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Distributed, decentralized, and democratized artificial intelligence
Abstract
The accelerating investment in artificial intelligence has vast implications for economic and cognitive development globally. However, AI is currently dominated by an oligopoly of centralized mega-corporations, who focus on the interests of their stakeholders. There is a now universal need for AI services by businesses who lack access to capital to develop their own AI services, and independent AI developers lack visibility and a source of revenue. This uneven playing field has a high potential to lead to inequitable circumstances with negative implications for humanity. Furthermore, the potential of AI is hindered by the lack of interoperability standards. The authors herein propose an alternative path for the development of AI: a distributed, decentralized, and democratized market for AIs run on distributed ledger technology. We describe the features and ethical advantages of such a system using SingularityNET, a watershed project being developed by Ben Goertzel and colleagues, as a case study. We argue that decentralizing AI opens the doors for a more equitable development of AI and AGI. It will also create the infrastructure for coordinated action between AIs that will significantly facilitate the evolution of AI into true AGI that is both highly capable and beneficial for humanity and beyond.
... Due to the high computing resources and big data demanded by ML and DL models, the training and development of AI ended in the single centralized data centers controlled and most often owned by a single company or entity [22]. Thus, this kind of AI is often called centralized AI (CEAI). ...
... As already mentioned, DEAI is a field that intersects the AI and decentralized systems fields, most often (but not strictly) utilizing DLTs and blockchain [22], [35]. While from a technical perspective, some concepts and design choices from DAI and Edge AI are the same or very similar in DEAI, the latter differs, especially in the following objectives: removing a single point of failure of the system, no centralized control of the system, utilizing open-source code and technologies, self-sovereignty of all actors in the system, enhanced privacy, and sharing of the resources (e.g., computing and AI models). ...
... Authors in [86] explored adding ontology to the P2P agent implementation based on the FIPA Nomadic Agents Working Group. Project SingularityNET is working on its own AI-DSL that includes hierarchical data and agent ontology, focusing on standardizing input and output structures of the agents, the time and costs required to perform agent's tasks, financial and payment information, and building high-level end-user language for defining all that information [22]. Effect Network is developing on the decentralized registry of AI services enhanced with rich ontology and defining technical schema for inputs and outputs [76]. ...
Artificial intelligence is transforming our lives, and technological progress and transfer from the academic and theoretical sphere to the real world are accelerating yearly. But during that progress and transition, several open problems and questions need to be addressed for the field to develop ethically, such as digital privacy, ownership, and control. These are some of the reasons why the currently most popular approaches of artificial intelligence, i.e., centralized AI (CEAI), are questionable, with other directions also being widely explored, such as decentralized artificial intelligence (DEAI), to solve some of the most reaching problems. This paper provides a systematic literature review (SLR) of existing work in the field of DEAI, presenting the findings of 71 identified studies. The paper's primary focus is identifying the building blocks of DEAI solutions and networks, tackling the DEAI analysis from a bottom-up approach. In the end, future directions of research and open problems are proposed.
... The second focus of the literature review concerns governance challenges, which can be categorized into three themes: public values challenges; data quality, processing, and outcome challenges; societal governance challenges. The extensive list of references for each theme is presented in Table 2. Public values challenges mainly emphasize that the lack of The most-cited challenges for AI systems are the lack of appropriate governance mechanisms to foster transparency and accountability (e.g., [50,65,70,73,83]). Governance challenges associated with ensuring equity are also highlighted [3,9,63,73,77,79]. ...
... The extensive list of references for each theme is presented in Table 2. Public values challenges mainly emphasize that the lack of The most-cited challenges for AI systems are the lack of appropriate governance mechanisms to foster transparency and accountability (e.g., [50,65,70,73,83]). Governance challenges associated with ensuring equity are also highlighted [3,9,63,73,77,79]. Additionally, privacy and safety concerns pose governance challenges to minimize the negative impact of AI (e.g., [36,79,84]). ...
... Explainable AI [59,61,65,68,74,86] Inclusion of more public values [50,60,66,71,72,74] Ethical AI [46,61,74,77] Distributed, decentralized, and democratized market [73] Ethical agent [46] Impacts on bureaucratic discretion [39] Privacy by design [35] Trustworthy AI [92,93] Data Quality and Processing sSolutions ...
While there has been growth in the literature exploring the governance of artificial intelligence (AI) and recognition of the critical importance of guiding public values, the literature lacks a systematic study focusing on public values as well as the governance challenges and solutions to advance these values. This article conducts a systematic literature review of the relationships between the public sector AI and public values to identify the impacts on public values and the governance challenges and solutions. It further explores the perspectives of U.S. government employees on AI governance and public values via a national survey. The results suggest the need for a broad inclusion of diverse public values, the salience of transparency regarding several governance challenges, and the importance of stakeholder participation and collaboration as governance solutions. This article also explores and reports the nuances in these results and their practical implications.
... Considering the absence of formal infrastructure for AI cooperation (Montes and Goertzel, 2019), there is an urgent need to propose a unified global agenda that sets the foundations and priorities of AI based on responsible innovation practices (Herrmann, 2023). Similarly, Watson (2010) argues that governments will operate as gatekeepers to regulate the AI revolution. ...
... Some implications to be addressed through regulation are hyperinformation anxiety (Watson, 2010), cyberterrorism (Canton, 2007), manipulation of information, censorship to promote diverse and controversial agendas related to foreign affairs, and uneven power distribution between the creators and users of AI (NIST, 2023). These issues can be resolved to some degree if AI becomes distributed, decentralized, and properly regulated based on democratic values and improves access to equal opportunities (Montes and Goertzel, 2019). Therefore, instead of infusing human values into AI, policymakers should focus on formal institutional structures that can promote a safe environment for the use of AI, along with a smart state. ...
