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Perspective

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

by
Tan Yigitcanlar
1,2,*,
Juan M. Corchado
3,4,5,
Rashid Mehmood
6,
Rita Yi Man Li
7,
Karen Mossberger
8 and
Kevin Desouza
9
1
School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane QLD 4000, Australia
2
School of Technology, Federal University of Santa Catarina, Campus Universitario, Trindade, Florianópolis, SC 88040-900, Brazil
3
Bisite Research Group, University of Salamanca, 37007 Salamanca, Spain
4
Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
5
Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
6
High Performance Computing Center, King Abdulaziz University, Al Ehtifalat St, Jeddah 21589, Saudi Arabia
7
Department of Economics and Finance, Hong Kong Shue Yan University, 10 Wai Tsui Cres, North Point, Hong Kong, China
8
School of Public Affairs, Arizona State University, 411 North Central Avenue, Phoenix, AZ 85004, USA
9
School of Management, Queensland University of Technology, 2 George Street, Brisbane QLD 4000, Australia
*
Author to whom correspondence should be addressed.
J. Open Innov. Technol. Mark. Complex. 2021, 7(1), 71; https://0-doi-org.brum.beds.ac.uk/10.3390/joitmc7010071
Submission received: 28 January 2021 / Revised: 15 February 2021 / Accepted: 20 February 2021 / Published: 24 February 2021
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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.

1. Introduction

Over the last 50 years, the pace of technological development has increased significantly. We owe this remarkable progress to the efforts of the stakeholders of the global innovation ecosystem that activated two ground-breaking digital revolutions [1,2,3]. The First Digital Revolution occurred in the 1980s and 1990s—some scholars even date it back to the 1970s, when the development of the personal computer commenced [4]. These technological developments resulted in mass digitization, an increasing number of products and services being encoded in the cyberspace, and the diffusion of the internet on a pervasive scale [5]. Today, the world is on the verge of the Second Digital Revolution—where an increasing number of computing- and internet-enabled objects and devices allow for ubiquitous computing and open innovation opportunities in our everyday lives [6,7,8].
Moreover, Makridakis [9] estimates that the next digital revolution will take place within the next couple of decades, and calls it the ‘artificial intelligence (AI) revolution’. He further predicts that it will have a greater impact than both the first and the second digital revolutions combined. However, we are already on track towards the AI revolution. For instance, the Internet-of-Things (IoT) links objects wirelessly to a network that enables data sharing, and within this network AI is simultaneously analyzing IoT data and making decisions autonomously [10,11,12]. The smart home can be offered as an example of the popular application areas for this technology [13,14]. While highly innovative technologies—e.g., artificially intelligent internet-of-things (AI-IoT) [15]—are disrupting the industrial processes—i.e., Industry 4.0 [16]—, they are disrupting our cities and societies as well—i.e., smart city and smart community [17,18,19].
Nonetheless, this disruption is not necessarily solely generating positive externalities and delivering the desired outcomes or the desired outcomes for all [20]. For instance, on the one hand, autonomous vehicles—in the form of autonomous shuttle buses—could increase public transport coverage and patronage, and hence decrease the carbon emissions associated with transport [21,22]. On the other hand, autonomous vehicles—in the form of private autonomous cars—could increase mobility and urban sprawl, and thus increase transport carbon emissions [23]. Issues similar to these bring up the need for technological innovation in the context of cities, or in other words urban innovation, to become responsible for maximizing the desired outcomes and positive impacts for all and minimizing the unwanted ones [24,25,26].
Responsible innovation is vital in order to tackle the challenges our cities face, irrespective of whether they are related to natural resource degradation, climate change, economic progress or social welfare [27]. According to Von Schomberg [28] (p. 51), “responsible innovation is a transparent, interactive process by which societal actors and innovators become mutually responsive to each other with a view to the ethical acceptability, sustainability and societal desirability of the innovation process and its marketable products in order to allow for the proper embedding of scientific and technological advances in our society”. Responsible urban innovation can be defined as “a collective commitment of care for the urban futures through responsive stewardship of science, technology and innovation in the present” [29] (p. 27). That is to say, responsible urban innovation challenges us not only to generate science, technology and innovation which can have a positive impact on our cities and societies today, but also makes us think about and act upon our responsibility to build the desired urban futures for all [30].
This perspective paper is written with the purpose of contributing to the existing responsible urban innovation discourse—that is an understudied and a relatively under-advocated area. With a specific focus on technology for responsible urban innovation, the paper concentrates on AI and its use as part of local government systems. The rationale behind this selection is as follows: (a) AI, a technology with an increasing number of applications in the urban context, is referred to as one of the most powerful technologies of our time with both positive and negative externalities for cities [31,32]; (b) AI is an integral part of a smart city structure that provides the required efficiencies and automation ability in the delivery of local infrastructures, services and amenities [33,34], and; (c) there is a trend among local government agencies to adopt AI for managing routine, complex and complicated urban issues, where the knowledge and the experience of the staff in the area of responsible innovation, in general, are fairly limited [35,36,37].
As for the methodological approach, this perspective paper undertakes a review of the literature, research, developments, trends and applications concerning responsible urban innovation with local government AI systems, and develops a conceptual framework. In the light of the findings, the paper advocates the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems targeting responsible urban innovation.
Following this introduction, Section 2 provides an overview of the notion of responsible urban innovation. Subsequently, Section 3 focuses on local government AI systems including their common application areas. Next, Section 4 presents the concept of responsible local government AI and its necessity for obtaining the desired urban outcomes as well as for showcasing responsible urban innovation practices. Section 5 introduces a conceptual framework of responsible urban innovation with local government AI. Lastly, Section 6 closes the paper with some concluding remarks and prospective lines of research.

