Risk Analysis and Mitigation of Human Interface with Generative Artificial Intelligence Systems to Enable Responsible Development

Jennifer Giang; Steven J. Simske

doi:10.65351/hsi24.023

Abstract

As Generative Artificial Intelligence (GAI) becomes increasingly prevalent in society, ensuring responsible and ethical development and use is crucial. This study conducts a risk analysis of human interface with GAI Large Language Models (LLM) to identify potential hazards and suggest mitigation strategies for promoting responsible GAI development. This study is component of a larger research project that is proposing the adaptation of testing strategies for responsible development of GAI. Risks included in this analysis are bias/discrimination, security/privacy concerns, and lack of transparency/reliability are assessed based on probability, impact of cost, schedule, and performance criteria. Following the implementation of mitigation strategies, a re-evaluation of the risks is conducted to gauge their adjusted system risk. The findings show that implementing targeted mitigation strategies can effectively reduce the likelihood and severity of risks associated with human interaction and GAI systems, thus enabling the development of more responsible and ethically sound AI technology. This study contributes to the ongoing discourse on responsible AI development and provides practical insights for organizations seeking to navigate the complexities of human-AI interaction responsibly.

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Citation

BibTeX citation:

@inproceedings{giang2024,
  author = {Giang, Jennifer and J. Simske, Steven},
  editor = {Masson, Dimitri},
  publisher = {INCOSE},
  title = {Risk {Analysis} and {Mitigation} of {Human} {Interface} with
    {Generative} {Artificial} {Intelligence} {Systems} to {Enable}
    {Responsible} {Development}},
  booktitle = {Proceedings International Conference on Human Systems
    Integration},
  volume = {3},
  date = {2024-08-29},
  url = {https://hsi-wg.github.io/conferences/HSI2024/proceedings/risk-analysis-and-mitigation-of-human-interface-with-generative-artificial-intelligence-systems-to-enable-responsible-development.html},
  doi = {10.65351/hsi24.023},
  langid = {en},
  abstract = {As Generative Artificial Intelligence (GAI) becomes
    increasingly prevalent in society, ensuring responsible and ethical
    development and use is crucial. This study conducts a risk analysis
    of human interface with GAI Large Language Models (LLM) to identify
    potential hazards and suggest mitigation strategies for promoting
    responsible GAI development. This study is component of a larger
    research project that is proposing the adaptation of testing
    strategies for responsible development of GAI. Risks included in
    this analysis are bias/discrimination, security/privacy concerns,
    and lack of transparency/reliability are assessed based on
    probability, impact of cost, schedule, and performance criteria.
    Following the implementation of mitigation strategies, a
    re-evaluation of the risks is conducted to gauge their adjusted
    system risk. The findings show that implementing targeted mitigation
    strategies can effectively reduce the likelihood and severity of
    risks associated with human interaction and GAI systems, thus
    enabling the development of more responsible and ethically sound AI
    technology. This study contributes to the ongoing discourse on
    responsible AI development and provides practical insights for
    organizations seeking to navigate the complexities of human-AI
    interaction responsibly.}
}

For attribution, please cite this work as:

Giang, Jennifer, and Steven J. Simske. 2024. “Risk Analysis and Mitigation of Human Interface with Generative Artificial Intelligence Systems to Enable Responsible Development.” In Proceedings International Conference on Human Systems Integration, edited by Dimitri Masson. Vol. 3. INCOSE. https://doi.org/10.65351/hsi24.023.