This study regards AI as a socioecological issue and highlights the social identity determinants of the social perceptions of AI, which is the main dependent variable. We analyze Greece in 2022 as a case study. Our findings suggest that specific social identity variables concerning fundamental and social values, such as religion, views on new technologies, economic and political standings, and education, impact social perceptions of AI in a positive or negative manner. To enhance the analysis, we independently analyze the social identity framework shaping the relationship between jobs and AI, and the need to scientifically verify the results of AI technologies with an expert. Overall, social views of AI are shaped by the influence of a composite portfolio of fundamental and social values (which reflect both social stability and adaptability to change), economic and political standings, and demographics. Therefore, the social understanding of AI, along with other major issues, relates to its complex cultural dimensions. The findings go beyond the superficial understanding of the qualities AI should have since they underline the importance of existing institutional and value systems in the design of appropriate policies to combat the negative consequences, or capitalize on the benefits of such technologies.
... Research from these perspectives dealt with the role and influence of social processes and practices on the development of AI technologies as well as with principles that technologists can apply in order to develop ethical AI technologies. Six studies incorporated a perspective in which the technical is understood as imperative on the social (Asatiani et al., 2020;Montes and Goertzel, 2019) or a predominantly technical point of view in which social factors were taken into consideration as the context or setting of the research (Villegas-Ch et al., 2021;Dey and Lee, 2021;Gupta et al., 2021;Reddy et al., 2021). Research from these perspectives dealt with the specific development of AI-based solutions or with concrete frameworks governing the technical process of AI technology development, giving explicit technical recommendations on how to build an AI model to ensure a responsible, explainable and safe output. ...
... Next, we into consideration the type of AI agent that is referred to within the current research on AI technology development. Five publications drew on the perspective of AI technologies as agents that think rationally (e.g., Gupta et al., 2021;Henriksen and Bechmann, 2020), three conceptualized AI technologies as agents that act humanly (e.g., Dey and Lee, 2021;Reddy et al., 2021) and one defined AI technologies as agents that think humanly (Montes and Goertzel, 2019). None of the studies included referred to AI technologies as agents that act rationally. ...
Research on artificial intelligence technology has not only increased rapidly over recent years, it is also no longer limited to the technical disciplines from which it originates. AI technologies are also at the center of social and sociotechnical studies including those conducted in information systems. Through a scoping review we explore how the research on different kinds of AI technologies has progressed over the past decade. Particularly, we explore whether the research on AI technologies has been informed by and informs our sociotechnical understanding of phenomena related to their design, development and implementation. For this purpose, we develop an analytical framework that differentiates sociotechnical perspectives, categorizes AI technologies into different kinds, and distinguishes research on AI technology design, development, and implementation. The findings from our review point to several directions for future research.
... What we call Web3 will center around a decentralized ecosystem of technology products based on blockchain networks that are interoperable and free of traditionally trusted validators such as businesses, institutions, and government agencies. The Web3 architecture is shown in Fig. 2. It adopts AI technology [1], [2], [3], [4], [5], machine learning [6], and blockchain [7], [8], [9] to provide users with smart applications. This enables the intelligent creation and distribution of highly tailored content to every internet user. ...
... These technologies work together to ensure that the decentralization property can be implemented in new applications. Decentralized Finance DeFi [24] Regenerative Finance ReFi [25] Decentralized Science DeSci [26] Decentralized Social Blockchain DeSo [27] Non-Fungible Token NFT [28] Fungible Token FT [7] Blockchain Technology BT [29] Internet of Thing IoT [1] Decentralized Artificial Intelligence DAI [30] Secure Multi-Party Computation SMPC [31] Trustworthy Federated Learning TFL [32] Trusted Execution Environment TEE ...
Since the first appearance of the World Wide Web, people more rely on the Web for their cyber social activities. The second phase of World Wide Web, named Web 2.0, has been extensively attracting worldwide people that participate in building and enjoying the virtual world. Nowadays, the next internet revolution: Web3 is going to open new opportunities for traditional social models. The decentralization property of Web3 is capable of breaking the monopoly of the internet companies. Moreover, Web3 will lead a paradigm shift from the Web as a publishing medium to a medium of interaction and participation. This change will deeply transform the relations among users and platforms, forces and relations of production, and the global economy. Therefore, it is necessary that we technically, practically, and more broadly take an overview of Web3. In this paper, we present a comprehensive survey of Web3, with a focus on current technologies, challenges, opportunities, and outlook. This article first introduces several major technologies of Web3. Then, we illustrate the type of Web3 applications in detail. Blockchain and smart contracts ensure that decentralized organizations will be less trusted and more truthful than that centralized organizations. Decentralized finance will be global, and open with financial inclusiveness for unbanked people. This paper also discusses the relationship between the Metaverse and Web3, as well as the differences and similarities between Web 3.0 and Web3. Inspired by the Maslow's hierarchy of needs theory, we further conduct a novel hierarchy of needs theory within Web3. Finally, several worthwhile future research directions of Web3 are discussed.
... There is a dearth of research on how consumers respond to machine-controlled products and services, especially those utilizing AI, despite the fact that the standardization of social services is the subject of much current research (Montes & Goertzel 2019). Due to the fact that self-management innovations are frequently at the center of the design of these services, the effectiveness of operations and service quality of services that use artificial intelligence and social services may differ dramatically. ...
Management is an art of getting things done through and with the people in formally organized groups. It is an art of creating an environment in which people can perform and individuals and can co-operate towards attainment of group goals. Management Study HQ describes Management as a set of principles relating to the functions of planning, directing and controlling, and the application of these principles in harnessing physical, financial, human and informational resources efficiently and effectively to achieve organizational goals. A good management is the backbone of all successful organizations. And to assist business and non-business organizations in their quest for excellence, growth and contribution to the economy and society, Management Book Series covers research knowledge that exists in the world in various management sectors of business through peer review chapters. This book series helps company leaders and key decision-makers to have a clear, impartial, and data-driven perspective of how factors will impact the economy moving forward and to know what they should be doing in response.
