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This study aims to investigate how living lab (LL) activities align with responsible research and innovation (RRI) principles, particularly in artificial intelligence (AI)-driven digital transformation (DT) processes. The study seeks to define a framework termed “responsible living lab” (RLL), emphasizing transparency, stakeholder engagement, ethics and sustainability. This emerging issue paper also proposes several directions for future researchers in the field.

The research methodology involved a literature review complemented by insights from a workshop on defining RLLs. The literature review followed a concept-centric approach, searching key journals and conferences, yielding 32 relevant articles. Backward and forward citation analysis added 19 more articles. The workshop, conducted in the context of UrbanTestbeds.JR and SynAir-G projects, used a reverse brainstorming approach to explore potential ethical and responsible issues in LL activities. In total, 13 experts engaged in collaborative discussions, highlighting insights into AI’s role in promoting RRI within LL activities. The workshop facilitated knowledge sharing and a deeper understanding of RLL, particularly in the context of DT and AI.

This emerging issue paper highlights ethical considerations in LL activities, emphasizing user voluntariness, user interests and unintended participation. AI in DT introduces challenges like bias, transparency and digital divide, necessitating responsible practices. Workshop insights underscore challenges: AI bias, data privacy and transparency; opportunities: inclusive decision-making and efficient innovation. The synthesis defines RLLs as frameworks ensuring transparency, stakeholder engagement, ethical considerations and sustainability in AI-driven DT within LLs. RLLs aim to align DT with ethical values, fostering inclusivity, responsible resource use and human rights protection.

The proposed definition of RLL introduces a framework prioritizing transparency, stakeholder engagement, ethics and sustainability in LL activities, particularly those involving AI for DT. This definition aligns LL practices with RRI, addressing ethical implications of AI. The value of RLL lies in promoting inclusive and sustainable innovation, prioritizing stakeholder needs, fostering collaboration and ensuring environmental and social responsibility throughout LL activities. This concept serves as a foundational step toward a more responsible and sustainable LL approach in the era of AI-driven technologies.

India is a fast-growing economy, that has a majority share in the global information technology industry (IT). Rapid urbanisation and modernisation in India have strained its energy sector, which is being reformed to cope. Despite being the global IT heart and having above average research output in the field of artificial intelligence (AI), India has not yet managed to leverage its benefits to the full. This study aims to address the role of AI and information management (IM) in India’s energy transition to highlight the challenges and barriers to its development and use in the energy sector.

The study, through analysis of proposed strategies, current policies, available literature and reports, discusses the role of AI and IM in the energy transition in India, highlighting the current situation and challenges.

The results show dispersed research and development incentives for IT in the Indian energy sector; however, the needed holistic top-down approach is lacking, calling for due attention in this matter. Adaptive and swift actions from policymakers towards AI and IM are warranted in India.

The ongoing transition of the Indian energy sector with the integration of smart technologies would result in increased access to big data. Extracting the maximum benefits from this would require a comprehensive AI and IM policy.

The revolution in AI and robotics must be carried out in line with sustainable development goals, to support climate action and to consider privacy issues – both areas in India must be strengthened.

The paper offers an original discussion on certain applicable solutions regarding the energy transition of AI coming from the Global South; they are based on lessons learned from the Indian case studies presented in this study.

Manufacturing small and medium-sized enterprises (SMEs) have already noticed the tangible benefits offered by artificial intelligence (AI). Several approaches have been proposed with a view to support them in the processes entailed in this innovation path. These include multisided platforms created to enable the connection between SMEs and AI developers, making it easier for them to network each other. While such platforms are complex, they facilitate simultaneous interaction with several stakeholders and reaching out to new potential users (both SMEs and AI developers), through a collaboration with supporting ecosystems such as digital innovation hubs (DIHs).

Mixed methods were used. The literature review was performed to identify the existing approaches within and outside the manufacturing domain. Computer-assisted telephonic (in-depth) interviewing , was conducted to include perspectives of AI platform stakeholders and collect primary data from various European countries.

Several challenges and barriers for AI platform stakeholders were identified alongside the corresponding best practices and guidelines on how to address them.

An effective approach was proposed to provide support to the industrial platform managers in this field, by developing guidelines and best practices on how a platform should build its services to support the ecosystem.