2. Responsible Urban Innovation

Cities continue to experience significant challenges—e.g., resource demands, governance complexity, socioeconomic inequality and environmental threats—where innovation is seen as an important means of addressing these problems [38,39,40]. In other words, innovation is considered necessary for tackling urbanization problems and ensuring smart, sustainable and inclusive growth [41,42,43]. While local governments conducting urban experiments to trial combatting urban problems in a novel way with technological innovation [44], this also created a lucrative business opportunity for the high-tech companies—such as Cisco, IBM, Siemens, Huawei and Sidewalk Labs—which merged technology solutions with urban planning and development under the popular ‘smart city’ brand [45,46].
Some initiatives are renowned for their success in the use of advanced technologies, such as AI to guide urban planners in making improvements in the city. The following are just some of the success cases: “(a) Massachusetts Institute of Technology (MIT) Media Lab’s agent-based simulation to explore possible designs for busy public spaces, including a regenerated Champs-Élysées in Paris; (b) the AI application of Topos, a New York based startup, including image recognition and natural language processing, to help understand how the layout of a city affects those living in it, and to identify how different areas of New York were used by the residents; (c) University of Melbourne’s AI utilization for future urban design decisions through the use of generative adversarial networks (GANs) to reproduce Google Street View images in the style of Melbourne’s neighborhoods with public health characteristics” [47] (p. 4); (d) the Array of Things (AoT) project of the University of Chicago that comprise “a network of interactive, modular devices, or nodes, that are installed around Chicago to collect real-time data on the city’s environment, infrastructure, and activity. These measurements are also shared as open data for research and public use” [48] (p. 1)—also see Hawthorne [49] for how citizen devices are being used for tracking Chicago’s pollution hot spots, and; (e) additional examples can be given with the cities’ living lab experiments that utilize quadruple helix as a form of local innovation system [50], deploying innovative technology to encourage citizen participation in urban decisions [51], and social innovation initiatives concerning urban problems, such as sustainable development and climate change [52].
Nonetheless, the short-term profit-at-any-cost mindset of many disruptive technology companies has been generating innovation with more negative externalities—e.g., increased energy demand, pollution, damage to physical and mental health, and waste of taxpayers’ money—than positive externalities [53,54]. This is to say, innovation without responsibility creates more problems than it solves—e.g., technology push, negligence of fundamental ethical principles, policy pull, and lack of precautionary measures and technology foresight [55,56].
Some of the common examples of the negative consequences of urban innovation involving advanced technologies, such as AI, include, but are not exclusively limited to: (a) the failure of algorithmic decision-making and predictive analytics of Pittsburgh, PA, in solving urban poverty, homelessness and violence problems, particularly by misdiagnosing child maltreatment and prescribing the wrong solutions [57]. This issue of automating inequality is discussed in length in the seminal work of Eubanks [58]. (b) Bias algorithmic decision-making has become one of the major unintended negative externalities, and the examples range from excluding women [59] to excluding people of color [60], and from excluding religious minorities [61] to excluding indigenous people [62]. (c) In most cases the failure and bias of algorithmic decisions led to the abolition of AI adoption endeavors in local governments. A good example is scrapping of the use of algorithms in benefit and welfare decisions in 20 local councils in the UK [63]. Furthermore, as stated by McKnight [64] (p. 1), “as AI has no a moral compass, OpenAI’s managers originally refused to release GPT-3 (Generative Pre-trained Transformer 3)—an autoregressive language model that uses deep learning to produce human-like text—ostensibly because they were concerned about the generator being used to create fake material, such as reviews of products or election-related commentary. Similarly, AI writing bots may need to be eliminated by humans, as in the case of Microsoft’s racist Twitter prototype AI chatbot—i.e., Tay”. (d) The other negative externalities include “creating opaque decision-making processes, challenges in accountability and trust in AI-enabled decisions, and risks to privacy due to sensitive, granular and in-depth data collection practices” [65] (p. 3).
Moreover, even the most celebrated smart city initiatives—that represent urban innovation in management and policy as well as technology—have failed to deliver their promises or have even been abandoned before project initiation—e.g., Songdo, Masdar, PlanIT Valley and Sidewalk Toronto [66,67]. The main reasons behind this failure include technology myopia, a top-down approach, solutionism and the lack of clear objectives and socio-spatial responsibility [68]. Consequently, technology giants—e.g., Google’s Sidewalk Labs and Cisco—have recently pulled back from the smart city push that did not practiced clear responsible urban innovation principles—including accountability, anticipation, reflexivity, transparency, responsiveness, inclusiveness and sustainability [69,70,71]. As argued by Green [72] (p. 1), we need to “recognize the complexity of urban life rather than merely see the city as something to optimize, truly smart cities are the ones that successfully incorporate technology into a holistic vision of justice and equity”.
Responsible urban innovation is central to addressing the current and emerging challenges of cities characterized by complexity, uncertainty, risk and myopia [73]. It encompasses a public and environmental value-sensitive approach to technology design and adoption, which makes environmental (e.g., eco-responsibility) and societal (e.g., social-responsibility) factors as relevant as the economic (e.g., frugality) ones in the urban innovation and development processes [74,75,76]. In other words, responsible urban innovation carefully considers the effects of innovation on the environment and society [77]. Responsible urban innovation, thus, is characterized by its sustainability, which is vital for generating long-lasting solutions [78].
Furthermore, as stated by Ziegler [79] (p. 195), there are two roles for responsible urban innovation to play for socio-spatial justice. These are: “(a) to contribute to the long-term stability of the society, and thus to find creative responses to socio-spatial challenges such as climate change, and; (b) to find ideas that specifically improve the benefits for the least advantaged members of the community in the present”.

3. Local Government Artificial Intelligence Systems

As stated by Das and Rad [80] (p. 1), AI-based algorithms “are transforming the way we approach real-world tasks done by humans; where recent years have seen a surge in the use of these algorithms in automating various facets of science, business, and social workflow”. In particular, government agencies are increasingly interested in using AI capabilities to deliver policy and generate efficiencies in high-uncertainty environments [81,82]. A study by De Sousa et al. [83] disclosed a growing trend of interest in AI in the public sector, with the US as the most active country. This is also the case with many local government agencies [84]. According to Wirtz et al. [85], the most common AI applications in government agencies are as follows: (a) AI-based knowledge management software; (b) AI process automation systems; (c) chatbots/virtual agents; (d) predictive analytics and data visualization; (e) identity analytics; (f) cognitive robotics and autonomous systems; (g) recommendation systems; (h) intelligent digital assistants; (i) speech analytics, and; (j) cognitive security analytics and threat intelligence.
AI offers urban innovation opportunities to generate novel solutions to the problems of our cities [86,87]. It has the potential to create a great impact on the way citizens experience and receive services and interact with their government, as recent advances in AI have resulted in an increasing number of decisions being handed over to algorithms [88,89]. This also applies to local government operations and services [90,91]. Today, AI is not only becoming an integral part of local government operations and services, but is also impacting and shaping the future of our cities and societies [92]—e.g., the forthcoming autonomous vehicle disruption [93,94,95].
In the context of cities, AI systems were first introduced as part of smart city initiatives [96] (Ullah et al., 2020). Nonetheless, today AI is no longer exclusively associated with smart city projects. For instance, there is an increasing number of local governments, with no smart city agenda, which have utilized AI-driven chatbots in their customer and service delivery services [97,98]. This is because local government agencies are becoming more aware of the benefits of AI. According to the International City/County Management Association (ICMA) [99], these benefits include: (a) local governments can run more efficiently; (b) local governments can focus on their residents; (c) local governments can remove a great deal of bias, and; (d) local governments can make data-smart decisions and gain that extra edge for under-resourced departments.
Besides AI-powered chatbots for engaging with the local community, local governments are using AI for automating routine tasks via self-service, and enhancing public services with data and analytics [100,101]. Additionally, hyper-personalized services, predictive maintenance of assets, workforce, schedule and resource optimization, reducing our carbon footprints, optimizing energy usage, and combatting child abuse and financial fraud are among the applications of AI used in local governments [102].
Today, many cities around the world are trying to position themselves as the leaders of urban innovation through the development and utilization of AI [103]. Some of these cities which are experimenting with and adopting AI systems include, but are not exclusively limited to, New York, Washington, Los Angeles, San Antonio, Pittsburgh, Phoenix, London, Singapore, Barcelona, Oslo, Helsinki, Hong Kong, Beijing, Brisbane, Sydney, Melbourne, Bangalore, Dubai and Jeddah [104]. Moreover, the world’s first ‘AI City’—or artificially intelligent city—is being planned in Chongqing (China) with wired AI-IoT, robotics, networking and big data [105].
While the popularity of AI is skyrocketing in the urban context, Allam [106] (p. 31) warns us that, “whilst AI stands as a potential savior and as its role is being accentuated in urban planning, governance and management, there are increasing concerns that its practical implications and planning principles are disconnected with sensibilities linked to the dimensions of sustainability”. This very issue has also been raised by other urban scholars [107].
Importantly, as today AI-based decision-making is in a trend to become commonplace, local government AI systems should be used “responsibly and ethically that extends beyond compliance with the narrow letter of the law. It also requires the system to be aligned with broadly-accepted social norms, and considerate of impact on individuals, communities and the environment” [108] (p. 1).
Despite the use of AI in local governments being relatively new, it is already possible to find promising examples of responsible practices, which include, but are not limited to: (a) AI-driven transportation analytics and decision-making systems to address the urban traffic problems of Austin, TX [109]; (b) autonomous shuttle buses are currently being used as first- and last-mile solutions to increase public transport patronage and/or to provide transport service to disadvantaged populations in cities, including Lyon (France), Geneva (Switzerland), Wien (Austria), Oslo (Norway), Las Vegas (USA), Masdar City (UAE), Thuwal (Saudi Arabia), West Kowloon (Hong Kong) and Renmark (Australia) [110,111], and; (c) computer vision and machine learning for robots to identify material characteristics while sorting waste and increasing the capacity of recycling of the San Francisco Bay Area [112].
Building on the aforementioned practices which are limited but promising practices, Schmelzer [113] (p. 1) raised the following key issues which must be fully tackled so that many more local governments can successfully implement responsible AI practices: “(a) identifying the unique challenges around data at the local government level; (b) determining the areas that AI has the biggest impact at the local level; (c) understanding the challenges local governments face around data privacy, transparency and security; (d) developing an AI-ready local government workforce, including upskilling the current workforce around data and AI skills, and; (e) having a responsive lens when deploying and managing AI technologies.” A study by Chen et al. [114] reveals the success factors for AI adoption as: (a) innovation attributes of AI; (b) organizational capability, and; (c) external environment.