... The fundamental means to exploit loopholes has been human ingenuity [10]. Now, there are concerns that so-called bad actors could find ways around rules about what should be done by AI [11]. However, motivation to exploit loopholes can arise from common pressures experienced by many people such as organizational pressure [12] and/or time pressure [13]. ...
Loopholes involve misalignments between rules about what should be done and what is actually done in practice. The focus of this paper is loopholes in interactions between human organizations’ implementations of task-specific artificial intelligence and individual people. The importance of identifying and addressing loopholes is recognized in safety science and in applications of AI. Here, an examination is provided of loophole sources in interactions between human organizations and individual people. Then, it is explained how the introduction of task-specific AI applications can introduce new sources of loopholes. Next, an analytical framework, which is well-established in safety science, is applied to analyses of loopholes in interactions between human organizations, artificial intelligence, and individual people. The example used in the analysis is human–artificial intelligence systems in gig economy delivery driving work.
... Second, future research might consider companies outside the tech domain (e.g., smaller companies) to identify further strategies about the interplay of AI and sustainability. In this context, research might discuss how AI x Sustainability strategies can be implemented at smaller companies and in public services that cannot rely on the resources of the Big Tech companies, which almost hold an oligopoly on AI resources (Montes & Goertzel, 2019), potentially threatening sustainability initiatives. Future research might explore how AI resources can be more equally shared through policies, heading to open access to AI for sustainability. ...
Sustainability is at the top of the agenda of most tech companies. Specifically, tech companies increasingly utilize artificial intelligence (AI) to meet their sustainability goals. However, little is known about how tech companies can leverage AI to accelerate sustainability by formulating and implementing appropriate strategies. To better understand the intertwined nature of AI and sustainability from a strategy perspective, this research conceptually develops a novel AI x Sustainability framework by drawing from the nested sustainability model and integrating insights from different literature streams. It then applies this framework to six leading Big Tech companies (i.e., Amazon, Google, IBM, Meta, Microsoft, and SAP) by conducting a comprehensive document analysis of 69 documents describing 244 individual AI x Sustainability initiatives to reveal whether and how these companies appear to follow specific AI x Sustainability strategies. Lastly, an exploratory survey with potential tech com-panies' clients (N = 192) sheds light on how clients perceive tech companies' communicated strategic positioning based on the framework. The research provides new theoretical insights, serves as a blueprint for other tech companies, including implications for their AI x Sustainability positioning, and offers a variety of future research directions.
... Many corporations are investing vast amounts of money in MA. However, mega-companies are at the forefront of the use of MA with the interest of their stakeholders only (Montes & Goertzel, 2019). Due to the conjunction of different computational, statistical, machine learning, technological, analytical, and research trends, we are currently standing in a time of ubiquitous MA (Liang et al., 2022;Paschen et al., 2019). ...
This study aimed to examine the complex forces that influence the adoption of marketing analytics in the ready-made garments (RMG) industry in an emerging country context. The forces for change and those that impede the adoption of marketing analytics in the industry were explored. We used Lewis's Force Field Analysis framework to inform the research. Semi-structured interviews with managers , technology experts, and government officials were conducted using face-to-face and virtual meetings. The results reveal that RMG buyers' demand, competitors, lack of employee performance, and climate change issues are central forces pushing for implementing marketing analytics in the industry. However, the lack of knowledge, interest, and technology-skilled people, high cost, employee resistance, privacy issues, high employee turnover, and government policies are significant impediments to marketing analytics adoption in the RMG industry. The theoretical, organisa-tional, policy, and professional implications are then discussed. Theoretically, this study contributes by creating a conceptual framework using Lewin's Force Field Analysis. In practical terms, this study suggests that marketing analytics in the Industry 4.0 era offers significant opportunities for businesses and policymakers to increase their flexibility, competitiveness and responsiveness.
... users' perceptions of the technology, encompassing perceived usefulness, ease of use, trust, and risk, shape their attitudes (Davis, 1989;Vimalkumar et al., 2021). Thirdly, external factors such as marketplace dynamics, privacy concerns, and ethical considerations can either facilitate or hinder AI adoption (Klaus & Zaichkowsky, 2022;Montes & Goertzel, 2019;Sharma et al., 2022). Finally, individual characteristics like demographics, personality traits, innovativeness, and prior experience predict AI adoption (Liu & Tao, 2022;Venkatesh et al., 2003). ...
In the rapidly evolving landscape of technology, the emergence of Chat Generative Pre-trained Transformer (ChatGPT) marks a pivotal milestone in the realm of Artificial Intelligence (AI). However, little research has reported the predictors of people's intentions to use ChatGPT. This pioneering study empirically examines user adoption through the lens of the Technology Acceptance Model (TAM) using a convenience sampling method. The study surveyed 784 ChatGPT users in China, of whom 58.93% were males. The results have revealed several key findings: (1) perceived usefulness, perceived ease of use, behavioral intention, and use behavior were positively correlated with each other; (2) behavioral intention acted as a mediating factor in the relationship between perceived usefulness and use behavior, as well as the relationship between perceived ease of use and use behavior; (3) perceived usefulness and behavioral intention played a chain-mediated role between perceived ease of use and use behavior; (4) the relationship between behavioral intention and use behavior exhibited greater strength among females compared to males; (5) the association between behavioral intention and use behavior was found to be stronger among urban users in comparison to their rural counterparts; (6) the connections between perceived ease of use and perceived usefulness, perceived ease of use and behavioral intention, and behavioral intention and use behavior were observed to be stronger among individuals with higher educational backgrounds relative to those with lower educational backgrounds. These findings provide crucial nuanced insights to advance the practical application of ChatGPT, emphasizing the need for enhanced usability and ease of use. However, this study exclusively captured usage behaviors within the Chinese user base. Future investigations could encompass diverse demographics across multiple countries, enabling cross-cultural comparisons. KEYWORDS Technology acceptance model (TAM); ChatGPT; perceived usefulness; perceived ease of use; behavioral intention; use behavior
... One significant way AI improves accessibility is by democratizing financial services. Historically, accessing financial services has been hindered by factors such as geographical constraints, income disparities, and educational limitations (Montes, and Goertzel, 2019;Kaggwa, et al., 2024). However, AI-driven digital platforms and mobile applications are changing this narrative by providing convenient and affordable access to banking services for individuals worldwide. ...
