Artificial Intelligence Governance Research Articles

Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK.

Technological advancements in computer science have started to bring artificial intelligence (AI) from the bench closer to the bedside. While there is still lots to do and improve, AI models in medical imaging and radiotherapy are rapidly being developed and increasingly deployed in clinical practice. At the same time, AI governance frameworks are still under development. Clinical practitioners involved with procuring, deploying, and adopting AI tools in the UK should be well-informed about these AI governance frameworks. This scoping review aimed to map out available literature on AI governance in the UK, focusing on medical imaging and radiotherapy. Searches were performed on Google Scholar, Pubmed, and the Cochrane Library, between June and July 2022. Of 4225 initially identified sources, 35 were finally included in this review. A comprehensive conceptual AI governance framework was proposed, guided by the need for rigorous AI validation and evaluation procedures, the accreditation rules and standards, and the fundamental ethical principles of AI. Fairness, transparency, trustworthiness, and explainability should be drivers of all AI models deployed in clinical practice. Appropriate staff education is also mandatory to ensure AI's safe and responsible use. Multidisciplinary teams under robust leadership will facilitate AI adoption, and it is crucial to involve patients, the public, and practitioners in decision-making. Collaborative research should be encouraged to enhance and promote innovation, while caution should be paid to the ongoing auditing of AI tools to ensure safety and clinical effectiveness.

Artificial intelligence in government: Concepts, standards, and a unified framework

Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI.

Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance

The utilization of artificial intelligence (AI) applications has experienced tremendous growth in recent years, bringing forth numerous benefits and conveniences. However, this expansion has also provoked ethical concerns, such as privacy breaches, algorithmic discrimination, security and reliability issues, transparency, and other unintended consequences. To determine whether a global consensus exists regarding the ethical principles that should govern AI applications and to contribute to the formation of future regulations, this paper conducts a meta-analysis of 200 governance policies and ethical guidelines for AI usage published by public bodies, academic institutions, private companies, and civil society organizations worldwide. We identified at least 17 resonating principles prevalent in the policies and guidelines of our dataset, released as an open source database and tool. We present the limitations of performing a global-scale analysis study paired with a critical analysis of our findings, presenting areas of consensus that should be incorporated into future regulatory efforts.

Knowledge mapping of an artificial intelligence application scenario: A bibliometric analysis of the basic research of data-driven autonomous vehicles

With the rapid development and maturation of basic research in related fields such as deep learning and convolutional neural networks, artificial intelligence (AI) has become a policy hotspot of high interest in all major economies. Furthermore, the governance of application scenarios has become one of the important topics of AI governance policy research. The practice of AI governance policy depends on a change in the paradigm of relevant governance policy research theories, and there is an urgent need to empirically analyze the structural characteristics of the knowledge evolution of AI application scenarios. This study conducts a bibliometric analysis of the basic research trends in autonomous vehicles, which is one of the most important application scenarios of AI. The study empirically analyzes the relevant literature data from the Science Citation Index Expanded (SCI-Expanded) and Social Sciences Citation Index (SSCI) databases of Web of Science, based on both technical and social dimensions through the knowledge mapping analysis tools in Bibliometrix (in R environment). Based on the empirical analysis, the results show that the basic research on autonomous vehicles is characterized by strong data-driven innovation under the influence of AI. The fusion of AI and basic research on autonomous vehicles has become a major driver of knowledge innovation in this domain while there is a lack of the integration of social science research.

Towards Defining a Trustworthy Artificial Intelligence System Development Maturity Model

ABSTRACT The trustworthiness of artificial intelligence (AI) has been challenged for quite some time. The AI results will be trustworthy and reliable if trust-related principles are built into the AI system along with effective AI governance and monitoring. Therefore, this study seeks to define a trustworthy AI system development maturity model to assist firms in understanding where they stand in terms of developing a trustworthy AI platform. Since trust is time-bound, it is difficult to regain once lost, and this maturity model helps to establish robust controls and governance mechanisms in AI systems from their inception to minimize risk and establish trust. Hence, this study starts by defining the trustworthy enhancing techniques (TET) to build a trustworthy AI system, then advances on to the Trustworthy AI system maturity model (TAS-MM). The study’s findings include a maturity model with four stages of trustworthy AI system growth and maturity progression.

