In an era in which technological advancements have a profound impact on our cities and societies, it is crucial to ensure that digital technology is not only driven by technological progress with economic goals but that it can also fulfill moral and social responsibilities. Hence, it is needed to advocate for ‘Responsible Innovation and Technology’ (RIT) to ensure cities and societies can harness the potential of technological progress and prosperity while safeguarding the well-being of individuals and communities. This study conducts a PRISMA review to explore and understand RIT concepts and its characteristics. In this study, we emphasize that RIT should deliver acceptable, accessible, trustworthy, and well governed technological outcomes, while ensuring these outcomes are aligned with societal desirability and human values, and should also be responsibly integrated into our cities and societies. The main contribution of this study is to identify and clarify the key characteristics of RIT, which has not been performed in such detail so far. The study, reported in this paper, also broadens the understanding of responsible research and innovation in the technosphere, particularly from a bottom-up perspective. Furthermore, the paper develops an RIT conceptual framework outlining its possible design procedures, which could be used by governments, companies, practitioners, researchers, and other stakeholders as a tool to address the grand challenges that accompany technological and scientific progress. The framework also informs science, technology, and innovation policy.

As digital technology continues to evolve rapidly and get integrated into various aspects of our cities and societies, the alignment of technological advancements with societal values becomes paramount. The evolving socio-technical landscape has prompted an increased focus on responsible innovation and technology (RIT) among technology companies, driven by mounting public scrutiny, regulatory pressure, and concerns about reputation and long-term sustainability. This study contributes to the ongoing discourse on responsible practices by conducting a policy review that delves into insights from the most influential high-tech companies’—so-called tech giants’—RIT guidance. The findings disclose that (a) leading high-tech companies have started to focus on RIT; (b) the main RIT policy focus of the leading high-tech companies is artificial intelligence; (c) trustworthiness and acceptability of technology are the most common policy areas; (d) affordability related to technology outcomes and adoption is almost absent from the policy; and (e) sustainability considerations are rarely part of the RIT policy, but are included in annual corporate reporting. Additionally, this paper proposes a RIT assessment framework that integrates views from the policy community, academia, and the industry and can be used for evaluating how well high-tech companies adhere to RIT practices. The knowledge assembled in this study is instrumental in advancing RIT practices, ultimately contributing to technology-driven cities and societies that prioritise human and social well-being.

Cognitive Computing Systems (CCSs) are increasing in prominence in the public sector. This paper develops a framework drawing on public value and information technology service management literature to guide the management of CCSs in the public sector. We draw on academic literature, gray literature, legislation and government reports, and examples on CCS initiatives in the public sector to develop insights for research and practice. We then outline the themes and present the insights in the form of guiding principles and specific (detailed) recommendations. These include guiding principles and recommendations for establishing legitimacy, understanding the required capabilities, executing capabilities, creating and measuring public value.

Most managers lack guidance on how to address uncertainties associated with deploying artificial intelligence (AI) technologies. Drawing on our experiences with projects in the public sector, we describe consistent patterns related to what AI deployment uncertainties are and strategies to successfully tackle them. Synthesizing these patterns, we arrive at five major areas of uncertainties, and relevant solution directions for addressing them.