The integration of Artificial Intelligence (AI) within financial markets has become increasingly pivotal, particularly in emerging economies where efficiency and accessibility remain significant challenges. This abstract explores how AI technologies are reshaping financial market development, with a specific focus on enhancing efficiency and accessibility in emerging economies. AI facilitates automation of routine tasks, predictive modeling, and robust risk management, thereby streamlining operations and reducing costs. Moreover, AI-driven solutions democratize financial services, offering personalized advice and expanding financial inclusion initiatives. Despite its transformative potential, challenges such as data privacy concerns, regulatory barriers, and technological infrastructure limitations persist. By examining successful AI implementations and case studies, this review underscores the importance of collaborative efforts between public and private sectors to overcome these challenges. Looking ahead, the abstract emphasizes the need for policymakers to develop conducive regulatory frameworks and encourages stakeholders to embrace AI technologies for sustainable financial market development in emerging economies. Keywords: AI, Financial Market Development, Efficiency, Accessibility, Emerging Economies, Automation.
... Despite humans, machines in the crowd could be empowered by modeling as agents [29], and its high-level potential benefits include: 1) Collaborative and effective decision system. Democratize access to IndAI models and their benefits by enabling coordinated IndAI and multi-agent interactions [8], decentralizing data ownership, sharing knowledge, and access to huge datasets; 2) Self-organizing cooperation. ...
With the continuous development of human-centric, resilient, and sustainable manufacturing towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportunities for additional functionalities, new features, and tendencies in the industrial landscape. On the other hand, the technology-driven Industry 4.0 paradigm is still in full swing. However, there exist many unreasonable designs, configurations, and implementations of Industrial Artificial Intelligence (IndAI) in practice before achieving either Industry 4.0 or Industry 5.0 vision, and a significant gap between the individualized requirement and actual implementation result still exists. To provide insights for designing appropriate models and algorithms in the upgrading process of the industry, this perspective article classifies IndAI by rating the intelligence levels and presents four principles of implementing IndAI. Three significant opportunities of IndAI, namely, collaborative intelligence, self-learning intelligence, and crowd intelligence, towards Industry 5.0 vision are identified to promote the transition from a technology-driven initiative in Industry 4.0 to the coexistence and interplay of Industry 4.0 and a value-oriented proposition in Industry 5.0. Then, pathways for implementing IndAI towards Industry 5.0 together with key empowering techniques are discussed. Social barriers, technology challenges, and future research directions of IndAI are concluded, respectively. We believe that our effort can lay a foundation for unlocking the power of IndAI in futuristic Industry 5.0 research and engineering practice.
... For example, healthcare and the military call for operators or professionals using these technologies to be ultimately responsible for actions of these technologies (Luxton, 2014a;O'Sullivan et al., 2019;RAND Corporation, 2020;T oth et al., 2022). Research also highlights the need for macro-level regulation of AI development and use to ensure the creation of institutional frameworks for users and developers to operate within (Montes & Goertzel, 2019;Raab, 2020;Weber, 2020). The importance of professionalism consideration in developing and deploying AI to ensure accountability of the actions performed by the technology has been suggested by a number of studies (e.g., Carter, 2020;Gillies & Smith, 2022;Howard & Borenstein, 2018;Luxton, 2014a). ...
... Siau and Wang (2020) reports the risk of the lack of accessibility for the elderly. Montes and Goertzel (2019) notice that the field of AI is presently controlled by a small group of centralized mega-corporations, functioning as an oligopoly, and their primary focus is directed towards the concerns of their stakeholders. They propose a decentralized and democratized AI market using distributed ledger technology that has as its aim, equitable AI and AGI (Artificial General Intelligence) development. ...
The abundance of literature on ethical concerns regarding artificial intelligence (AI) highlights the need to systematize, integrate, and categorize existing efforts through a systematic literature review. The article aims to investigate prevalent concerns, proposed solutions, and prominent ethical approaches within the field. Considering 309 articles from the beginning of the publications in this field up until December 2021, this systematic literature review clarifies what the ethical concerns regarding AI are, and it charts them into two groups: (i) ethical concerns that arise from the design of AI and (ii) ethical concerns that arise from human–AI interactions. The analysis of the obtained sample highlights the most recurrent ethical concerns. Finally, it exposes the main proposals of the literature to handle the ethical concerns according to the main ethical approaches. It interprets the findings to lay the foundations for future research on the ethics of AI.
... On the positive side, a collaborative strategy can help improve the organizational antecedents (van Noordt & Misuraca, 2020) in which AI is to be implemented, such as increasing the amount of available resources, know-how, skills, or infrastructures. It can also help to enlarge the capacities of the technology allowing its interoperability (Desouza, 2018), potential decentralization (Montes & Goertzel, 2019), or ensuring that the regulations allow the full development of the projects in which different stakeholders are involved (Cath, 2018). Engaging and activating several partners enables benefiting from their potential and exponentially increasing the reach of the innovation. ...
This chapter presents an overview and analysis of artificial intelligence-driven solutions created and implemented by or with the support of Poland’s central public administration (PA). After discussing governance of AI-related issues, we analyze a set of examples of AI innovation to map the actors and their relations within the ecosystem, describe the field where innovation in AI for PA occurs, and highlight the potentialities and limitations of the current scenario. Moreover, we examine the dynamics among stakeholders in AI-driven innovation building for PA. We conducted an exploratory study of Poland’s situation, assuming the capacity of this methodological strategy for the early examination of the topic and opening new avenues for further research. Thus, we followed the replication logic of Yin's case study. The interviews were approached inductively, to unveil the underlying elements that connect the practical development of AI projects with the institutional framework and the theoretical background.