A reality check and a way forward for the global governance of artificial intelligence

ABSTRACT Global governance of artificial intelligence (AI) must grapple with four monumental challenges. AI is a tough problem to govern given the speed, scale, and uncertainty of its progress. Various aspects of the AI problem require governing because of the range of benefits, risks, and impacts on other global issues. Multilateral efforts on AI are nascent, as is national-level policy. And the multilateral system is under immense pressure from institutional gridlock, fragmentation, and geopolitical competition. No one global governance model for AI is perfect, or desirable. Instead, policymakers must pursue several governance models, each starting in a targeted and focused manner before evolving. They must make clear what policy outcomes are being sought and which institutional functions are needed to reach those outcomes. AI governance within regional and multilateral issue-based groupings would commit nations to action and test models for governing AI globally. And national champions will be critical to success. This pragmatic yet optimistic path will allow humanity to maximize the benefits of artificial intelligence applications and distribute them as widely as possible, while mitigating harms and reducing risks as effectively as possible.

Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore

Artificial intelligence (AI)-driven clinical decision support systems (CDSSs) can augment antibiotic decision-making capabilities, but physicians' hesitancy in adopting them may undermine their utility. We conducted a cross-country comparison of physician perceptions on the barriers and facilitators in accepting an AI-enabled CDSS for antibiotic prescribing. We conducted in-depth interviews with physicians from the National Centre for Infectious Diseases (NCID), Singapore, and Christian Medical College Vellore (CMCV), India, between April and December 2022. Our semi-structured in-depth interview guides were anchored on Venkatesh's UTAUT model. We used clinical vignettes to illustrate the application of AI in clinical decision support for antibiotic prescribing and explore medico-legal concerns. Most NCID physicians felt that an AI-enabled CDSS could facilitate antibiotic prescribing, while most CMCV physicians were sceptical about the tool's utility. The hesitancy in adopting an AI-enabled CDSS stems from concerns about the lack of validated and successful examples, fear of losing autonomy and clinical skills, difficulty of use, and impediment in work efficiency. Physicians from both sites felt that a user-friendly interface, integration with workflow, transparency of output, a guiding medico-legal framework, and training and technical support would improve the uptake of an AI-enabled CDSS. In conclusion, the acceptance of AI-enabled CDSSs depends on the physician's confidence with the tool's recommendations, perceived ease of use, familiarity with AI, the organisation's digital culture and support, and the presence of medico-legal governance of AI. Progressive implementation and continuous feedback are essential to allay scepticism around the utility of AI-enabled CDSSs.

An extended review on cyber vulnerabilities of AI technologies in space applications: Technological challenges and international governance of AI

The aerospace community and industry have recently shown increasing interest towards the use of Artificial Intelligence (AI) for space applications, partially driven by the recent development of the NewSpace economy. AI has already come into extensive use in spacecraft operations, for example to support efficient operations of satellite constellations and system health management. However, since most critical infrastructures rely on space systems, the use of new technologies, such as AI algorithms or increased system autonomy on-board, introduces further vulnerabilities on the system level. As a matter of fact, AI cyber security is becoming an important aspect to ensure space safety and operational security. Apart from identifying new vulnerabilities that AI systems may introduce to space assets, this paper seeks for safety guidelines and technical standardisations developed for terrestrial applications that can be applicable to AI systems in space. Existing policy guidance for cybersecurity and AI, especially for the European context, is discussed. To promote the safe use of AI technologies in space this work underlines the urgency for policymakers, governance, and technical institutions to initiate or further support the development of a suitable framework to address the new cyber-vulnerabilities introduced by AI technologies when applied to space systems. The paper suggests a regulatory approach based on technical standardisation in the field of AI, which is built upon a multidisciplinary research of AI applications in non-space sectors where the level of autonomy is more advanced.

Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model

A broad range of companies around the world has welcomed artificial intelligence (AI) technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis. This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies, which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment. To obtain this goal and inspired by a model ensemble, we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing, fuzzy set theory, and a multi-attribute decision making algorithm. The results display that the order of priority in improvement—(A) AI application strategy, (B) AI governance, (D) the human factor, and (C) data infrastructure and data quality—is based on the magnitude of their impact. This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment.

