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Open AccessJournal ArticleDOI

Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda

TLDR
In this article, the authors contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework.
Abstract
The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development; where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes.

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Posted Content

Responsible Innovation and the Innovation of Responsibility: Governing Sustainable Development in a Globalized World

TL;DR: In this article, the authors argue that responsible innovation that contributes to sustainable development consists of three dimensions: (1) innovations avoid harming people and the planet, (2) innovations do good by offering new products, services or technologies that foster sustainable development, and (3) global governance schemes are in place that facilitate innovations that avoid harm and do good.
Journal ArticleDOI

Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures

TL;DR: This perspective paper concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures.
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Artificial intelligence, systemic risks, and sustainability

TL;DR: In this paper, a global overview of the progress of Artificial Intelligence in sectors with high impact potential for sustainability like farming, forestry and the extraction of marine resources is presented, and possible systemic risks in these domains including algorithmic bias and allocative harms, unequal access and benefits, cascading failures and external disruptions, and trade-offs between efficiency and resilience.
Journal ArticleDOI

Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review

TL;DR: In this article , a systematic literature review of the literature focusing on the planning, design, and construction stages of the construction project lifecycle is presented, revealing that AI is particularly beneficial in the planning stage as the success of construction projects depends on accurate events, risks, and cost forecasting.
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Not deep learning but autonomous learning of open innovation for sustainable artificial intelligence

TL;DR: In this paper, the authors build up the interaction model between direct and autonomous learning from the human cognitive learning process and firms' open innovation process, and the key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant.
References
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Proceedings ArticleDOI

Conceptualizing smart city with dimensions of technology, people, and institutions

TL;DR: A set of the common multidimensional components underlying the smart city concept and the core factors for a successful smart city initiative is identified by exploring current working definitions of smart city and a diversity of various conceptual relatives similar to smart city.
Journal ArticleDOI

Developing a framework for responsible innovation

TL;DR: In this article, the authors present a framework for understanding and supporting efforts aimed at "responsibly innovation" in emerging science and innovation, which is a major challenge for contemporary democracies.
Journal ArticleDOI

The expected contribution of Industry 4.0 technologies for industrial performance

TL;DR: In this article, the authors studied how the adoption of different Industry 4.0 technologies is associated with expected benefits for product, operations and side-effects aspects in the Brazilian industry.
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