Abstract
Background
Governments, enterprises, civil organizations, and academics are engaged to promote normative guidelines aimed at regulating the development and application of Artificial Intelligence (AI) in different fields such as judicial assistance, social governance, and business services.
Aim
Although more than 160 guidelines have been proposed globally, it remains uncertain whether they are sufficient to meet the governance challenges of AI. Given the absence of a holistic theoretical framework to analyze the potential risk of AI, it is difficult to determine what is overestimated and what is missing in the extant guidelines. Based on the classic theoretical model in the field of risk management, we developed a four-dimensional structure as a benchmark to analyze the risk of AI and its corresponding governance measures. The structure consists of four pairs of risks: specific-general, legal-ethical, individual-collective and generational-transgenerational.
Method
Using the framework, a comparative study of the extant guidelines is conducted by coding the 123 guidelines with 1023 articles.
Result
We find that the extant guidelines are eccentric, while collective risk and generational risk are largely underestimated by stakeholders. Based on this analysis, three gaps and conflicts are outlined for future improvements.
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Notes
A comprehensive dataset of global AI Ethic Guidelines See: https://inventory.algorithmwatch.org
See Boddington (2018). Alphabetical list of resources. Ethics for Artificial Intelligence https://www.cs.ox.ac.uk/efai/resources/alphabetical-list-of-resources/. Winfield (2017). A round up of robotics and AI ethics. Alan Winfield’s Web Log http://alanwinfield.blogspot.com/2019/04/an-updated-round-up-of-ethical.html. National and international AI strategies (2018). Future of Life Institute https://futureoflife.org/national-international-ai-strategies. Summaries of AI policy resources. (2018). Future of Life Institute https://futureoflife.org/ai-policy-resources/.
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Acknowledgements
The authors would like to present thank Meiyin Huang, Peifen Li, Zongyang Li, Sisi Tang, Yu Wang, Haiming Wu, Qianwei Xu, Jing Zhang, who are students and research assistants in the Tsinghua University, and Yanglan Xu, Hao Wang, Zhihao Chen, Mingyang Wei, Jiayu Zhang,who are students in the University of Electronic Science and Technology of China, for their excellent work in the cross-checking of article codes. This work was partially supported by the National Key Research and Development Program of China (2018YFC0832305), the National Natural Science Foundation of China (71974111, 91646103), and the National Social Science Fund of China (18CZZ025).
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Jia, K., Zhang, N. Categorization and eccentricity of AI risks: a comparative study of the global AI guidelines. Electron Markets 32, 59–71 (2022). https://doi.org/10.1007/s12525-021-00480-5
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DOI: https://doi.org/10.1007/s12525-021-00480-5