... On the positive side, a collaborative strategy can help improve the organizational antecedents (van Noordt & Misuraca, 2020) in which AI is to be implemented, such as increasing the amount of available resources, know-how, skills, or infrastructures. It can also help to enlarge the capacities of the technology allowing its interoperability (Desouza, 2018), potential decentralization (Montes & Goertzel, 2019), or ensuring that the regulations allow the full development of the projects in which different stakeholders are involved (Cath, 2018). Engaging and activating several partners enables benefiting from their potential and exponentially increasing the reach of the innovation. ...
This chapter presents an overview and analysis of artificial intelligence (AI)-driven solutions created and implemented by or with the support of the central public administration (PA) in Poland. We focus on GovTech Polska, a special unit within the Chancellery of the Prime Minister that acts as a hub for innovation in central public administration and in designing AI-based tools for other PA units. The development of solutions enabled by emerging technologies, such as AI, blockchain, or the Internet of Things (IoT), and the automation of services are drivers of innovative and sustainable change in organisations. Implementation of such technologies increases organisational flexibility, resilience, and fosters the production of social capital. However, in contrast to business, the PA's implementation of emerging technologies is generally slow and cumbersome. Thus, many civic-centric services lag behind in digitisation and digital transformation.
... Compared to the previous technological revolutions which "were mainly due to advances in general purpose technologies, namely steam power, electricity, computerization, the current fourth industrial revolution, going to the fifth revolution, involves a shift of paradigms [3] in all disciplines, economies and industries since it challenges what it means to be human and consequently raises important political and ethical issues" [2]. In this direction, algorithms of artificial intelligence (AI) "are trained" to consider big data from the past and embody stereotypes and values of their designers and coders [1,4]. Hence, innovative technological solutions, specifically algorithms should give the opportunity to face and overcome stereotypes and support human biases especially concerning the most vulnerable individuals, like people with disability (PWD) [1]. ...
This paper aims to investigate the role and function of digital and smart technologies, including AI applications, within organizations in making them much more inclusive for people with disability (PWD) at the workplace starting from the recruitment process. Specifically, this conceptual study provides an indepth analysis of smart recruitment process for creating work environments much more inclusive and sustainable for PWD. In the last three decades, also because of the COVID-19 pandemic, the digital transformation, largely adopting digital and smart technologies, has significantly, both positively and negatively, affected any field and industry in the private and professional life. Indeed, our ways of working and quality of life have been improved by digital and smart technologies which are able to overcome geographical, physical, and social barriers. Likewise, some negative effects are related to this phenomenon, such as digital divide especially for some categories of people, like those with disabilities or special needs. This conceptual paper provides a systematic literature review; indeed, the phenomenon of smart recruitment is investigated providing an overview regarding its insights, challenges, and future developments. A bibliometric analysis is conducted using WoS and Scopus databases with manual selection through the VOSviewer software.
... For example, healthcare and the military call for operators or professionals using these technologies to be ultimately responsible for actions of these technologies (Luxton, 2014a;O'Sullivan et al., 2019;RAND Corporation, 2020;T oth et al., 2022). Research also highlights the need for macro-level regulation of AI development and use to ensure the creation of institutional frameworks for users and developers to operate within (Montes & Goertzel, 2019;Raab, 2020;Weber, 2020). The importance of professionalism consideration in developing and deploying AI to ensure accountability of the actions performed by the technology has been suggested by a number of studies (e.g., Carter, 2020;Gillies & Smith, 2022;Howard & Borenstein, 2018;Luxton, 2014a). ...
Ethical conduct of artificial intelligence (AI) is undoubtedly becoming an ever more pressing issue considering the inevitable integration of these technologies into our lives. The literature so far discussed the responsibility domains of AI; this study asks the question of how to instil ethicality into AI technologies. Through a three‐step review of the AI ethics literature, we find that (i) the literature is weak in identifying solutions in ensuring ethical conduct of AI, (ii) the role of professional conduct is underexplored, and (iii) based on the values extracted from studies about AI ethical breaches, we thus propose a conceptual framework that offers professionalism as a solution in ensuring ethical AI. The framework stipulates fairness, nonmaleficence, responsibility, freedom, and trust as values necessary for developers and operators, as well as transparency, privacy, fairness, trust, solidarity, and sustainability as organizational values to ensure sustainability in ethical development and operation of AI.
... Technology-driven innovation refers to shifts in the combined working of new and old technological operations and their acceptance and adaptation in a market. 32 In the post-digital era, 33 the convergence of Industry 4.0 technologies will give rise to new applications. Industry 4.0 is expected to undergo a revolution due to quantum technology, which relies on probabilities instead of binary bits and promises significant innovation. ...
Background: Cancer is a leading cause of death worldwide. Machine learning (ML) and quantum computers (QCs) have recently advanced significantly. Numerous studies have examined the application of quantum machine learning (QML) in healthcare and validated its superiority over classical ML algorithms. Objectives: This review investigates and reports the oncological applications of QML. Methods: In March 2023, an electronic investigation of PubMed, Scopus, Web of Science, IEEE, and Cochrane databases was performed. The articles were screened based on titles and abstracts, and their full texts were examined. Results: Initially, a total of 207 articles were retrieved. Thereafter, 9 articles were included in the study, most of which were published from 2020 onwards. The results indicated the implementation of various QML techniques in different aspects of oncology, such as reducing mammography image noise, edge detection of breast cancer, clinical decision support in radiotherapy treatment, and cancer classification. Conclusion: These studies revealed that integrating quantum science with ML can significantly improve patient care and clinical outcomes. Future studies should explore the integration of QC and ML and the development of novel algorithms to enhance cancer prognosis, diagnosis, and treatment planning.