4. Responsible Local Government Artificial Intelligence

Amid the global push for AI use at the local government level [115], there are growing concerns over AI uptake in the absence of an in-depth understanding of the implementation challenges, contextual local differences, and local government readiness [116]—as well as the lack of responsible urban innovation practices [117].
The upcoming digital urban infrastructure that will be supporting future societies will intrinsically be based on AI [118]. This is evident from the work on the sixth-generation (6G) networks with extreme-scale ubiquitous AI services through the next-generation softwarization, platformization, heterogeneity and configurability of networks [119,120,121]. Such infrastructure will provide unimaginable opportunities for urban innovation. Nevertheless, a responsible approach is critical when it comes to eliminating the negative externalities of innovation which could otherwise have catastrophic consequences—e.g., worsening socioeconomic inequity, widening digital divides, devastating environmental externalities and increased bias.
Some promising developments being made at present. A good outcome of the academic discourse is the ‘Montreal Declaration for Responsible AI’, developed under the auspices of the University of Montreal, following the Forum on the Socially Responsible Development of AI of November 2017 [122]. Another notable example is AI regulation and ethics frameworks being rolled out in various countries. The most celebrated of these is the European Parliament’s initiative on the guidelines for the European Union (EU) on ethics in AI [123]. While thus far, more than 50 countries have developed their national AI strategies—where the intention is mainly economic development and national security [124]—only a few have attempted to form their AI ethics frameworks that advocate responsible AI [125].
Despite the increasing interest in the scholarly debate, responsible urban innovation and responsible local government AI remain highly understudied, and thus far, there is a limited empirical evidence base and few conceptual expansions [126]. Thus, local governments’ adoption and use of AI systems [127], in the context of urban innovation, is an important topic of scholarly research, and key to expanding our understanding of the pathways leading to the achievement of responsible urban innovation through these systems.
Moreover, in the absence of an abundance of responsible urban innovation with local government AI, the generation of new insights and evidence is of utmost importance. Prospective research in this interdisciplinary field will aid in conceptualizing and providing a sound understanding of the most appropriate approaches for local governments engaging with AI to achieve responsible urban innovation. Such conceptualization will also aid in the efforts to develop clear pathways for healthy AI design and deployment. Furthermore, the outcomes of these future studies will help urban policymakers, managers and planners better understand the crucial role played by local government AI systems in ensuring responsible outcomes. This consolidated understanding will guide the efforts in developing new coordination and delivery practices for local government AI systems to achieve the desired responsible urban innovation outcomes in their cities.

5. Conceptual Framework

We have developed a conceptual framework of responsible urban innovation with local government AI. The main rationale behind the development of this high-level information, i.e., the conceptual framework, is to highlight the relationship between the key drivers, components and the fundamental principles of responsible urban innovation in the context of local government AI. We believe this approach will create an interest and curiosity in the academic community to further investigate the topic and subsequently generate new research directions and practical solutions for the government and industry to adopt or benefit from. Hence, the framework, which is illustrated in Figure 1 and elaborated below, shall not be seen as an operational framework to directly guide the AI system development, deployment and management practices of local government agencies.
In this framework, the responsible urban innovation phenomenon is conceptualized through a process that involves technology, policy and community to produce strategies, action plans and initiatives, with balanced costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems.
First, technology, policy and community are envisaged as the key drivers of responsible urban innovation; where local government AI systems represent ‘technology’, local government operation and service planning and delivery decisions represent ‘policy’, and local community perceptions and expectations represent the ‘community’ views and input. This is attributed to the fact that the trio of ‘technology, policy and community’ are being widely seen as the key drivers of smart cities—urban localities that use digital data and technology to create efficiencies for boosting economic development, enhancing the quality of life, and improving the sustainability of the city [128]—or technology-based urban growth practices [129,130,131] or technology-based public policy [132].
Second, ‘cost’, ‘benefit’, ‘risk’ and ‘impact’ are identified as the central foci of the framework for realizing responsible outcomes concerning local government AI systems. This is attributed to the need for undertaking a cost-benefit analysis before developing, adopting and deploying AI systems in order to ensure the worthiness of the investment [133]. Likewise, being aware of the potential hazards by undertaking a risk analysis, is critical when it comes to assuring the success of the AI system [134]. Similarly, forecasting the impact of the AI system on the society and the environment is crucial for identifying both positive and negative externalities [135]. Additionally, rather than only looking at costs and benefits overall, responsible use of AI should also consider the distribution of costs and benefits, and whether some groups or places (e.g., disadvantaged neighborhoods) bear more risk. This is an ethical concern—equity as a public value.
Moreover, this conceptualization advocates that local government AI systems should include the following characteristics: to be (a) explicable; (b) ethical; (c) trustworthy, and; (d) frugal.
First, the effectiveness of the AI systems is limited by the machine’s inability to explain its thoughts and actions to human users. Hence, explainable AI (XAI)—which refers to methods and techniques that generate high-quality interpretable, intuitive, human-understandable explanations of AI decisions—is essential for operators and users to understand, trust, and effectively manage local government AI systems [136,137].
Second, the ethical considerations made by the designers and adopters of AI systems, are critical when it comes to avoiding the unethical consequences of AI systems [138,139]. As stated by Floridi et al. [122] (p. 694), “ensuring socially preferable outcomes of AI relies on resolving the tension between incorporating the benefits and mitigating the potential harms of AI, in short, simultaneously avoiding the misuse and underuse of these technologies. In this context, the value of an ethical approach to AI technologies comes into starker relief”.
Third, today, we are seeing an increasing trend of autonomous decision-making, but we are not there yet. This is especially true for most government services [140,141]. In particular, local governments around the world are looking at technological solutions, including decisions being made autonomously by AI systems with limited or null human involvement, to address a range of social and policy challenges. While this handover of decisions to AI provides benefits—e.g., reduction in human error, faster decision-making, 24/7 availability, completion of repetitive tasks—it also creates risks, e.g., algorithmic bias caused by bad/limited data and training, privacy violations, removing human responsibility, and a lack of transparency [142]. The public is already becoming increasingly distrustful of many AI decisions; robodebt and systematic racism traumas caused by AI systems in Australia and the US are among the recent examples [143,144,145,146]. In order to prevent these risks from occurring and gaining public confidence, AI must be trustworthy [147,148,149].
Fourth, at present comprehensive AI systems are significant investments for local governments, with many of these organisations having limited budgets and responsibilities for justifying the investment and management cost to their citizens and taxpayers. For a wide-scale AI adoption in local governments, AI systems should become accessible and affordable, or in other words frugal [150]. In this context, frugality is the minimum use of scarce resources—e.g., capital, time, workforce and energy. Alternatively, the resources can be leveraged in new ways or other solutions can be found that do not jeopardize the delivery of high value outputs [151].
The framework, illustrated in Figure 1 and elaborated above, underlines the key drivers, components and the fundamental principles of responsible urban innovation with local government AI. It sheds light on the overall principles of the development and deployment of AI systems that assure the delivery of not only the desired urban outcomes, but also the desired urban outcomes for all citizens, stakeholders, users and the environment. The approach outlined in this framework is invaluable for local government agencies when it comes to being aware of the issues around responsible innovation; as local governments continue in the phase of experimentation with AI technologies, they need guidance on how best to design, develop, and deploy these solutions in a responsible manner that advances public value. Nevertheless, it should be noted that the framework presented here is a conceptual one developed with the goal of contributing to the academic discourse on the topic and generating directions for future research agendas. Thus, prospective research is needed to develop more operational frameworks involving specific guidelines for local governments so that they can make informed decisions regarding their investments in AI systems.

