Artificial Intelligence Ecosystem Research Articles

Ethics dumping in artificial intelligence

Artificial Intelligence (AI) systems encode not just statistical models and complex algorithms designed to process and analyze data, but also significant normative baggage. This ethical dimension, derived from the underlying code and training data, shapes the recommendations given, behaviors exhibited, and perceptions had by AI. These factors influence how AI is regulated, used, misused, and impacts end-users. The multifaceted nature of AI’s influence has sparked extensive discussions across disciplines like Science and Technology Studies (STS), Ethical, Legal and Social Implications (ELSI) studies, public policy analysis, and responsible innovation—underscoring the need to examine AI’s ethical ramifications. While the initial wave of AI ethics focused on articulating principles and guidelines, recent scholarship increasingly emphasizes the practical implementation of ethical principles, regulatory oversight, and mitigating unforeseen negative consequences. Drawing from the concept of “ethics dumping” in research ethics, this paper argues that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments. Four key trends illustrating such ethics dumping are identified: (1) AI developers embedding ethics through coded value assumptions, (2) AI ethics guidelines promoting broad or unactionable principles disconnected from local contexts, (3) institutions implementing AI systems without evaluating ethical implications, and (4) decision-makers enacting ethical governance frameworks disconnected from practice. Mitigating AI ethics dumping requires empowering users, fostering stakeholder engagement in norm-setting, harmonizing ethical guidelines while allowing flexibility for local variation, and establishing clear accountability mechanisms across the AI ecosystem.

Towards trustworthy medical AI ecosystems – a proposal for supporting responsible innovation practices in AI-based medical innovation

AbstractIn this article, we explore questions about the culture of trustworthy artificial intelligence (AI) through the lens of ecosystems. We draw on the European Commission’s Guidelines for Trustworthy AI and its philosophical underpinnings. Based on the latter, the trustworthiness of an AI ecosystem can be conceived of as being grounded by both the so-called rational-choice and motivation-attributing accounts—i.e., trusting is rational because solution providers deliver expected services reliably, while trust also involves resigning control by attributing one’s motivation, and hence, goals, onto another entity. Our research question is: What aspects contribute to a responsible AI ecosystem that can promote justifiable trustworthiness in a healthcare environment? We argue that especially within devising governance and support aspects of a medical AI ecosystem, considering the so-called motivation-attributing account of trust provides fruitful pointers. There can and should be specific ways and governance structures supporting and nurturing trustworthiness beyond mere reliability. After compiling a list of preliminary requirements for this, we describe the emergence of one particular medical AI ecosystem and assess its compliance with and future ways of improving its functioning as a responsible AI ecosystem that promotes trustworthiness.

Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundation Models.

Foundation Models (FMs) are gaining increasing attention in the biomedical artificial intelligence (AI) ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders-especially those involved in or affected by these clinical and translational applications-are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem.

Organizational factors influencing the growth of Canada’s scientific and research potential in the field of artificial intelligence

A comprehensive analysis of the Pan-Canadian Artificial Intelligence Strategy and its implementation measures aimed at the growth of Canada’s scientific and research potential in the field of artificial intelligence forms the foundation of this study. Canada’s selection as the subject of study is attributed to its distinction as a pioneering country to adopt a strategy of this nature, and proving its status through drafting the Artificial Intelligence and Data Act, known as AIDA. The authors have discerned and deliberated on the main organizational factors that have positioned Canada as one of the leading nations in artificial intelligence in accordance with AI country rankings. This article presents the components of the Pan-Canadian Strategy, encompassing principal tasks and areas, including the practical introduction of novel technologies due to second-phase commercialization. It outlines the key focus areas of Canada’s public policy, including research, development and retention of skilled professionals, and the creation of essential infrastructure. The article also consolidates some significant societal outcomes realized during its implementation while identifying current trends. The foundation and activities of national institutions are underscored as pivotal in fostering scientific and research potential, with special emphasis on the initiative to establish a new institute dedicated to the safety of artificial intelligence under the strong influence of AI Safety Summit at Bletchley Park. The authors identify the key participants in the artificial intelligence ecosystem who have the most influence on implementing the Strategy. The conclusions drawn from the article aid in fostering a deeper comprehension of the role played by organizational and administrative processes in propelling advancements in the field of artificial intelligence. The favorable impact on societal development is highlighted, provided risks are mitigated. Given Ukraine’s historical association with high intellectual potential, the findings of this study can be instrumental in honing the national policy pertaining to artificial intelligence in Ukraine.