... As reported by Montes and Goertzel[129], AI research and development is today dominated by a few mega-corporations.A. Bennich ...
This paper addresses the ‘digital imperative’ and how organisations are pressured to digitalise. In comparison to previous studies that often emphasise market pressures to digitalise, this paper focuses on institutional pressures exerted on organisations that lack competitive pressures. By drawing upon institutional theory, which considers the social embeddedness of organisations, it acknowledges that social expectations and approval can shape the way organisations respond to technological change. Based on a study of digitalisation within the water sector, where the perceived benefits of digital technologies are advocated under the label of ‘digital water’, it shows that there are isomorphic processes in place through which the idea of digital water is legitimised. Hence, water utilities are under institutional pressures to digitalise which, in turn, can influence how they respond to digitalisation.
... Concerns about the democratic legitimacy of AI-based decisions have been voiced in the context of infrastructures related to speech, deliberation, and media (see for example Helberger, 2019;Nemitz, 2018). Furthermore, increasing dependency on corporate-controlled opaque, and unaccountable AI-based services and products can increase power asymmetries and weaken the position of democratic states and citizens (Montes & Goertzel, 2019;Sadowski & Levenda, 2020;Taylor, 2021). In light of these concerns, it is clear that 'democratizing AI' requires more than users getting access to technology. ...
Artificial Intelligence (AI) technologies offer new ways of conducting decision-making tasks that influence the daily lives of citizens, such as coordinating traffic, energy distributions, and crowd flows. They can sort, rank, and prioritize the distribution of fines or public funds and resources. Many of the changes that AI technologies promise to bring to such tasks pertain to decisions that are collectively binding. When these technologies become part of critical infrastructures, such as energy networks, citizens are affected by these decisions whether they like it or not, and they usually do not have much say in them. The democratic challenge for those working on AI technologies with collectively binding effects is both to develop and deploy technologies in such a way that the democratic legitimacy of the relevant decisions is safeguarded. In this paper, we develop a conceptual framework to help policymakers, project managers, innovators, and technologists to assess and develop approaches to democratize AI. This framework embraces a broad sociotechnical perspective that highlights the interactions between technology and the complexities and contingencies of the context in which these technologies are embedded. We start from the problem-based and practice-oriented approach to democracy theory as developed by political theorist Mark Warren. We build on this approach to describe practices that can enhance or challenge democracy in political systems and extend it to integrate a sociotechnical perspective and make the role of technology explicit. We then examine how AI technologies can play a role in these practices to improve or inhibit the democratic nature of political systems. We focus in particular on AI-supported political systems in the energy domain.
... AutoML is an attempt to solve the above issues, not constrained by domain, to minimize human intervention in data preprocessing, feature selection, and model development and deployment. While traditional artificial intelligence (AI) requires expertise in model development and deployment, autoML aims to make the technology more decentralized and accessible (3). AutoML is a serious attempt at algorithmic automation with support for explainability so that cross-domain experts without specialized knowledge of AI can benefit from the advancements. ...
Background and objective
Automated machine learning or autoML has been widely deployed in various industries. However, their adoption in healthcare, especially in clinical settings is constrained due to a lack of clear understanding and explainability. The aim of this study is to utilize autoML for the prediction of functional outcomes in patients who underwent mechanical thrombectomy and compare it with traditional ML models with a focus on the explainability of the trained models.
Methods
A total of 156 patients of acute ischemic stroke with Large Vessel Occlusion (LVO) who underwent mechanical thrombectomy within 24 h of stroke onset were included in the study. A total of 34 treatment variables including clinical, demographic, imaging, and procedure-related data were extracted. Various conventional machine learning models such as decision tree classifier, logistic regression, random forest, kNN, and SVM as well as various autoML models such as AutoGluon, MLJAR, Auto-Sklearn, TPOT, and H2O were used to predict the modified Rankin score (mRS) at the time of patient discharge and 3 months follow-up. The sensitivity, specificity, accuracy, and AUC for traditional ML and autoML models were compared.
Results
The autoML models outperformed the traditional ML models. For the prediction of mRS at discharge, the highest testing accuracy obtained by traditional ML models for the decision tree classifier was 74.11%, whereas for autoML which was obtained through AutoGluon, it showed an accuracy of 88.23%. Similarly, for mRS at 3 months, the highest testing accuracy of traditional ML was that of the SVM classifier at 76.5%, whereas that of autoML was 85.18% obtained through MLJAR. The 24-h ASPECTS score was the most important predictor for mRS at discharge whereas for prediction of mRS at 3 months, the most important factor was mRS at discharge.
Conclusion
Automated machine learning models based on multiple treatment variables can predict the functional outcome in patients more accurately than traditional ML models. The ease of clinical coding and deployment can assist clinicians in the critical decision-making process. We have developed a demo application which can be accessed at https://mrs-score-calculator.onrender.com/.
... Moreover, the AI will perform any situation indistinguishable from how a human would perform the same task. There are predictions that we won't see this level of proficiency until well beyond the year 2050 (Montes & Goertzel, 2019). ...
Companies across industries are embracing artificial intelligence (AI) to create more frictionless and memorable customer service experiences. However, successfully implementing AI customer service strategies requires companies to realize the value of strategically deploying AI and human agents to optimize customer service. To realize the full potential of AI, companies ideally ought to foster a sustainable AI–human symbiosis—a technology-oriented eco-system where AI and humans assist, complement, and learn from each other to offer a seamless service experience for customers. After outlining the significance of an AI customer service strategy, Chap. 4 highlights how companies could capitalize on AI as a key element in successfully implementing customer service strategies. Specifically, we outline the conceptualization of AI, followed by a brief overview of AI customer service strategies. Next, we highlight key steps to successfully implement an AI customer service strategy—including triage, prospect, pre-consumption, and post-consumption management—and showcase relevant real-world examples.
Keywords: Artificial Intelligence (AI), AI customer service strategy, AI–human symbiosis, Triage.