6. Concluding Remarks and Research Agenda

With the advent of powerful technologies and the strong short-term profit-at-any-cost mindset of many disruptive technology companies, ensuring responsible urban innovation will continue to be a major challenge in the third decade of the 21st century as well [152]. In particular, there are some important issues, in the context of responsible urban innovation with local government AI, that require urgent attention.
With the aforementioned urgency in mind, in this perspective paper, we underlined the importance of local government agencies making informed decisions while developing, adopting, deploying and managing AI systems. This is becoming a highly critical issue, as stated by Arrieta et al. [137] (p. 82), in recent years, the “sophistication of AI systems has increased to such an extent that almost no human intervention is required for their design and deployment. When decisions derived from such systems ultimately affect humans’ lives, there is an emerging need for understanding how such decisions are furnished” and how responsible they are. Moreover, this also increases the danger that unintended consequences can grow and multiply through policy inattention or delayed recognition.
Nevertheless, at present there are no operational frameworks and clear guidelines to assist local governments in achieving responsible innovation through AI practices. In the absence of such guidelines, the conceptual framework illustrated in Figure 1 is a step towards increasing awareness on the matter and triggering potential research agendas for the development of operational frameworks and guidelines. In this instance, we underline some of the fundamental issues, as listed below, where focusing on these could pave the way for a new research agenda concerning responsible urban innovation with local government AI:
  • How can local governments utilize AI systems effectively, what are the requirements for making them responsible, and how can local government AI support responsible urban innovation efforts?
  • Why do some local governments experiment with and adopt AI systems when the risks are not clearly known, while the others prefer to take a wait and see approach before making this decision?
  • How can local governments conduct trade-offs between costs, risks, benefits and impacts, and utilize AI systems in their municipal operations and services, and what externalities do these trade-offs generate?
  • How can the AI systems’ costs, risks, benefits and impacts be distributed across the local government service users and communities, and how can the equity concerns be addressed?
  • Further dwelling on the above issue, if some groups or geographic areas bear greater risk or costs, should AI be used anyway, should there be a just compensation in some manner, and if yes, how?
  • How can local governments align the public perceptions and expectations of AI, and mitigate the impact of consequential negative externalities of AI systems on the environment and society?
  • How can local governments support and ensure high levels of trust, transparency, and openness in the local community culture and extend these concepts of digital trust to AI?
  • How can local governments successfully adopt, deploy and manage AI systems to generate responsible urban innovation in their cities, and how can the guidelines for successful adoption be developed?
  • How will local government AI systems shape the future of our cities and impact the lives of citizens, and how can the negative externalities of the disruption be alleviated?
  • How can operational frameworks and guidelines be developed for local governments in regard to adopting, deploying and managing AI systems to achieve responsible urban innovation outcomes?
There is very limited to no coverage on either theoretical or applied aspects concerning the aforementioned questions in the literature and practice—as the responsible local government AI notion is still cutting-edge but incipient in nature. Most studies reported in the literature, thus far, are drawn from the intuitions and predictions of scholars rather than hard evidence obtained through exhaustive empirical research. We strongly believe that investigation of these issues in prospective research projects, by the scholars of this highly interdisciplinary (more correctly transdisciplinary) field, will shed light on the better conceptualization and practice of responsible urban innovation with local government AI.
Thus, the future research agenda should focus on tackling the aforementioned issues via carefully designed empirical studies in international contexts. In this regard, the conceptual framework, presented in Figure 1, is a useful compass to guide in the design of new empirical investigations. For instance, an example of a prospective research agenda, concerning responsible urban innovation with local government AI, could be as follows.
At present, AI is not only becoming an integral part of urban services, but also is impacting and shaping the future of living and cities [153,154]. Nevertheless, the current AI practice has shown that urban innovation without responsibility generates more problems than it solves. In particular, the absence of a deep understanding of the costs, benefits, risks and impacts of deploying local government AI systems creates concerns. Focusing on the local government case studies, prospective projects can generate new knowledge on the local government use of AI systems, and expand our understanding of the pathways for responsible urban innovation. These studies could also produce invaluable outcomes that include the local government AI adoption and implementation of guiding principles for informed decisions.
In conclusion, the AI revolution is already under way and its disruption will likely be comparable to that of the agricultural and industrial revolutions, which changed the course of human civilization radically [155]. Nevertheless, it should not be forgotten that the AI revolution contains an equal measure of opportunities and challenges [156,157]. According to Walsh [158], the ethical challenges posed by AI—e.g., fairness, transparency, trustworthiness, protection of privacy and respect, and many other fundamental rights—are the biggest issues, and they must be addressed with utmost care and urgency. Importantly, the actions to address these challenges should be adopted before the local government AI systems are actually in use [159,160,161].