Corpus-based Critical Discourse Analysis on AI Policy: A Comparison Between North America and Developing Countries in East Asia

Artificial Intelligence (AI) has undergone a global transformative impact on people’s lives. However, the realm of empirical research focused on AI within applied linguistics remains limited, predominantly dwelling on qualitative rhetoric and narrative analysis. This research aims to fill this gap by shedding light on the discursive constructions of AI policies between North America and developing countries in East Asia based on corpus data. Two corpora are constructed for quantitative study, utilizing keywords, collocates, and concordance analysis, and two dimensions of dialectical-relational approach are adopted to delve into the results. The findings reveals that both EA and NA prioritize AI-related efforts, but EA emphasizes more on economic and societal applications, while NA highlights governmental sectors’ efforts, cooperation with allies, and national security in AI deployment. The results are closely associated with their social context. This nuanced understanding of AI policy research holds significant implications for policymakers, stakeholders, and scholars actively involved in shaping responsible AI regulations and governance practices on an international scale.

Sustainability challenges of artificial intelligence and Citizens' regulatory preferences

Following the idea that citizens' regulatory preferences matter for the acceptance and success of policy measures, this paper investigates citizens' support for the regulation of Artificial Intelligence (AI). The focus lies on the transparency and the ecological sustainability of AI as two key challenges tied to possible long-term impacts on societies. Findings from survey data representative of the German population show overall moderate to strong support for the government regulating AI. Perceived regulatory competence of policymakers is positively associated with citizens' support for soft regulation. Lower trust in tech companies is linked to a lower readiness to rely on soft regulation, but not to more demand for hard regulation. While regulatory preferences barely map on political conflict lines, people's future orientation emerges as a strong correlate of support for both hard and soft regulation. Citizens thus seem to perceive a clear sustainability dimension in the development and governance of AI.

Exploring the negative impacts of artificial intelligence in government: the dark side of intelligent algorithms and cognitive machines

This research proposes a framework for the negative impacts of artificial intelligence (AI) in government by classifying 14 topics of its dark side into five socio technical categories. The framework is based on a systematic literature review and highlights that the dark side is predominantly driven by political, legal, and institutional aspects, but it is also influenced by data and technology. Lack of understanding of AI outcomes, biases, and errors, as well as manipulation of intelligent algorithms and cognitive machines are contributing factors. The public sector should create knowledge about AI from an ethical, inclusive, and strategic perspective, involving experts from different areas. Points for practitioners Government officials and other decision-makers should be aware of the potential benefits of artificial intelligence, but also of the dark side, and try to avoid those potential negative consequences.

To govern or be governed: an integrated framework for AI governance in the public sector

Abstract There has been a significantly increased amount of literature on various aspects of artificial intelligence (AI), but research on AI governance has been a considerably underdeveloped area. This study proposes a comprehensive framework for designing AI governance in the public sector to overcome the limitations of previous studies that primarily dealt with the fragmentary aspect of AI. Also, we applied the developed framework to the case of Korea by combining it with the rank-order survey questions that target experts. This case study presents how to use the framework and provides insight for other countries.

The Different Faces of AI Ethics Across the World: A Principle-to-Practice Gap Analysis

Artificial Intelligence (AI) is transforming our daily life with many applications in healthcare, space exploration, banking and finance. This rapid progress in AI has brought increasing attention to the potential impacts of AI technologies on society, with ethically questionable consequences. In recent years, several ethical principles have been released by governments, national and international organisations. These principles outline high-level precepts to guide the ethical development, deployment, and governance of AI. However, the abstract nature, diversity, and context-dependency of these principles make them difficult to implement and operationalize, resulting in gaps between principles and their execution. Most recent work analysed and summarized existing AI principles and guidelines but did not provide findings on principle-to-practice gaps nor how to mitigate them. These findings are particularly important to ensure that AI practical guidances are aligned with ethical principles and values. In this paper, we provide a contextual and global evaluation of current ethical AI principles for all continents, with the aim to identify potential principle characteristics tailored to specific countries or applicable across countries. Next, we analyze the current level of AI readiness and current practical guidances of ethical AI principles in different countries, to identify gaps in the practical guidance of AI principles and their causes. Finally, we propose recommendations to mitigate the principle-to-practice gaps.