This paper aims to critically examine the digital transformation of anti-money laundering (AML) and countering the financing of terrorism (CFT) in light of the Financial Action Task Force (FATF) San Jose principles, the Organisation for Economic Co-operation and Development (OECD) principles for artificial intelligence (AI) and the proposed European Union (EU) Artificial Intelligence Act. The authors argue that AI tools can revolutionize AML/CFT and asset recovery, but there is a need to strike a balance between optimizing AML efficiency and safeguarding fundamental rights.

This paper draws on reports, legislation, legal scholarships and other open-source data on the digital transformation of AML/CFT, particularly the deployment of AI in this context.

A new regulatory framework with robust safeguards is necessary to mitigate the risks associated with the use of new technologies in the AML context.

This study is one of the first to examine the use of AI in the AML/CFT context in light of the FATF San Jose principles, the OECD AI principles and the proposed EU AI Act.

This paper aims to describe the results of applying ethical AI requirements to a healthcare use case. The purpose of this study is to investigate the effectiveness of using open educational resources for Trustworthy AI to provide recommendations to an AI solution within the healthcare domain.

This study utilizes the Hackathon method as its research methodology. Hackathons are short events where participants share a common goal. The purpose of this to determine the efficacy of the educational resources provided to the students. To achieve this objective, eight teams of students and faculty members participated in the Hackathon. The teams made suggestions for healthcare use case based on the knowledge acquired from educational resources. A research team based at the university hosting the Hackathon devised the use case. The healthcare research team participated in the Hackathon by presenting the use case and subsequently analysing and evaluating the utility of the outcomes.

The Hackathon produced a framework of proposed recommendations for the introduced healthcare use case, in accordance with the EU's requirements for Trustworthy AI.

The educational resources have been applied to one use-case.

This is the first time that open educational resources for Trustworthy AI have been utilized in higher education, making this a novel study. The university hosting the Hackathon has been the coordinator for the Trustworthy AI Hackathon (as partner to Trustworthy AI project).

The purpose of this paper is to raise awareness on the need for a more comprehensive approach on the interdependence between artificial intelligence (AI) and environmental sustainability. It provides an overview of existing sustainable AI policy initiatives at the national and regional level. More precisely, it discusses whether existing European Union (EU) environmental policies are suitable for the AI era or whether new regulations are needed in this field. Finally, this paper assesses cross-fertilisation opportunities between the EU and non-EU countries.

This study is based on a qualitative analysis of sustainable applications of AI and the sustainability of AI. Emphasis is laid on the latter, and a “sustainable by design” approach is proposed, which in essence is a prerequisite for transparent, responsible and human-centred AI systems. The analysis primarily focuses on environmental sustainability.

The majority of studies focus on how to use AI to protect the environment with very little attention paid to sustainable design of AI. On the other hand, the EU’s comprehensive approach towards sustainable AI is closest to promoting “sustainable by design” AI. Several ways have been identified in which the EU’s actions can be translated beyond its borders.

One of the largest limitations of this study is its moderate scope. This paper is confined to the EU and as such provides a limited assessment of global policies and measures on the interplay between sustainability and AI. Consequently, the paper did not provide an in-depth analysis of environmental policies worldwide that could help provide a better picture of possible cooperation areas or common grounds. Another limitation of this study is that it primarily focuses on environmental aspects and as such accords little attention to the economic and social pillars of sustainability.

With less than 10 years to go before reaching the sustainable development goal deadline, this study can help stakeholders better understand what is being done worldwide in terms of sustainable AI. Moreover, given that the technology is still in its early phase, this study can inspire a “sustainable by design” approach to the development of AI technologies.

All national AI strategies published by 1 June 2021 were analysed to identify whether and to what extent they prioritise the interplay between environment and AI. Furthermore, the authors also looked at the EU policy and how it aims to address AI from a sustainable perspective.

The purpose of this paper is clearly illustrate this convergence and the prescriptive recommendations that such documents entail. There is a significant amount of research into the ethical consequences of artificial intelligence (AI). This is reflected by many outputs across academia, policy and the media. Many of these outputs aim to provide guidance to particular stakeholder groups. It has recently been shown that there is a large degree of convergence in terms of the principles upon which these guidance documents are based. Despite this convergence, it is not always clear how these principles are to be translated into practice.