Author Contributions

Conceptualization, methodology, formal analysis, writing—original draft preparation, T.Y.; formal analysis, writing—review and editing, and improving the rigor, critical perspective and relevance of the manuscript, J.M.C., R.M., R.Y.M.L., K.M. and K.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank the Editor-in-Chief and two anonymous referees for their constructive comments on an earlier version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Yun, J.J.; Zhao, X.; Yigitcanlar, T.; Lee, D.; Ahn, H. Architectural design and open innovation symbiosis: Insights from research campuses, manufacturing systems, and innovation districts. Sustainability 2018, 10, 4495. [Google Scholar] [CrossRef] [Green Version]
  2. Yun, J.J.; Jung, K.; Yigitcanlar, T. Open innovation of James Watt and Steve Jobs: Insights for sustainability of economic growth. Sustainability 2018, 10, 1553. [Google Scholar] [CrossRef] [Green Version]
  3. Beltagui, A.; Rosli, A.; Candi, M. Exaptation in a digital innovation ecosystem. Res. Policy 2020, 49, 103833. [Google Scholar] [CrossRef]
  4. Barnatt, C. The second digital revolution. J. Gen. Manag. 2001, 27, 1–16. [Google Scholar]
  5. Jarach, D. The digitalisation of market relationships in the airline business. J. Air Transp. Manag. 2002, 8, 115–120. [Google Scholar] [CrossRef]
  6. Yun, J.J.; Zhao, X.; Jung, K.; Yigitcanlar, T. The culture for open innovation dynamics. Sustainability 2020, 12, 5076. [Google Scholar] [CrossRef]
  7. Flores, P.; Carrillo, F.J.; Robles, J.G.; Leal, M.A. Applying open innovation to promote the development of a knowledge city: The Culiacan experience. Int. J. Knowl. Based Dev. 2018, 9, 312–335. [Google Scholar] [CrossRef]
  8. Rindfleisch, A. The second digital revolution. Mark. Lett. 2020, 31, 13–17. [Google Scholar] [CrossRef]
  9. Makridakis, S. The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures 2017, 90, 46–60. [Google Scholar] [CrossRef]
  10. Alam, F.; Mehmood, R.; Katib, I.; Albogami, N.; Albeshri, A. Data fusion and IoT for smart ubiquitous environments: A survey. IEEE Access 2017, 5, 9533–9554. [Google Scholar] [CrossRef]
  11. Alotaibi, S.; Mehmood, R.; Katib, I.; Rana, O.; Albeshri, A. Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using. Twitter Apache Spark Mach. Learn. Appl. Sci. 2020, 10, 1398. [Google Scholar]
  12. Yigitcanlar, T.; Butler, L.; Windle, E.; Desouza, K.; Mehmood, R.; Corchado, J. Can building ‘artificially intelligent cities’ protect humanity from natural disasters, pandemics and other catastrophes? Sensors 2020, 20, 2988. [Google Scholar] [CrossRef] [PubMed]
  13. Alaa, M.; Zaidan, A.; Zaidan, B.; Talal, M.; Kiah, M. A review of smart home applications based on Internet of Things. J. Netw. Comput. Appl. 2017, 97, 48–65. [Google Scholar] [CrossRef]
  14. Sodhro, A.; Gurtov, A.; Zahid, N.; Pirbhulal, S.; Wang, L.; Rahman, M.; Abbasi, Q. Towards convergence of AI and IoT for energy efficient communication in smart homes. IEEE Internet Things J. 2020. [Google Scholar] [CrossRef]
  15. Kumar, S.; Raut, R.; Narkhede, B. A proposed collaborative framework by using artificial intelligence-internet of things (AI-IoT) in COVID-19 pandemic situation for healthcare workers. Int. J. Healthc. Manag. 2020, 13, 337–345. [Google Scholar] [CrossRef]
  16. Dalenogare, L.; Benitez, G.; Ayala, N.; Frank, A. The expected contribution of Industry 4.0 technologies for industrial performance. Int. J. Prod. Econ. 2018, 204, 383–394. [Google Scholar] [CrossRef]
  17. Millar, C.; Lockett, M.; Ladd, T. Disruption: Technology, innovation and society. Technol. Forecast. Soc. Chang. 2018, 129, 254–260. [Google Scholar] [CrossRef]
  18. Yigitcanlar, T.; Cugurullo, F. The sustainability of artificial intelligence. Sustainability 2020, 12, 8548. [Google Scholar] [CrossRef]
  19. Yun, Y.; Lee, M. Smart city 4.0 from the perspective of open innovation. J. Open Innov. Technol. Mark. Complex. 2019, 5, 92. [Google Scholar] [CrossRef] [Green Version]
  20. Yigitcanlar, T.; Foth, M.; Kamruzzaman, M. Towards post-anthropocentric cities. J. Urban Technol. 2019, 26, 147–152. [Google Scholar] [CrossRef] [Green Version]
  21. Faisal, A.; Yigitcanlar, T.; Kamruzzaman, M.; Currie, G. Understanding autonomous vehicles. J. Transp. Land Use 2019, 12, 45–72. [Google Scholar] [CrossRef] [Green Version]
  22. Yigitcanlar, T.; Wilson, M.; Kamruzzaman, M. Disruptive impacts of automated driving systems on the built environment and land use: An urban planner’s perspective. J. Open Innov. Technol. Mark. Complex. 2019, 5, 24. [Google Scholar] [CrossRef] [Green Version]
  23. Butler, L.; Yigitcanlar, T.; Paz, A. Barriers and risks of mobility-as-a-service (MaaS) adoption in cities. Cities 2021, 109, 103036. [Google Scholar] [CrossRef]
  24. Butler, L.; Yigitcanlar, T.; Paz, A. Smart urban mobility innovations. IEEE Access 2020, 8, 196034–196049. [Google Scholar] [CrossRef]
  25. Goddard, M.; Davies, Z.; Guenat, S.; Ferguson, M.; Fisher, J.; Akanni, A.; Bates, A. A global horizon scan of the future impacts of robotics and autonomous systems on urban ecosystems. Nat. Ecol. Evol. 2020. [Google Scholar] [CrossRef]
  26. Liu, Z.; Ma, L.; Huang, T.; Tang, H. Collaborative governance for responsible innovation in the context of sharing economy. J. Open Innov. Technol. Mark. Complex. 2020, 6, 35. [Google Scholar] [CrossRef]
  27. Gonzales-Gemio, C.; Cruz-Cázares, C.; Parmentier, M. Responsible innovation in SMEs. Sustainability 2020, 12, 10232. [Google Scholar] [CrossRef]
  28. Von Schomberg, R. A vision of responsible research and innovation. In Responsible Innovation; Wiley: London, UK, 2013; pp. 51–74. [Google Scholar]
  29. Owen, R.; Stilgoe, J.; Macnaghten, P.; Gorman, M.; Fisher, E.; Guston, D. A framework for responsible innovation. In Responsible innovation; Wiley: London, UK, 2013; pp. 27–50. [Google Scholar]
  30. Zhao, Y.; Liao, M. Chinese perspectives on responsible innovation. In International Handbook on Responsible Innovation; Edward Elgar: Cheltenham, UK, 2019; pp. 426–440. [Google Scholar]
  31. Yigitcanlar, T. Technology and the City: Systems, Applications and Implications; Routledge: New York, NY, USA, 2016. [Google Scholar]
  32. Cugurullo, F. Urban artificial intelligence. Front. Sustain. Cities 2020, 2, 1–14. [Google Scholar] [CrossRef]
  33. Batty, M. Artificial intelligence and smart cities. Environ. Plan. B 2018. [Google Scholar] [CrossRef]
  34. Luckey, D.; Fritz, H.; Legatiuk, D.; Dragos, K.; Smarsly, K. Artificial intelligence techniques for smart city applications. In International Conference on Computing in Civil and Building Engineering; Springer: Cham, Switzerland, 2020; pp. 3–15. [Google Scholar]
  35. Desouza, K.; Swindell, D.; Smith, K.; Sutherland, A.; Fedorschak, K.; Coronel, C. Local government 2035: Strategic trends and implications of new technologies. Issues Technol. Innov. 2015, 27, 27. [Google Scholar]
  36. Vogl, T.; Seidelin, C.; Ganesh, B.; Bright, J. Smart technology and the emergence of algorithmic bureaucracy. Public Adm. Rev. 2020, 80, 946–961. [Google Scholar] [CrossRef]
  37. Wang, Y.; Zhang, N.; Zhao, X. Understanding the determinants in the different government AI adoption stages. Soc. Sci. Comput. Rev. 2020. [Google Scholar] [CrossRef]
  38. Cooke, P. Regionally asymmetric knowledge capabilities and open innovation: Exploring ‘Globalisation 2’—A new model of industry organisation. Res. Policy 2005, 34, 1128–1149. [Google Scholar] [CrossRef]
  39. Belussi, F.; Sammarra, A.; Sedita, S.R. Learning at the boundaries in an “open regional innovation system”: A focus on firms’ innovation strategies in the Emilia Romagna life science industry. Res. Policy 2010, 39, 710–721. [Google Scholar] [CrossRef]
  40. Voegtlin, C.; Scherer, A. Responsible innovation and the innovation of responsibility. J. Bus. Ethics 2017, 143, 227–243. [Google Scholar] [CrossRef]
  41. Joss, S. Sustainable Cities: Governing for Urban Innovation; Palgrave: London, UK, 2015. [Google Scholar]
  42. Yun, J.J.; Lee, D.; Ahn, H.; Park, K.; Yigitcanlar, T. Not deep learning but autonomous learning of open innovation for sustainable artificial intelligence. Sustainability 2016, 8, 797. [Google Scholar] [CrossRef] [Green Version]