Building an AI ecosystem in a small nation: lessons from Singapore’s journey to the forefront of AI

Artificial intelligence (AI) is arguably the most transformative technology of our time. While all nations would like to mobilize their resources to play an active role in AI development and utilization, only a few nations, such as the United States and China, have the resources and capacity to do so. If so, how can smaller or less resourceful countries navigate the technological terrain to emerge at the forefront of AI development? This research presents an in-depth analysis of Singapore’s journey in constructing a robust AI ecosystem amidst the prevailing global dominance of the United States and China. By examining the case of Singapore, we argue that by designing policies that address risks associated with AI development and implementation, smaller countries can create a vibrant AI ecosystem that encourages experimentation and early adoption of the technology. In addition, through Singapore’s case, we demonstrate the active role the government can play, not only as a policymaker but also as a steward to guide the rest of the economy towards the application of AI.

The AI community building the future? A quantitative analysis of development activity on Hugging Face Hub

Open model developers have emerged as key actors in the political economy of artificial intelligence (AI), but we still have a limited understanding of collaborative practices in the open AI ecosystem. This paper responds to this gap with a three-part quantitative analysis of development activity on the Hugging Face (HF) Hub, a popular platform for building, sharing, and demonstrating models. First, various types of activity across 348,181 model, 65,761 dataset, and 156,642 space repositories exhibit right-skewed distributions. Activity is extremely imbalanced between repositories; for example, over 70% of models have 0 downloads, while 1% account for 99% of downloads. Furthermore, licenses matter: there are statistically significant differences in collaboration patterns in model repositories with permissive, restrictive, and no licenses. Second, we analyse a snapshot of the social network structure of collaboration in model repositories, finding that the community has a core-periphery structure, with a core of prolific developers and a majority of isolate developers (89%). Upon removing these isolates from the network, collaboration is characterised by high reciprocity regardless of developers’ network positions. Third, we examine model adoption through the lens of model usage in spaces, finding that a minority of models, developed by a handful of companies, are widely used on the HF Hub. Overall, the findings show that various types of activity across the HF Hub are characterised by Pareto distributions, congruent with open source software development patterns on platforms like GitHub. We conclude with recommendations for researchers, and practitioners to advance our understanding of open AI development.

Transdisciplinary Skills for AI Ecosystems: Using Future Visioning to Collaboratively Unpack Skills in UK Health and Emergency Response Scenarios

Transdisciplinary practices in artificial intelligence (AI) requires good methodological techniques to bring researchers together from diverse disciplines. Different fields and traditions each may have an impact on the greater AI ecosystem, however, to properly gauge this impact, and include diverse voices, research methodologies must evolve to adopt new methods and techniques. To understand the shifting demands for new skills, we conducted a transdisciplinary workshop, bringing together stakeholders from industry, government and academia, by engaging with scenarios in healthcare and emergency response, using future visioning to imagine new skill requirements across job roles. We found that future visioning was a useful methodology to help frame discussions in a manner conducive to eliciting qualitative evidence of different policy approaches. Beyond individual skills, we propose the Human-AI Synergy Matrix as a collaborative competency framework, which integrates technical, professional, and strategic skills with levels of engagement such as awareness, communication, synergy, governance, and transformation. The matrix aims to facilitate evaluation and understanding of skills and critical issues in human-AI collaboration within AI ecosystems, emphasizing the need to address broader challenges in an interconnected manner.

Understanding AI innovation contexts: a review and content analysis of artificial intelligence and entrepreneurial ecosystems research

PurposeAn emerging research stream focuses on the place-based ecosystems where artificial intelligence (AI) innovations emerge and develop. This literature builds on the contextual turn in management research and, specifically, work on entrepreneurial ecosystems. However, as a nascent research area, the literature on AI and entrepreneurial ecosystems is fragmented across academic and practitioner boundaries and unconnected disciplines because of disparate and ill-defined concepts. As a result, the literature is disorganized and its main insights are latent. The purpose of this paper is to synthesize research on AI ecosystems and identify the main insights.Design/methodology/approachWe first consolidate research on the “where” of AI innovation through a scoping review. To address the fragmentation in the literature and understand how entrepreneurial ecosystems are associated with AI innovation, we then use content analysis to explore the literature.FindingsWe identify the main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies. We clarify the relationships among AI technologies and ecosystem dimensions and uncover the latent themes and underlying structure of research on AI entrepreneurial ecosystems.Originality/valueWe increase conceptual precision by introducing and defining an umbrella concept—AI entrepreneurial ecosystem—and propose a research agenda to spur further insights. Our analysis contributes to research at the intersection of management, information systems, and entrepreneurship and creates actionable insights for practitioners influenced by the geographic agglomeration of AI innovation.

