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Categorization and eccentricity of AI risks: a comparative study of the global AI guidelines

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  • Kai Jia

    (University of Electronic Science and Technology of China)

  • Nan Zhang

    (Tsinghua University)

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.

Suggested Citation

  • Kai Jia & Nan Zhang, 2022. "Categorization and eccentricity of AI risks: a comparative study of the global AI guidelines," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 59-71, March.
    • Handle: RePEc:spr:elmark:v:32:y:2022:i:1:d:10.1007_s12525-021-00480-5
    DOI: 10.1007/s12525-021-00480-5
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    References listed on IDEAS

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    1. Jan Zacharias & Moritz Zahn & Johannes Chen & Oliver Hinz, 2022. "Designing a feature selection method based on explainable artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2159-2184, December.

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