A Policy Framework for Harnessing Artificial Intelligence Systems in Urban Settings Using a Meta Synthesis Approach

Document Type : Research Paper

Authors

1 Prof., Department of Management, Faculty of Management, University of Tehran, Tehran, Iran.

2 Ph.D. Candidate, Department of Leadership and Human Capital, Faculty of Management, University of Tehran, Tehran, Iran.

3 Associate Prof., Department of Management, Faculty of Management, University of Tehran, Tehran, Iran.

Abstract

Objective
This systematic study aims to analyze qualitative research related to the use of Artificial Intelligence (AI) systems in urban management. The study seeks to achieve a comprehensive understanding of the current state of AI policy experiences in cities. By systematically reviewing and synthesizing prior findings in this domain, this research aims to contribute to the development of more effective policies for the responsible and efficient use of AI in urban management.
Methods
In this study, a meta synthesis approach was employed, relying on the seven-step framework proposed by Sandelowski and Barroso (2007). Thematic analysis was used as the tool for qualitative data analysis. To assess research quality, inter-coder agreement, provision of coded text samples, reporting of research process steps, and iterative reviewer discussions were utilized.
Results
Relevant articles were identified and analyzed. The findings from the coding process were categorized twice, first within a contextual framework following PESTLE’s analysis, and secondly into 27 proposed thematic categories. Furthermore, three dimensions of sustainability (data-driven, algorithmic, and ethical) in AI systems relevant to urban management were identified.
 
 
Conclusion
As a final outcome of this research, we propose a metaphorical framework called the "AI Policy Staircase for Cities." This framework comprises five steps: World view, Sociey, Legal system, Urban Governance, and Technology Management. Researchers of this metaphorical framework take a cross-disciplinary approach to prioritize among these five high-level concepts and introduce key dimensions within each, providing guidance to stakeholders involved in AI policy-making for urban management.
Objective
This systematic study aims to analyze qualitative research related to the use of Artificial Intelligence (AI) systems in urban management. The study seeks to achieve a comprehensive understanding of the current state of AI policy experiences in cities. By systematically reviewing and synthesizing prior findings in this domain, this research aims to contribute to the development of more effective policies for the responsible and efficient use of AI in urban management.
Methods
In this study, a meta synthesis approach was employed, relying on the seven-step framework proposed by Sandelowski and Barroso (2007). Thematic analysis was used as the tool for qualitative data analysis. To assess research quality, inter-coder agreement, provision of coded text samples, reporting of research process steps, and iterative reviewer discussions were utilized.
Results
Relevant articles were identified and analyzed. The findings from the coding process were categorized twice, first within a contextual framework following PESTLE’s analysis, and secondly into 27 proposed thematic categories. Furthermore, three dimensions of sustainability (data-driven, algorithmic, and ethical) in AI systems relevant to urban management were identified.
 
 
Conclusion
As a final outcome of this research, we propose a metaphorical framework called the "AI Policy Staircase for Cities." This framework comprises five steps: World view, Sociey, Legal system, Urban Governance, and Technology Management. Researchers of this metaphorical framework take a cross-disciplinary approach to prioritize among these five high-level concepts and introduce key dimensions within each, providing guidance to stakeholders involved in AI policy-making for urban management.

Keywords

Main Subjects

References

References
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Journal of Public Administration
Volume 15, Issue 3
2023
Pages 512-552

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