Empowering Mental Well-being: AI-guided Self-Recognition and Support

In an era marked by the convergence of technology and mental health, the integration of artificial intelligence (AI) holds promise for revolutionizing how individuals engage with their own well-being. This abstract explores the concept of AI-guided self-recognition and support as a transformative approach to empower mental wellness. Through sophisticated algorithms, AI can analyze diverse data streams, including digital behaviors and physiological markers, to detect subtle shifts indicative of mental health challenges. Leveraging this capability, AI becomes a proactive companion in individuals' journeys toward self-awareness. Furthermore, AI augments traditional support systems by offering personalized interventions tailored to individual needs and preferences. From empathetic chatbots providing emotional solace to curated resources and mindfulness exercises promoting resilience, the AI ecosystem becomes a versatile toolkit for mental health enhancement. Importantly, this paradigm shift emphasizes continuous learning and refinement, ensuring that interventions evolve in tandem with users' experiences and feedback. However, the implementation of AI-guided mental wellness initiatives necessitates careful consideration of privacy and ethical principles. Striking a balance between data utilization and user confidentiality remains paramount to fostering trust and engagement. Moreover, transparency in AI algorithms' functioning and accountability in decision-making processes are imperative to uphold ethical standards.

Ensuring AI Accountability: The Need for Expedited Oversight Frameworks Based On EU

The inevitability of artificial intelligence (AI) impacting every facet of our life is already evident in many ways. Nations are contending with the prospects and difficulties that AI offers. India has emerged as a frontrunner among South Asian nations in spearheading the promotion and regulation of AI. Nevertheless, it falls far behind nations like China or the United States. This article examines the AI ecosystem in India, including the risks and problems it encounters, as well as the ethical concerns it must address. Ultimately, it analyses the need for a comprehensive framework based on the EU regulation for a better control and regulation.

Implications of Identity in AI: Creators, Creations, and Consequences

The field of Artificial Intelligence (AI) is rapidly advancing, with significant potential to transform society. However, it faces a notable challenge: lack of diversity, a longstanding issue in STEM fields. In this context, this position paper examines the intersection of AI and identity as a pathway to understanding biases, inequalities, and ethical considerations in AI development and deployment. We present a multifaceted definition of AI identity, which encompasses its creators, applications, and their broader impacts. Understanding AI's identity involves analyzing the diverse individuals involved in AI's development, the technologies produced, and the social, ethical, and psychological implications. After exploring the AI identity ecosystem and its societal dynamics, We propose a framework that highlights the need for diversity in AI across three dimensions: Creators, Creations, and Consequences through the lens of identity. This paper presents a research framework for examining the implications and changes needed to foster a more inclusive and responsible AI ecosystem through the lens of identity.

Proceedings From the 2022 ACR-RSNA Workshop on Safety, Effectiveness, Reliability, and Transparency in AI

Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a 1-day workshop in November 2022, co-organized by the ACR and the RSNA, participants outlined experiences and problems with implementing AI in clinical practice, defined the needs of various stakeholders in the AI ecosystem, and elicited potential solutions and strategies related to the safety, effectiveness, reliability, and transparency of AI algorithms. Participants included radiologists from academic and community radiology practices, informatics leaders responsible for AI implementation, regulatory agency employees, and specialty society representatives. The major themes that emerged fell into two categories: (1) AI product development and (2) implementation of AI-based applications in clinical practice. In particular, participants highlighted key aspects of AI product development to include clear clinical task definitions; well-curated data from diverse geographic, economic, and health care settings; standards and mechanisms to monitor model reliability; and transparency regarding model performance, both in controlled and real-world settings. For implementation, participants emphasized the need for strong institutional governance; systematic evaluation, selection, and validation methods conducted by local teams; seamless integration into the clinical workflow; performance monitoring and support by local teams; performance monitoring by external entities; and alignment of incentives through credentialing and reimbursement. Participants predicted that clinical implementation of AI in radiology will continue to be limited until the safety, effectiveness, reliability, and transparency of such tools are more fully addressed.