... Nonetheless, the paper shows the challenges of isolating and measuring the impact of algorithmic trading. Some literature has also started investigating AI's potentials to democratize investing (Montes & Goertzel, 2019). For instance, "The AI Book" optimistically discusses AI lowering costs and barriers for retail investors to access sophisticated tools and alternative datasets previously exclusive to institutional investors (Bartoletti, 2020). ...
Artificial intelligence’s (AI) ingress into the investment landscape warrants an urgent rethink of traditional economic theories and governance frameworks. AI’s superior information processing prowess, ability to analyze alternative datasets, and speed of execution defy the utility of traditional theories like the efficient market hypothesis. This paper focuses specifically on AI’s impacts in retail investing, where opacity of algorithms and information asymmetry disproportionately disadvantage non-institutional investors. Firstly, I formulate an algorithm-conscious spectral efficiency model spanning beyond the rigid efficient–anomalous binary. Secondly, I recommend an ethical oversight blueprint fusing agency theory with stewardship principles to align investor-algorithm incentives via a contractual solution. The proposal encompasses regulatory sandboxes for controlled AI testing, retail investor representation in external audits of algorithms, plain language disclosure standards, and specialized financial literacy campaigns. In addition, I sketch five potential insurance products to mitigate AI-induced risks in investment. I conclude by underscoring early ethical interventions in governing financial AI to avoid entrenching negative externalities. Together, these institutional solutions offer updated theories and pragmatic frameworks for firms and regulators to integrate AI in investment pro-socially.
... In addition, the inclusiveness coverage provides the research opportunity in digital governance and ethics-oriented theories and issues (Wright and Schultz, 2018), which, as inferred from the cultural element of Alibaba Group (2019), should span across and give the optimal advantages to small businesses. Thus, conceptually, Figure 2 can be a framework for realizing distributed, decentralized and democratized artificial intelligence (Montes and Goertzel, 2018). Clearly, what is demonstrated in the Alibaba Group and Hangzhou AI town is the extension of the external broader layer at socio-cultural level. ...
T his article presents how an instrumental case research-oriented approach can be used in a teacher field trip. The purpose is to help the teachers on a field trip to identify and explore research topics and propositions that have currency in today’s digital and AI economy. The authors of this article were among the 55-member team in the educational field trip to China, in 2018. The trip was motivated by the strategic significance of China as an emerging leader in the world for many contemporary phenomena in new business model and strategies, i.e. new retails (新零售, 人工智能).
The visits to Hangzhou’s Artificial Intelligence (AI) town (中国-杭州人工智能小镇) and Alibaba group headquarter led to some important propositions highlighted in this article. Clearly, there are active progress and achievements of a digitally enabled business ecosystem that emerged as a new and active trend of business model development in China. Among the identified propositions are a model describing the architecture of the business ecosystem, consisting of the production ecosystems, the consumption ecosystems, the broader socio-cultural and public domain level, and the digital envelope and information-in-use, which shares also concepts and knowledge already available in Complex Adaptive Systems (CAS).
In addition, the visit to the Banyan Tree Hotel group in Hangzhou helps the researchers to better understand the role of brand storytelling. Banyan Tree brand started with a story of the founders experiencing their honeymoon in surrounding Banyan trees in Phuket. Since then, everything they do, i.e. the quality assurance system, the infrastructure, new product development, and philanthropy activities, are all related to brand storying telling that shares the same roots of themes, and brand personality. As such, Banyan Tree is an exemplar case, which has significant instrumental utility. A narrative discussion is provided in the article.
... Although existing ML and DL services use cloud computing and servers to run, and therefore require an Internet connection, there is a trend towards decentralization [15], [16]. [17] argues that decentralizing AI opens the door for more equitable development. Instead of connecting to data centre-based services, queried through mobile communications, AI capabilities will reside on the device itself. ...
Not all frameworks used in machine learning and deep learning integrate with Android, which requires some prerequisites. The primary objective of this paper is to present the results of the analysis and a comparison of deep learning development frameworks, which can be adapted into fully decentralized Android apps from a cloud server. As a work methodology, we develop and/or modify the test applications that these frameworks offer us a priori in such a way that it allows an equitable comparison of the analysed characteristics of interest. These parameters are related to attributes that a user would consider, such as (1) percentage of success; (2) battery consumption; and (3) power consumption of the processor. After analysing numerical results, the proposed framework that best behaves in relation to the analysed characteristics for the development of an Android application is TensorFlow, which obtained the best score against Caffe2 and Snapdragon NPE in the percentage of correct answers, battery consumption, and device CPU power consumption. Data consumption was not considered because we focus on decentralized cloud storage applications in this study.
... In the current era of information, artificial intelligence and blockchain technologies are increasingly being applied across various domains, with data security and privacy protection emerging as pressing issues to be addressed. Innovative cases, such as Anthropic's Constitutional AI [13], SingularityNET's Decentralized AI [14], and ChainLink's Decentralized Oracle [15] are delving into the deep integration of artificial intelligence and blockchain technologies to achieve more efficient, secure, and transparent data processing. ...
With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This paper initially presents an overview of AI and blockchain, summarizing their combination along with derived privacy protection technologies. It then explores specific application scenarios in data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity methods. Moreover, the paper evaluates five critical aspects of AI-blockchain-integration privacy protection systems, including authorization management, access control, data protection, network security, and scalability. Furthermore, it analyzes the deficiencies and their actual cause, offering corresponding suggestions. This research also classifies and summarizes privacy protection techniques based on AI-blockchain application scenarios and technical schemes. In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy.
... Artificial Intelligence (AI) is viewed as the most promising architectural innovation in the 21 st Century and will impact all aspects of society and businesses in far greater magnitude than any previous digital revolution (Kulkov, 2021;Makridakis, 2017;Montes & Goertzel, 2019;Olan, Liu, Suklan, Jayawickrama, & Arakpogun, 2021). Some business analysts suggest that the market for AI technologies will grow 10-times bigger by 2025 (Tractica, 2019), thus attracting massive investments from resource-holders across various industries. ...
