"I Always Felt that SomethingWasWrong.": Understanding Compliance Risks and Mitigation Strategies when Highly-Skilled Compliance Knowledge Workers Use Large Language Models
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866918103946887168 |
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| author | Hu, Siying Wang, Piaohong Chan, Ka I Yao, Yaxing Lu, Zhicong |
| author_facet | Hu, Siying Wang, Piaohong Chan, Ka I Yao, Yaxing Lu, Zhicong |
| contents | The rapid advancement of Large Language Models (LLMs) has transformed knowledge-intensive has led to its widespread usage by knowledge workers to enhance their productivity. As these professionals handle sensitive information, and the training of text-based GenAI models involves the use of extensive data, there are thus concerns about privacy, security, and broader compliance with regulations and laws. While existing research has addressed privacy and security concerns, the specific compliance risks faced by highly-skilled knowledge workers when using the LLMs, and their mitigation strategies, remain underexplored. As understanding these risks and strategies is crucial for the development of industry-specific compliant LLM mechanisms, this research conducted semi-structured interviews with 24 knowledge workers from knowledge-intensive industries to understand their practices and experiences when integrating LLMs into their workflows. Our research explored how these workers ensure compliance and the resources and challenges they encounter when minimizing risks. Our preliminary findings showed that knowledge workers were concerned about the leakage of sensitive information and took proactive measures such as distorting input data and limiting prompt details to mitigate such risks. Their ability to identify and mitigate risks, however, was significantly hampered by a lack of LLM-specific compliance guidance and training. Our findings highlight the importance of improving knowledge workers' compliance awareness and establishing support systems and compliance cultures within organizations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04576 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | "I Always Felt that SomethingWasWrong.": Understanding Compliance Risks and Mitigation Strategies when Highly-Skilled Compliance Knowledge Workers Use Large Language Models Hu, Siying Wang, Piaohong Chan, Ka I Yao, Yaxing Lu, Zhicong Human-Computer Interaction The rapid advancement of Large Language Models (LLMs) has transformed knowledge-intensive has led to its widespread usage by knowledge workers to enhance their productivity. As these professionals handle sensitive information, and the training of text-based GenAI models involves the use of extensive data, there are thus concerns about privacy, security, and broader compliance with regulations and laws. While existing research has addressed privacy and security concerns, the specific compliance risks faced by highly-skilled knowledge workers when using the LLMs, and their mitigation strategies, remain underexplored. As understanding these risks and strategies is crucial for the development of industry-specific compliant LLM mechanisms, this research conducted semi-structured interviews with 24 knowledge workers from knowledge-intensive industries to understand their practices and experiences when integrating LLMs into their workflows. Our research explored how these workers ensure compliance and the resources and challenges they encounter when minimizing risks. Our preliminary findings showed that knowledge workers were concerned about the leakage of sensitive information and took proactive measures such as distorting input data and limiting prompt details to mitigate such risks. Their ability to identify and mitigate risks, however, was significantly hampered by a lack of LLM-specific compliance guidance and training. Our findings highlight the importance of improving knowledge workers' compliance awareness and establishing support systems and compliance cultures within organizations. |
| title | "I Always Felt that SomethingWasWrong.": Understanding Compliance Risks and Mitigation Strategies when Highly-Skilled Compliance Knowledge Workers Use Large Language Models |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.04576 |