An integrated AI framework for managing organizational risk and climate change concerns in B2B market

The United Nations identified sustainable development goals that focus on the three pillars of sustainability-Economy, Society, and Environment. The capability of Artificial intelligence (AI) for societal good and support to these three pillars is a matter of research importance. Drawing attention to literature on various AI frameworks for climate change and the dynamic capability theory, this study conducts a survey of managers working within their AI ecosystem of B2B firms. Confirmatory factor analysis and structural equation modeling are used to test the reliability, validity, and fitness of the research model. This model illustrates how big data-based AI system can influence business performance due to climate change. Research findings reveals paths involving “big data based artificial intelligence system”, “climate change competency process (prediction competency, mitigation competency, adaptation competency)”, “climate competent decision-making,” and “firm performance”, are statistically significant. As a part of research validation, three business cases are studied that focus on the decision-making capability of business under an uncertain environment. This study justifies that climate competent decision-making is a critical skill for businesses operating under uncertain environmental changes. This research brings out management perceptions generated due to climate change and discusses how they influence managers' behaviors that alter relationship dynamics and structure.

A New Era of Artificial Intelligence Begins – Where Will it Lead Us?

In this Editorial, we highlight the emerging dominance of AI + Big Data, and here are some excerpts : We have entered into the age of Artificial Intelligence (AI). Everything around us is becoming artificially intelligent: from business applications to healthcare, education to finance and governance to art, music and entertainment. The fact that AI has gripped public attention is evident from the steep rise in public engagement with artificial intelligence applications, explosive increase in news media coverage of AI, increasing volumes of social media posts and the mushrooming of a range of AI ecosystem initiatives. We at JBDAI (formerly JBDTP) hope to encourage and foster much high quality research, rigor and innovative thought leadership on big data and artificial intelligence in the years ahead, supporting human well-being, the sustainability of our natural resources and balanced societal progress – please contribute to JBDAI and be a part of this exciting intellectual adventure!

Conceptualizing ethical AI-enabled marketing: Current state and agenda for future research

This paper addresses the conceptual exploration of various issues that form an integral part of the ethical dimensions surrounding the use of artificial intelligence (AI) in the field of marketing. We critically review some of the main ethical challenges AI poses to humanity: privacy, data security, bias, transparency, and accountability. Therefore, we will discuss the delicate balance between exploiting the transformation potential of AI for personally designed marketing strategies and ethical imperatives to safeguard the rights of consumers to retain trust. Then, the present regulatory landscape responding to these ethical challenges will be assessed by building on effective regulation, like the GDPR, existing proposed legislative frameworks, and industry guidelines. To underscore the role of an ongoing dialogue between marketers, technologists, ethicists, and regulators for developing a responsible AI ecosystem in marketing. Further, strategic recommendations for implementing company-based ethical AI marketing practices. The further strong guidelines provide enhancement transparency to consumers and investment in research to remove biases from algorithms. It also underscores principal scopes of further research and development, focusing on the need for new-age solutions to guide the ethical quagmires that AI-led marketing throws up. We suggest that integration should be done proactively and collaboratively. Thus, if attention is paid more to ethics and multidisciplinary dialogue, only such hyperbolic promises can be realized by AI-driven marketing. There are promises through which AI-driven marketing will meet business goals and be huge at societies' values.

AI and the quest for diversity and inclusion: a systematic literature review

AbstractThe pervasive presence and wide-ranging variety of artificial intelligence (AI) systems underscore the necessity for inclusivity and diversity in their design and implementation, to effectively address critical issues of fairness, trust, bias, and transparency. However, diversity and inclusion (D&I) considerations are significantly neglected in AI systems design, development, and deployment. Ignoring D&I in AI systems can cause digital redlining, discrimination, and algorithmic oppression, leading to AI systems being perceived as untrustworthy and unfair. Therefore, we conducted a systematic literature review (SLR) to identify the challenges and their corresponding solutions (guidelines/ strategies/ approaches/ practices) about D&I in AI and about the applications of AI for D&I practices. Through a rigorous search and selection, 48 relevant academic papers published from 2017 to 2022 were identified. By applying open coding on the extracted data from the selected papers, we identified 55 unique challenges and 33 unique solutions in addressing D&I in AI. We also identified 24 unique challenges and 23 unique solutions for enhancing D&I practices by AI. The result of our analysis and synthesis of the selected studies contributes to a deeper understanding of diversity and inclusion issues and considerations in the design, development and deployment of the AI ecosystem. The findings would play an important role in enhancing awareness and attracting the attention of researchers and practitioners in their quest to embed D&I principles and practices in future AI systems. This study also identifies important gaps in the research literature that will inspire future direction for researchers.

