the comparison of different ethical guidelines (Zeng et al. 2018; Fjeld et al. 2019; Jobin et al. 2019).”
- ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles (2019, background): “Of 50 AI principle statements documented through the Linking AI Principles (LAIP) project, 94% (47) explicitly mention transparency[19].”