  43. Burget, M.; Bardone, E.; Pedaste, M. Definitions and conceptual dimensions of responsible research and innovation. Sci. Eng. Ethics 2017, 23, 1–19. [Google Scholar] [CrossRef] [PubMed]
  44. Dijk, M.; De Kraker, J.; Hommels, A. Anticipating constraints on upscaling from urban innovation experiments. Sustainability 2018, 10, 2796. [Google Scholar] [CrossRef] [Green Version]
  45. Yigitcanlar, T.; Kamruzzaman, M. Smart cities and mobility: Does the smartness of Australian cities lead to sustainable commuting patterns? J. Urban Technol. 2019, 26, 21–46. [Google Scholar] [CrossRef]
  46. Gibson, E. Sidewalk Labs Creates Machine-Learning Tool for Designing Cities. 2020. Available online: https://www.dezeen.com/2020/10/20/delve-sidewalk-labs-machine-learning-tool-cities (accessed on 10 February 2021).
  47. Heaven, W. AI Planners in Minecraft could Help Machines Design Better Cities. 2020. Available online: https://www.technologyreview.com/2020/09/22/1008675/ai-planners-minecraft-urban-design-healthier-happier-cities (accessed on 10 February 2021).
  48. University of Chicago What is Array of Things? 2020. Available online: https://arrayofthings.github.io/faq.html (accessed on 14 February 2021).
  49. Hawthorne, M. Citizen Devices Tracking Chicago’s Pollution Hot Spots. 2017. Available online: https://www.chicagotribune.com/news/breaking/ct-chicago-air-quality-testing-met-20171111-story.html (accessed on 14 February 2021).
  50. Vallance, P.; Tewdwr-Jones, M.; Kempton, L. Building collaborative platforms for urban innovation: Newcastle City Futures as a quadruple helix intermediary. Eur. Urban Reg. Stud. 2020, 27, 325–341. [Google Scholar] [CrossRef]
  51. Wilson, A.; Tewdwr-Jones, M. Let’s draw and talk about urban change: Deploying digital technology to encourage citizen participation in urban planning. Environ. Plan. B 2020, 47, 1588–1604. [Google Scholar] [CrossRef]
  52. Angelidou, M.; Psaltoglou, A. An empirical investigation of social innovation initiatives for sustainable urban development. Sustain. Cities Soc. 2017, 33, 113–125. [Google Scholar] [CrossRef]
  53. Fleming, J. Profit at Any Cost? Baker Books: Grand Rapids, MI, USA, 2003. [Google Scholar]
  54. Spiegel, J. The ethics of virtual reality technology. Sci. Eng. Ethics 2018, 24, 1537–1550. [Google Scholar] [CrossRef]
  55. De Saille, S. Innovating innovation policy: The emergence of ‘responsible research and innovation’. J. Responsible Innov. 2015, 2, 152–168. [Google Scholar] [CrossRef]
  56. Selby, J.; Desouza, K. Fragile cities in the developed world. Cities 2019, 91, 180–192. [Google Scholar] [CrossRef]
  57. Eubanks, V. A child Abuse Prediction Model Fails Poor Families. 2018. Available online: https://www.wired.com/story/excerpt-from-automating-inequality (accessed on 10 February 2021).
  58. Eubanks, V. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor; St. Martin’s Press: New York, NY, USA, 2018. [Google Scholar]
  59. Eveleth, B. How Self-Tracking Apps Exclude Women. 2014. Available online: https://www.theatlantic.com/technology/archive/2014/12/how-self-tracking-apps-exclude-women/383673 (accessed on 10 February 2021).
  60. Ledford, X. Millions of Black People Affected by Racial Bias in Health-Care Algorithms. 2019. Available online: https://0-www-nature-com.brum.beds.ac.uk/articles/d41586-019-03228-6 (accessed on 10 February 2021).
  61. Bousquet, C. Algorithmic Fairness: Tackling Bias in City Algorithms. 2018. Available online: https://datasmart.ash.harvard.edu/news/article/algorithmic-fairness-tackling-bias-city-algorithms (accessed on 10 February 2021).
  62. Smith, C. Dealing with Bias in Artificial Intelligence. 2020. Available online: https://www.nytimes.com/2019/11/19/technology/artificial-intelligence-bias.html (accessed on 10 February 2021).
  63. Marsh, S. Councils Scrapping Use of Algorithms in Benefit and Welfare Decisions. 2020. Available online: https://www.theguardian.com/society/2020/aug/24/councils-scrapping-algorithms-benefit-welfare-decisions-concerns-bias (accessed on 10 February 2021).
  64. McKnight, L. To Succeed in an AI World, Students must Learn the Human Traits of Writing. 2021. Available online: https://theconversation.com/to-succeed-in-an-ai-world-students-must-learn-the-human-traits-of-writing-152321 (accessed on 10 February 2021).
  65. Van Noordt, C.; Misuraca, G. Exploratory insights on artificial intelligence for government in Europe. Soc. Sci. Comput. Rev. 2020. [Google Scholar] [CrossRef]
  66. Goodman, E.; Powles, J. Urbanism under Google. Law Rev. 2019, 88, 457–498. [Google Scholar]
  67. Desouza, K.; Hunter, M.; Jacob, B.; Yigitcanlar, T. Pathways to the making of prosperous smart cities. J. Urban Technol. 2020, 27, 3–32. [Google Scholar] [CrossRef]
  68. Yigitcanlar, T.; Han, H.; Kamruzzaman, M.; Ioppolo, G.; Sabatini-Marques, J. The making of smart cities. Land Use Policy 2019, 88, 104187. [Google Scholar] [CrossRef]
  69. Stilgoe, J.; Owen, R.; Macnaghten, P. Developing a framework for responsible innovation. Res. Policy 2013, 42, 1568–1580. [Google Scholar] [CrossRef] [Green Version]
  70. Deschamps, T. Google’s Sidewalk Labs Pulls out of Toronto Smart City Project. 2020. Available online: https://www.huffingtonpost.ca/entry/sidewalk-labs-toronto-google_ca_5eb41bebc5b646b73d28f8bc (accessed on 8 January 2021).
  71. Tilley, A. Cisco Systems Pulls Back from Smart City Push. 2020. Available online: https://www.wsj.com/articles/cisco-turns-off-lights-on-smart-city-push-11609178895 (accessed on 8 January 2021).
  72. Green, B. The Smart Enough City: Putting Technology in its Place to Reclaim our Urban Future; MIT Press: Boston, MA, USA, 2019. [Google Scholar]
  73. Blasko, B.; Lukovics, M.; Buzás, N. Good practices in responsible innovation. In Responsible Innovation; University of Szeged: Szeged, Hungary, 2014; pp. 179–192. [Google Scholar]
  74. Albert, M. Sustainable frugal innovation: The connection between frugal innovation and sustainability. J. Clean. Prod. 2019, 237, 117747. [Google Scholar] [CrossRef]
  75. Hadj, T. Effects of corporate social responsibility towards stakeholders and environmental management on responsible innovation and competitiveness. J. Clean. Prod. 2020, 250, 119490. [Google Scholar] [CrossRef]
  76. Umbrello, S. Imaginative value sensitive design. Sci. Eng. Ethics 2020, 26, 575–595. [Google Scholar] [CrossRef] [PubMed]
  77. Martinuzzi, A.; Blok, V.; Brem, A.; Stahl, B.; Schönherr, N. Responsible research and innovation in industry: Challenges, insights and perspectives. Sustainability 2018, 10, 702. [Google Scholar] [CrossRef] [Green Version]
  78. Van den Buuse, D.; Van Winden, W.; Schrama, W. Balancing exploration and exploitation in sustainable urban innovation. J. Urban Technol. 2020. [Google Scholar] [CrossRef]
  79. Ziegler, R. Justice and innovation–towards principles for creating a fair space for innovation. J. Responsible Innov. 2015, 2, 184–200. [Google Scholar] [CrossRef]
  80. Das, A.; Rad, P. Opportunities and challenges in explainable artificial intelligence (XAI): A survey. arXiv 2020, arXiv:2006.11371. [Google Scholar]
  81. Mikhaylov, S.; Esteve, M.; Campion, A. Artificial intelligence for the public sector. Philos. Trans. R. Soc. A 2018, 376, 20170357. [Google Scholar] [CrossRef] [Green Version]
  82. Dwivedi, Y.K.; Hughes, L.; Ismagilova, E.; Aarts, G.; Coombs, C.; Crick, T.; Williams, M.D. Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 2019. [Google Scholar] [CrossRef]
  83. De Sousa, W.; De Melo, E.; Bermejo, P.; Farias, R.; Gomes, A. How and where is artificial intelligence in the public sector going? Gov. Inf. Q. 2019, 36, 101392. [Google Scholar] [CrossRef]
  84. Swindell, D.; Desouza, K.; Hudgens, R. Dubai Offers Lessons for Using Artificial Intelligence in Local Government. 2018. Available online: https://www.brookings.edu/blog/techtank/2018/09/28/dubai-offers-lessons-for-using-artificial-intelligence-in-local-government/ (accessed on 10 January 2021).
  85. Wirtz, B.W.; Weyerer, J.C.; Geyer, C. Artificial intelligence and the public sector: Applications and challenges. Int. J. Public Adm. 2019, 42, 596–615. [Google Scholar] [CrossRef]
  86. D’Amico, G.; L’Abbate, P.; Liao, W.; Yigitcanlar, T.; Ioppolo, G. Understanding sensor cities. Sensors 2020, 20, 4391. [Google Scholar] [CrossRef] [PubMed]