Freezing out: Legacy media's shaping of AI as a cold controversy

Mainstream coverage of artificial intelligence often appears to emphasise the technologies’ benefit and economic potential over its growing downsides. How does a technology poised to be so disruptive become so uncritically embraced? Why is it, simply put, that artificial intelligence's representations in legacy media do not normally convey the controversialities otherwise found in research or policy debates? We introduce the concept of ‘freezing out’ to describe processes of translation that cool down debates over the merits of technology. Freezing out looks at the other side of controversy studies to study the production of uncontroversies or cold controversies rather than hot topics and debates. We use the coverage of artificial intelligence in Canadian national news outlets to analyse how controversiality becomes ‘frozen out’. Since Canadian academics won the prestigious ImageNet prize in 2012 introducing the modern turn toward machine learning approaches, Canada has promoted itself as a global leader. Using in-depth interviews with Francophone and Anglophone journalists as well as topic modelling on data collected from five major newspapers, we find that routine news making processes between journalists, experts, entrepreneurs, and governments build, maintain, and promote Canada's artificial intelligence ecosystem. Freezing out contributes to a broader interest in how heterogeneous actors traverse their domain of expertise across policy, media, and research circles to cool down artificial intelligence controversies.

DETERMINANTS OF THE FORMATION OF CHINA'S SMART ECONOMY

The development of China's smart economy has been heavily influenced by the government's strategic policies and investments in key technologies. One of the key technologies driving China's smart economy is the Internet of Things (IoT), which involves connecting machines, devices and sensors to the Internet for real-time data collection and analysis. This enables the optimisation of production processes and improved efficiency in industries such as manufacturing. The Chinese government has actively promoted the adoption of IoT technologies in manufacturing through policies such as the Made in China 2025 plan and the Internet Plus strategy. Another essential technology for China's smart economy is big data, which enables intelligent decision-making in various industries such as finance, healthcare and transportation. The government has encouraged the development of big data infrastructure, including data centres and cloud computing, to support the growth of the industry. Initiatives such as the National Big Data Comprehensive Pilot Zone have been established to promote the development of the big data industry. Artificial intelligence (AI) is also a key technology for China's smart economy, and the government has made significant investments in AI research and development. In 2017, China announced its plan to become a world leader in AI by 2030, with the aim of building a domestic AI industry worth $150 billion. The government has launched initiatives, such as the AI Development Plan, to build an AI ecosystem and promote the integration of AI with traditional industries. Robotics is another critical technology for China's smart economy, particularly in manufacturing. The government has encouraged the use of robots in manufacturing through tax incentives and subsidies, making China the world's largest market for industrial robots. The government aims to further develop the robotics industry to meet the needs of China's smart economy. In conclusion, China's smart economy is driven by strategic policies and investments in key technologies such as IoT, big data, AI and robotics. The government has launched various initiatives to support the development and integration of these technologies with traditional industries, with the aim of building a sustainable and innovative smart economy. These technologies play a critical role in China's industrial upgrading and transformation, and their continued development and adoption are expected to shape the future of China's economy and global competitiveness.

Artificial intelligence ecosystem for computational psychiatry: Ideas to practice

Artificial intelligence ecosystem for computational psychiatry: Ideas to practice

The US Intelligence Community, Global Security, and AI: From Secret Intelligence to Smart Spying

Abstract This article examines the ways in which the US intelligence community is leveraging the power of artificial intelligence (AI) for national security purposes. Drawing on declassified intelligence records, it contends that this community has been fascinated by AI for decades. This is important to acknowledge because this historical context has shaped contemporary projects and thinking within the community. It has given the United States a first-mover advantage, establishing precedents that other global actors need to comply with, negotiate or resist. The article advances three arguments. One, the community has long recognized that it needs to collaborate with the tech sector on AI. However, these relationships bring certain challenges since the sector is a curious compound of ideologies and interests. Two, while the community was initially attracted to the data processing advantages of AI to help human analysts to overcome “data smog,” today it has broadened its focus to consider how AI can improve all stages of the intelligence cycle. Three, while many voices feverishly herald the transformative potential of AI in the global security environment, we argue instead that US agencies will not be able to exploit the full potential of AI, and thus talk of an intelligence revolution is premature. This is because of national and international rules on data collection and retention but also because of cultural tensions within the global AI ecosystem. The discussion will appeal to scholars and practitioners interested in the impact of emerging technologies on national security processes and decision-making and, more broadly, global security.