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Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study

Created by
  • Haebom

Author

Yutan Huang, Chetan Arora, Wen Cheng Houng, Tanjila Kanij, Anuradha Madulgalla, John Grundy

Outline

This paper systematically analyzes the ethical issues and mitigation strategies of large-scale language models (LLMs), as well as the difficulties in implementing such strategies. By reviewing 39 previous studies, we categorize the core ethical issues related to the use of LLMs into five ethical dimensions, and investigate and evaluate the proposed mitigation strategies. The results of the study show that the ethical issues of LLMs are multidimensional and context-dependent, and the proposed mitigation strategies address some of the issues but still present significant challenges. In particular, ethical issues hinder the practical implementation of mitigation strategies in high-risk areas such as healthcare and public administration, and the existing frameworks show limitations in adapting to changing social expectations and diverse situations.

Takeaways, Limitations

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Takeaways:
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Systematically analyzes the ethical issues of LLM from a multidimensional and context-dependent perspective.
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Clarify the effectiveness and limitations of existing mitigation strategies
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Highlights ethical concerns and the difficulty of implementing mitigation strategies in high-risk areas
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Suggests the need for a new ethical framework that can adapt to changing social demands and diverse situations
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Limitations:
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The number of studies used in the analysis may be limited (39 studies)
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The classification into five ethical dimensions may not cover all ethical issues.
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Lack of specific indicators for assessing the effectiveness of mitigation strategies
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Lack of concrete proposals for a new ethical framework
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