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Human‐supervised data science framework for city governments: A design science approach

Author

Listed:
  • Loni Hagen
  • Mihir Patel
  • Luis Luna‐Reyes

Abstract

The importance of involving humans in the data science process has been widely discussed in the literature. However, studies lack details on how to involve humans in the process. Using a design science approach, this paper proposes and evaluates a human‐supervised data science framework in the context of local governments. Our findings suggest that the involvement of a stakeholder group, public managers in this case, in the process of data science project enhanced quality of data science outcomes. Public managers' detailed knowledge on both the data and context was beneficial for improving future data science infrastructure. In addition, the study suggests that local governments can harness the value of data‐driven approaches to policy and decision making through focalized investments in improving data and data science infrastructure, which includes culture and processes necessary to incorporate data science and analytics into the decision‐making process.

Suggested Citation

  • Loni Hagen & Mihir Patel & Luis Luna‐Reyes, 2023. "Human‐supervised data science framework for city governments: A design science approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(8), pages 923-936, August.
    • Handle: RePEc:bla:jinfst:v:74:y:2023:i:8:p:923-936
    DOI: 10.1002/asi.24764
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    References listed on IDEAS

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    Cited by:

    1. Robert Kumar & Yanzhen Qu, 2025. "Utilizing Large Language Model Enabled Agents to Streamline Business Decision Making," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 9(5), pages 14-21, September.

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