Artificial intelligence, institutions, and resilience: Prospects and provocations for cities

Artikel

The notion of "smart city" incorporates promises of urban resilience, referring generally to capacities for cities to anticipate, absorb, react, respond, and reorganize in the face of disruptive changes and disturbances. As such, artificial intelligence (AI), coupled with big data, is being heralded as a means for enhancing and accessing key determinants of resilience. At the same time, while AI generally has been extolled for contributions to urban resilience, less attention has been paid to the other side of the equation - i.e., to the ethical, governance, and social downsides of AI and big data that can operate to hinder or compromise resilience. With particular attention to relevant institutional dynamics and features, an encompassing and systemic conception of smart and resilient cities is delineated as a critical lens for viewing and analyzing complex instrumental and intrinsic aspects of the relationship between AI and resilience. As a broader contribution to the literature, a set of structural, process, and outcome conditions are offered for engaging and assessing linkages inherent in the use of AI relative to urban resilience in terms of absorptive capacity, speed of recovery, over-optimization avoidance, and creative destruction, especially as regards impacts on relevant practices, standards, and policies.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Urban Management ; ISSN: 2226-5856 ; Volume: 11 ; Year: 2022 ; Issue: 2 ; Pages: 256-268

Classification and Topics

• Classification: Landschaftsgestaltung, Raumplanung
• Subject: ResilienceTechnology ; Artificial intelligence ; Smart city ; Ethics ; Institutions ; Governance

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Schintler, Laurie A. ; McNeely, Connie L.
• Event: Veröffentlichung
• (who): Elsevier
• (where): Amsterdam
• (when): 2022

Further information

• DOI: doi:10.1016/j.jum.2022.05.004
• Handle: http://hdl.handle.net/10419/271464
• Last update: 10.03.2025, 11:41 AM CET