  87. Macrorie, R.; Marvin, S.; While, A. Robotics and automation in the city. Urban Geogr. 2020. [Google Scholar] [CrossRef] [Green Version]
  88. Mehr, H.; Ash, H.; Fellow, D. Artificial Intelligence for Citizen Services and Government. 2017. Available online: https://ash.harvard.edu/files/ash/files/artificial_intelligence_for_citizen_services.pdf (accessed on 11 January 2021).
  89. Walsh, T. Australia’s AI future. J. Proc. R. Soc. New South Wales 2019, 152, 101–105. [Google Scholar]
  90. Andreasson, U.; Stende, T. Nordic Municipalities’ Work with Artificial Intelligence; Nordic Council of Ministers: Copenhagen, Denmark, 2019. [Google Scholar]
  91. Androutsopoulou, A.; Karacapilidis, N.; Loukis, E.; Charalabidis, Y. Transforming the communication between citizens and government through AI-guided chatbots. Gov. Inf. Q. 2019, 36, 358–367. [Google Scholar] [CrossRef]
  92. Yigitcanlar, T.; Desouza, K.; Butler, L.; Roozkhosh, F. Contributions and risks of artificial intelligence (AI) in building smarter cities. Energies 2020, 13, 1473. [Google Scholar] [CrossRef] [Green Version]
  93. Faisal, A.; Yigitcanlar, T.; Kamruzzaman, M.; Paz, A. Mapping two decades of autonomous vehicle research: A systematic scientometric analysis. J. Urban Technol. 2020. [Google Scholar] [CrossRef]
  94. Golbabaei, F.; Yigitcanlar, T.; Paz, A.; Bunker, J. Individual predictors of autonomous vehicle public acceptance and intention to use: A systematic review of the literature. J. Open Innov. Technol. Mark. Complex. 2020, 6, 106. [Google Scholar] [CrossRef]
  95. Golbabaei, F.; Yigitcanlar, T.; Bunker, J. The role of shared autonomous vehicle systems in delivering smart urban mobility: A systematic review of the literature. Int. J. Sustain. Transp. 2020. [Google Scholar] [CrossRef]
  96. Ullah, Z.; Al-Turjman, F.; Mostarda, L.; Gagliardi, R. Applications of artificial intelligence and machine learning in smart cities. Comput. Commun. 2020, 154, 313–323. [Google Scholar] [CrossRef]
  97. Adam, M.; Wessel, M.; Benlian, A. AI-based chatbots in customer service and their effects on user compliance. Electron. Mark. 2020. [Google Scholar] [CrossRef] [Green Version]
  98. Luo, X.; Tong, S.; Fang, Z.; Qu, Z. Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Mark. Sci. 2019, 38, 937–947. [Google Scholar] [CrossRef]
  99. ICMA Using Artificial Intelligence as a Tool for Your Local Government. 2019. Available online: https://icma.org/blog-posts/using-artificial-intelligence-tool-your-local-government (accessed on 9 January 2021).
  100. Bright, J.; Ganesh, B.; Seidelin, C.; Vogl, T. Data Science for Local Government. 2019. Available online: https://smartcities.oii.ox.ac.uk/wp-content/uploads/sites/64/2019/04/Data-Science-for-Local-Government.pdf (accessed on 11 January 2021).
  101. Alomari, E.; Katib, I.; Albeshri, A.; Mehmood, R. COVID-19: Detecting government pandemic measures and public concerns from Twitter Arabic data using distributed machine learning. Int. J. Environ. Res. Public Health 2021, 18, 282. [Google Scholar] [CrossRef]
  102. Quddus, J. Innovation in Local Government. 2020. Available online: https://methods.co.uk/blog/innovation-in-local-government (accessed on 3 January 2021).
  103. Robinson, S. Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national public policy strategies for artificial intelligence (AI). Technol. Soc. 2020, 63, 101421. [Google Scholar] [CrossRef]
  104. Kirwan, C.; Zhiyong, F. Smart Cities and Artificial Intelligence: Convergent Systems for Planning, Design, and Operations; Elsevier: London, UK, 2020. [Google Scholar]
  105. Designboom Bjarke Ingels Group Plans AI CITY in China to Advance Future of Artificial Intelligence. 2020. Available online: https://www.designboom.com/architecture/bjarke-ingels-group-ai-city-china-artificial-intelligence-terminus-group-09-29-2020 (accessed on 11 January 2021).
  106. Allam, Z. Urban chaos and the AI Messiah. In Cities and the Digital Revolution; Palgrave Pivot: Cham, Switzerland, 2020; pp. 31–60. [Google Scholar]
  107. Khakurel, J.; Penzenstadler, B.; Porras, J.; Knutas, A.; Zhang, W. The rise of artificial intelligence under the lens of sustainability. Technologies 2018, 6, 100. [Google Scholar] [CrossRef] [Green Version]
  108. Caetano, T.; Simpson-Young, B. Artificial Intelligence can Deepen Social Inequality: Here are 5 Ways to Help Prevent This. 2021. Available online: https://theconversation.com/artificial-intelligence-can-deepen-social-inequality-here-are-5-ways-to-help-prevent-this-152226 (accessed on 13 January 2021).
  109. Diamante, R. UT Creates Artificial Intelligence to Solve Austin’s Traffic Problems. 2017. Available online: https://spectrumlocalnews.com/tx/south-texas-el-paso/news/2017/12/11/ut-creates-artificial-intelligence-to-solve-austin-s-traffic-problems (accessed on 10 February 2021).
  110. Iclodean, C.; Cordos, N.; Varga, B.O. Autonomous shuttle bus for public transportation: A review. Energies 2020, 13, 2917. [Google Scholar] [CrossRef]
  111. Dennis, S.; Paz, A.; Yigitcanlar, T. Perceptions and attitudes towards the deployment of autonomous and connected vehicles: Insights from Las Vegas, Nevada. J. Urban Technol. 2021. [Google Scholar] [CrossRef]
  112. Ioannou, L.; Petrova, M. America is Drowning in Garbage: Now Robots are Being Put on Duty to Help Solve the Recycling Crisis. 2019. Available online: https://www.cnbc.com/2019/07/26/meet-the-robots-being-used-to-help-solve-americas-recycling-crisis.html (accessed on 10 February 2021).
  113. Schmelzer, R. AI is Here to Stay in Your City and Local Government. 2020. Available online: https://www.forbes.com/sites/cognitiveworld/2020/11/08/ai-is-here-to-stay-in-your-city-and-local-government/?sh=28f22e0a77a3 (accessed on 10 February 2021).
  114. Chen, H.; Li, L.; Chen, Y. Explore success factors that impact artificial intelligence adoption on telecom industry in China. J. Manag. Anal. 2020. [Google Scholar] [CrossRef]
  115. Fatima, S.; Desouza, K.; Dawson, G. National strategic artificial intelligence plans. Econ. Anal. Policy 2020, 67, 178–194. [Google Scholar] [CrossRef]
  116. Butcher, J.; Beridze, I. What is the state of artificial intelligence governance globally? Rusi J. 2019, 164, 88–96. [Google Scholar] [CrossRef]
  117. Nagenborg, M. Urban robotics and responsible urban innovation. Ethics Inf. Technol. 2020, 22, 345–355. [Google Scholar] [CrossRef] [Green Version]
  118. Yigitcanlar, T.; Kankanamge, N.; Regona, M.; Maldonado, A.; Rowan, B.; Ryu, A.; Desouza, K.; Corchado, J.; Mehmood, R.; Li, R. Artificial intelligence technologies and related urban planning and development concepts. J. Open Innov. Technol. Mark. Complex. 2020, 6, 187. [Google Scholar] [CrossRef]
  119. Janbi, N.; Katib, I.; Albeshri, A.; Mehmood, R. Distributed artificial intelligence-as-a-service (DAIaaS) for smarter IoE and 6G environments. Sensors 2020, 20, 5796. [Google Scholar] [CrossRef] [PubMed]
  120. Corchado, J.; Chamoso, P.; Hernandez, G.; Gutierrez, A.; Camacho, A.; Briones, A.; Omatu, S. Deepint.net: A rapid deployment platform for smart territories. Sensors 2021, 21, 236. [Google Scholar] [CrossRef] [PubMed]
  121. Repette, P.; Sabatini-Marques, J.; Yigitcanlar, T.; Sell, D.; Costa, E. The evolution of City-as-a-Platform. Land 2021, 10, 33. [Google Scholar] [CrossRef]
  122. Floridi, L.; Cowls, J.; Beltrametti, M.; Chatila, R.; Chazerand, P.; Dignum, V.; Vayena, E. AI4People-an ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds Mach. 2018, 28, 689–707. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  123. European Parliament EU Guidelines on Ethics in Artificial Intelligence: Context and Implementation. 2019. Available online: https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/640163/EPRS_BRI (accessed on 11 January 2021).
  124. Allen, G. Understanding China’s AI Strategy: Clues to Chinese Strategic Thinking on Artificial Intelligence and National security. 2019. Available online: https://nsiteam.com/social/wp-content/uploads/2019/05/CNAS-Understanding-Chinas-AI-Strategy-Gregory-C.-Allen-FINAL-2.15.19.pdf (accessed on 10 January 2021).
  125. Australian Government Artificial Intelligence: Australia’s Ethics Framework. 2018. Available online: https://consult.industry.gov.au/strategic-policy/artificial-intelligence-ethics-framework (accessed on 11 January 2021).