Artificial intelligence governance: Ethical considerations and implications for social responsibility

AbstractA number of articles are increasingly raising awareness on the different uses of artificial intelligence (AI) technologies for customers and businesses. Many authors discuss about their benefits and possible challenges. However, for the time being, there is still limited research focused on AI principles and regulatory guidelines for the developers of expert systems like machine learning (ML) and/or deep learning (DL) technologies. This research addresses this knowledge gap in the academic literature. The objectives of this contribution are threefold: (i) It describes AI governance frameworks that were put forward by technology conglomerates, policy makers and by intergovernmental organizations, (ii) It sheds light on the extant literature on ‘AI governance’ as well as on the intersection of ‘AI’ and ‘corporate social responsibility’ (CSR), (iii) It identifies key dimensions of AI governance, and elaborates about the promotion of accountability and transparency; explainability, interpretability and reproducibility; fairness and inclusiveness; privacy and safety of end users, as well as on the prevention of risks and of cyber security issues from AI systems. This research implies that all those who are involved in the research, development and maintenance of AI systems, have social and ethical responsibilities to bear toward their consumers as well as to other stakeholders in society.

Challenges for AI Regulation in Health and for Healthcare Organizations: Notes from the University of Florida's NSF-Sponsored Workshop on AI Governance

Artificial intelligence (AI) has impacted human life at many levels, entailing economic and societal changes. AI algorithms are increasingly used by organizations to generate predictions that feed into decisions (e.g., who is eligible for insurance coverage, approved for bank loans, selected for job interviews). Since the data used for developing the algorithms can contain bias such as gender or racial prejudice, AI predictions can become discriminatory. For-profit and not-for-profit organizations face the hurdles of developing, applying, and maintaining governance of AI, making sure that goal optimization responds to ethical and fairness values.

AI and Global Governance: Modalities, Rationales, Tensions

Artificial intelligence (AI) is a salient but polarizing issue of recent times. Actors around the world are engaged in building a governance regime around it. What exactly the “it” is that is being governed, how, by who, and why—these are all less clear. In this review, we attempt to shine some light on those questions, considering literature on AI, the governance of computing, and regulation and governance more broadly. We take critical stock of the different modalities of the global governance of AI that have been emerging, such as ethical councils, industry governance, contracts and licensing, standards, international agreements, and domestic legislation with extraterritorial impact. Considering these, we examine selected rationales and tensions that underpin them, drawing attention to the interests and ideas driving these different modalities. As these regimes become clearer and more stable, we urge those engaging with or studying the global governance of AI to constantly ask the important question of all global governance regimes: Who benefits?

AI governance: themes, knowledge gaps and future agendas

PurposeFollowing 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.Design/methodology/approachThe 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).FindingsThe 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.Research limitations/implicationsTo 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.Practical implicationsFor practitioners, the authors highlight training and awareness, stakeholder management and the crucial role of organizational culture, including senior management commitment.Social implicationsFor society, the authors review elucidates the multitude of stakeholders involved in AI governance activities and complexities related to balancing the needs of different stakeholders.Originality/valueBy 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.

Discerning Between the “Easy” and “Hard” Problems of AI Governance

While there is widespread consensus that artificial intelligence (AI) needs to be governed owing to its rapid diffusion and societal implications, the current scholarly discussion on AI governance is dispersed across numerous disciplines and problem domains. This paper clarifies the situation by discerning two problem areas, metaphorically titled the “easy” and “hard” problems of AI governance, using a dialectic theory synthesis approach. The “easy problem” of AI governance concerns how organizations’ design, development, and use of AI systems align with laws, values, and norms stemming from legislation, ethics guidelines, and the surrounding society. Organizations can provisionally solve the “easy problem” by implementing appropriate organizational mechanisms to govern data, algorithms, and algorithmic systems. The “hard problem” of AI governance concerns AI as a general-purpose technology that transforms organizations and societies. Rather than a matter to be resolved, the “hard problem” is a sensemaking process regarding socio-technical change. Partial solutions to the “hard problem” may open unforeseen issues. While societies should not lose track of the “hard problem” of AI governance, there is significant value in solving the “easy problem” for two reasons. First, the “easy problem” can be provisionally solved by tackling bias, harm, and transparency issues. Second, solving the “easy problem” helps solve the “hard problem”, as responsible organizational AI practices create virtuous rather than vicious cycles.

Policy Studies Yearbook Annual Review 2022–23: Note from the editor and call for papers

<i>Policy Studies Yearbook Annual Review</i> 2022–23: Note from the editor and call for papers