In this paper, the authors move beyond the high-level ethical principles that are common across the AI ethics guidance literature and provide a description of the normative content that is covered by these principles. The outcome is a comprehensive compilation of normative requirements arising from existing guidance documents. This is not only required for a deeper theoretical understanding of AI ethics discussions but also for the creation of practical and implementable guidance for developers and users of AI.

In this paper, the authors therefore provide a detailed explanation of the normative implications of existing AI ethics guidelines but directed towards developers and organisational users of AI. The authors believe that the paper provides the most comprehensive account of ethical requirements in AI currently available, which is of interest not only to the research and policy communities engaged in the topic but also to the user communities that require guidance when developing or deploying AI systems.

The authors believe that they have managed to compile the most comprehensive document collecting existing guidance which can guide practical action but will hopefully also support the consolidation of the guidelines landscape. The authors’ findings should also be of academic interest and inspire philosophical research on the consistency and justification of the various normative statements that can be found in the literature.

This study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.

In this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.

Four main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.

The volunteer basis of this study meant that the sample was not representative or generalisable.

Although the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

Following the surge of documents laying out organizations' ethical principles for their use of artificial intelligence (AI), there is a growing demand for translating ethical principles to practice through AI governance (AIG). AIG has emerged as a rapidly growing, yet fragmented, research area. This paper synthesizes the organizational AIG literature by outlining research themes and knowledge gaps as well as putting forward future agendas.

The authors undertake a systematic literature review on AIG, addressing the current state of its conceptualization and suggesting future directions for AIG scholarship and practice. The review protocol was developed following recommended guidelines for systematic reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

The results of the authors’ review confirmed the assumption that AIG is an emerging research topic with few explicit definitions. Moreover, the authors’ review identified four themes in the AIG literature: technology, stakeholders and context, regulation and processes. The central knowledge gaps revealed were the limited understanding of AIG implementation, lack of attention to the AIG context, uncertain effectiveness of ethical principles and regulation, and insufficient operationalization of AIG processes. To address these gaps, the authors present four future AIG agendas: technical, stakeholder and contextual, regulatory, and process. Going forward, the authors propose focused empirical research on organizational AIG processes, the establishment of an AI oversight unit and collaborative governance as a research approach.

To address the identified knowledge gaps, the authors present the following working definition of AIG: AI governance is a system of rules, practices and processes employed to ensure an organization's use of AI technologies aligns with its strategies, objectives, and values, complete with legal requirements, ethical principles and the requirements set by stakeholders. Going forward, the authors propose focused empirical research on organizational AIG processes, the establishment of an AI oversight unit and collaborative governance as a research approach.

For practitioners, the authors highlight training and awareness, stakeholder management and the crucial role of organizational culture, including senior management commitment.

For society, the authors review elucidates the multitude of stakeholders involved in AI governance activities and complexities related to balancing the needs of different stakeholders.

By delineating the AIG concept and the associated research themes, knowledge gaps and future agendas, the authors review builds a foundation for organizational AIG research, calling for broad contextual investigations and a deep understanding of AIG mechanisms. For practitioners, the authors highlight training and awareness, stakeholder management and the crucial role of organizational culture, including senior management commitment.

This article ties in with current debates on the digital transformation of society and the consequent work changes. Using an artificial intelligence (AI)-based accounting context, the focus of this paper is on actors, roles and tasks and related skills on an individual level. The authors look at the effect of AI-based “smart” technology on the workforce in the broader accounting profession taking an intrafirm perspective, yet acknowledging that the digital transformation encompasses a much larger field in the financial sector.

The authors conduct a Delphi study to identify the new roles and tasks in future accounting. In addition, the authors use expert workshops to clarify the related tasks and skills and determine whether either humans or AI-based technologies perform the roles or collaborate in professional accounting occupations.

The results show that tasks and skills for existing professional occupations in the broader acounting context will be subject to major changes in the next 10 years due to (AI based) digital technologies, while “core” roles and tasks will continue to exist in the future, some will not be performed by humans but by AI-based technology. For other “new” roles, humans will need to make informed use of digital technologies and, to some extent, collaborate with AI-based technology.

The authors look at the effect of AI-based “smart” technology on the workforce in the broader accounting profession, taking an intrafirm perspective.

This article ties in with current debates on the digital transformation of society and the consequent work changes. Using an AI-based accounting context, the focus of this paper is on the new and adapted roles and tasks.

The comprehensive analysis based on the Delphi study and expert workshops provide ample innovative ground for future research on the impact of AI on organisations and society.