  126. Macnaghten, P.; Owen, R.; Stilgoe, J.; Wynne, B.; Azevedo, A.; de Campos, A.; Garvey, B. Responsible innovation across borders. J. Responsible Innov. 2014, 1, 191–199. [Google Scholar] [CrossRef]
  127. Desouza, K.; Dawson, G.; Chenok, D. Designing, developing, and deploying artificial intelligence systems. Bus. Horiz. 2020, 63, 205–213. [Google Scholar] [CrossRef]
  128. Mora, L.; Deakin, M.; Reid, A. Strategic principles for smart city development. Technol. Forecast. Soc. Chang. 2019, 142, 70–97. [Google Scholar] [CrossRef]
  129. Nam, T.; Pardo, T. Conceptualizing smart city with dimensions of technology, people, and institutions. In 12th Annual International Digital Government Research Conference; Association for Computing Machinery: New York, NY, USA, 2011; pp. 282–291. [Google Scholar]
  130. Tassey, G. A technology-based growth policy. Issues Sci. Technol. 2017, 33, 80–89. [Google Scholar]
  131. Yigitcanlar, T. Smart city beyond efficiency: Technology-policy-community at play for sustainable urban futures. Hous. Policy Debate 2021, 31, 88–92. [Google Scholar] [CrossRef]
  132. Valle-Cruz, D.; Criado, J.; Sandoval-Almazán, R.; Ruvalcaba-Gomez, E. Assessing the public policy-cycle framework in the age of artificial intelligence: From agenda-setting to policy evaluation. Gov. Inf. Q. 2020, 37, 101509. [Google Scholar] [CrossRef]
  133. Ivanov, S.; Webster, C. Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies–a cost-benefit analysis. In International Scientific Conference on Contemporary Tourism; Sofia University: Sofia, Bulgaria, 2017; pp. 1–9. [Google Scholar]
  134. Müller, V. Risks of Artificial Intelligence; CRC Press: Boca Raton, FL, USA, 2016. [Google Scholar]
  135. Rouhiainen, L. Artificial Intelligence: 101 Things you Must Know Today About our Future; Createspace Independent Publishing Platform: New York, NY, USA, 2018. [Google Scholar]
  136. Gunning, D.; Aha, D. DARPA’s explainable artificial intelligence program. Ai Mag. 2019, 40, 44–58. [Google Scholar] [CrossRef]
  137. Arrieta, A.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; Herrera, F. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 2020, 58, 82–115. [Google Scholar] [CrossRef] [Green Version]
  138. Torresen, J. A review of future and ethical perspectives of robotics and AI. Front. Robot. AI 2018, 4, 75. [Google Scholar] [CrossRef]
  139. Mittelstadt, B. Principles alone cannot guarantee ethical AI. Nat. Mach. Intell. 2019, 1, 501–507. [Google Scholar] [CrossRef] [Green Version]
  140. Sun, T.; Medaglia, R. Mapping the challenges of artificial intelligence in the public sector: Evidence from public healthcare. Gov. Inf. Q. 2019, 36, 368–383. [Google Scholar] [CrossRef]
  141. Aoki, N. An experimental study of public trust in AI chatbots in the public sector. Gov. Inf. Q. 2020, 37, 101490. [Google Scholar] [CrossRef]
  142. Osoba, O.; Welser, W. An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence; Rand Corporation: Santa Monica, CA, USA, 2017. [Google Scholar]
  143. Park, S.; Humphry, J. Exclusion by Design: Intersections of Social, Digital and Data Exclusion. Inf. Commun. Soc. 2019, 22, 934–953. [Google Scholar] [CrossRef]
  144. Curtis, C.; Gillespie, N.; Lockey, S. Australians have Low Trust in Artificial Intelligence and Want it to be Better Regulated. 2020. Available online: https://theconversation.com/australians-have-low-trust-in-artificial-intelligence-and-want-it-to-be-better-regulated-148262 (accessed on 10 January 2021).
  145. Harrison, T.M.; Luna-Reyes, L.F. Cultivating trustworthy artificial intelligence in digital government. Soc. Sci. Comput. Rev. 2020. [Google Scholar] [CrossRef]
  146. Sander, M. From Robodebt to Racism: What can go Wrong When Governments Let Algorithms Make the Decisions. 2020. Available online: https://theconversation.com/from-robodebt-to-racism-what-can-go-wrong-when-governments-let-algorithms-make-the-decisions-132594 (accessed on 11 January 2021).
  147. Feng, J.; Lansford, J.; Katsoulakis, M.; Vlachos, D. Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences. Sci. Adv. 2020, 6, eabc3204. [Google Scholar] [CrossRef] [PubMed]
  148. Janssen, M.; Brous, P.; Estevez, E.; Barbosa, L.S.; Janowski, T. Data governance: Organizing data for trustworthy Artificial Intelligence. Gov. Inf. Q. 2020, 37, 101493. [Google Scholar] [CrossRef]
  149. Kokuryo, J.; Walsh, T.; Maracke, C. AI for Everyone: Benefitting from and Building Trust in the Technology; AI Access: Sydney, Australia, 2020. [Google Scholar]
  150. Alliance, A. Artificial intelligence and frugal innovation. In Management and Business Education in the Time of Artificial Intelligence; Information Age: Charlotte, NC, USA, 2019; pp. 55–76. [Google Scholar]
  151. Agarwal, N.; Chung, K.; Brem, A. 8 new technologies for frugal innovation. In Frugal Innovation: A Global Research Companion; Routledge: New York, NY, USA, 2019. [Google Scholar]
  152. Fisher, E. Reinventing responsible innovation. J. Responsible Innov. 2020, 7, 1–5. [Google Scholar] [CrossRef]
  153. Kassens-Noor, E.; Hintze, A. Cities of the future? The potential impact of artificial intelligence. AI 2020, 1, 192–197. [Google Scholar] [CrossRef]
  154. Kassens-Noor, E.; Wilson, M.; Kotval-Karamchandani, Z.; Cai, M.; Decaminada, T. Living with autonomy: Public perceptions of an AI-mediated future. J. Plan. Educ. Res. 2021. [Google Scholar] [CrossRef]
  155. Miailhe, N.; Hodes, C. Making the AI Revolution Work for Everyone. 2017. Available online: http://ai-initiative.org/wp-content/uploads/2017/08/Making-the-AI-Revolution-work-for-everyone.-Report-to-OECD.-MARCH-2017pdf (accessed on 11 January 2021).
  156. Davenport, T. The AI Advantage: How to Put the Artificial Intelligence Revolution to Work; MIT Press: Cambridge, MA, USA, 2018. [Google Scholar]
  157. Alonso, C.; Berg, A.; Kothari, S.; Papageorgiou, C.; Rehman, S. Will the AI Revolution Cause a Great Divergence? 2020. Available online: https://www.imf.org/~/media/Files/Publications/WP/2020/English/wpiea2020184-print-pdf.ashx (accessed on 11 January 2021).
  158. Walsh, T. The AI Revolution. 2017. Available online: https://prod65.education.nsw.gov.au/content/dam/main-education/teaching-and-learning/education-for-a-changing-world/media/documents/The_AI_Revolution_TobyWalsh.pdf (accessed on 11 January 2021).
  159. Gandhi, S. Social Concerns about Artificial Intelligence. 2018. Available online: https://medium.com/@sharad.gandhi/social-concerns-about-artificial-intelligence-93e939b88a8c (accessed on 15 January 2021).
  160. Walsh, T. Artificial Intelligence should Benefit Society, not Create Threats. 2015. Available online: https://theconversation.com/artificial-intelligence-should-benefit-society-not-create-threats-36240 (accessed on 15 January 2021).
  161. Glance, D. What should Governments be doing about the Rise of Artificial Intelligence? 2017. Available online: https://theconversation.com/what-should-governments-be-doing-about-the-rise-of-artificial-intelligence-86561 (accessed on 15 January 2021).
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Figure 1. Conceptual framework of responsible urban innovation with local government artificial intelligence (AI).
Figure 1. Conceptual framework of responsible urban innovation with local government artificial intelligence (AI).
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MDPI and ACS Style

Yigitcanlar, T.; Corchado, J.M.; Mehmood, R.; Li, R.Y.M.; Mossberger, K.; Desouza, K. Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda. J. Open Innov. Technol. Mark. Complex. 2021, 7, 71. https://0-doi-org.brum.beds.ac.uk/10.3390/joitmc7010071

AMA Style

Yigitcanlar T, Corchado JM, Mehmood R, Li RYM, Mossberger K, Desouza K. Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda. Journal of Open Innovation: Technology, Market, and Complexity. 2021; 7(1):71. https://0-doi-org.brum.beds.ac.uk/10.3390/joitmc7010071

Chicago/Turabian Style

Yigitcanlar, Tan, Juan M. Corchado, Rashid Mehmood, Rita Yi Man Li, Karen Mossberger, and Kevin Desouza. 2021. "Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda" Journal of Open Innovation: Technology, Market, and Complexity 7, no. 1: 71. https://0-doi-org.brum.beds.ac.uk/10.3390/joitmc7010071

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