Purpose
An important but neglected area of investigation in digital entrepreneurship is the combined role of both core and peripheral members of an emerging technological field in shaping the symbolic and social boundaries of the field. This is a serious gap as both categories of members play a distinct role in expanding the pool of resources of the field. I address this gap by exploring how membership category is related to funding decisions in the emerging field of artificial intelligence (AI).
Design/methodology/approach
The first quantitative study involved a sample of 1,315 AI-based startups which were founded in the period of 2011–2018 in the United States. In the second qualitative study, the author interviewed 32 members of the field (core members, peripheral members and investors) to define the boundaries of their respective role in shaping the social boundaries of the AI field.
Findings
The author finds that core members in the newly founded field of AI were more successful at attracting funding from investors than peripheral members and that size of the founding team, number of lead investors, number of patents and CEO approval were positively related to funding. In the second qualitative study, the author interviewed 30 members of the field (core members, peripheral members and investors) to define their respective role in shaping the social boundaries of the AI field.
Research limitations/implications
This study is one of the first to build on the growing literature in emerging organizational fields to bring empirical evidence that investors adapt their funding strategy to membership categories (core and peripheral members) of a new technological field in their resource allocation decisions. Furthermore, I find that core and peripheral members claim distinct roles in their participation and contribution to the field in terms of technological developments, and that although core members attract more resources than peripheral members, both actors play a significant role in expanding the field’s social boundaries.
Practical implications
Core AI entrepreneurs who wish to attract funding may consider operating in fewer categories in order to be perceived as core members of the field, and thus focus their activities and limited resources to build internal AI capabilities. Entrepreneurs may invest early in filing a patent to signal their in-house AI capabilities to investors.
Social implications
The social boundaries of an emerging technological field are shaped by a multitude of actors and not only the core members of the field. The author should pay attention to the role of each category of actors and build on their contributions to expand a promising field.
Originality/value
This paper is among the first to build on the growing literature in emerging organizational fields to study the resource acquisition strategies of entrepreneurs in a newly establishing technological field.
... The respondent nodes must be intelligent enough to make the decisions in a real-time scenario. The decentralized approach is required to overcome various Big-Data and other issues in the centralized system [12]. The intrusion detection mechanism must be employed near the respondent nodes in a decentralized manner. ...
... With the collected data and the results of the load forecasting procedure, a power merchandiser may extrapolate the upcoming demand for load. Two examples of decentralised and distributed systems that make use of DML are the internet of things (IoT) and wireless communication [6]. By the application of ANN and various other methods of machine learning, DDL has introduced a standard for computing the training procedures of a wide variety of devices. ...
Smart grids depend on AI-based load forecasting to estimate future power demand (AI). Deep learning is especially important in smart grid load forecasting with neural networks (ANN). Processing time and data are needed to count smart grid deep learning. Combining data would speed load projections. The bottleneck strategy has been abandoned to attain this precision. Keeping the lights on requires short-term electricity demand prediction. But, the load’s intricacy and volatility make it fun to predict. EEMD breaks the load into many frequency-dependent components of different strengths. MLR predicts low-frequency regularities, while LSTM neural networks predict high-frequency components. Computational extent is unchanged. Despite its varied aggregation scope, the electric grid’s large data can be used to create the most effective deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Hence, a suitable forecasting strategy uses deep learning with a Micro-clustering (MC) job that mixes unsupervised and supervised clustering tasks utilizingKmeans and Gaussian Support Vector Machine. To guarantee accuracy. B-bidirectional LSTMs can store feed-forward and future hidden-layer data. Feedback and feed-forward loops do this. The DaviesBouldering index determined cluster production per hour. MC with B-LSTM networks improves prediction,especially around spike locations. Forecasting RE generation and grid load is difficult. Prosumer microgrids (PMGs) sell electricity to aggregators. A hybrid machine learning-based load and weather data transmission method provides the biggest benefit. ANFIS, MLP, and radial basis function artificial neural networks (ANNs) would be used in this technique (RBF). Machine learning-based hybrid forecasting can improve accuracy.
... Transparency & Compliance [110] ChainGuard Uses smart contracts to ensure that AI algorithms are transparent and auditable. ...
This research paper reviews the potential of smart contracts for responsible AI with a focus on frameworks, hardware, energy efficiency, and cyberattacks. Smart contracts are digital agreements that are executed by a blockchain, and they have the potential to revolutionize the way we conduct business by increasing transparency and trust. When it comes to responsible AI systems, smart contracts can play a crucial role in ensuring that the terms and conditions of the contract are fair and transparent as well as that any automated decision-making is explainable and auditable. Furthermore, the energy consumption of blockchain networks has been a matter of concern; this article explores the energy efficiency element of smart contracts. Energy efficiency in smart contracts may be enhanced by the use of techniques such as off-chain processing and sharding. The study emphasises the need for careful auditing and testing of smart contract code in order to protect against cyberattacks along with the use of secure libraries and frameworks to lessen the likelihood of smart contract vulnerabilities.
In response to the rapidly evolving business environment, human resource (HR) management is undergoing significant transformation and integration of cutting-edge technologies. This abstract provides an overview of emerging trends and technologies shaping the future of human resource management. As organizations strive for competitiveness, agility and flexibility, they are turning to innovative solutions to effectively attract, retain and develop talent.
The first trend is the increasing importance of data-driven decision-making in HR. Data analytics, artificial intelligence and machine learning give HR professionals
deeper insights into employee behavior, productivity and engagement This data-driven approach is reshaping recruitment, talent development and employee engagement strategies. AI (Artificial Intelligence) has dramatically changed the use of human resources. Its effect on the field is far from revolutionary. In conclusion, AI has fundamentally changed the way HR works The impact of AI on HR practices is
transformational, delivering increased productivity, data-driven decision-making,
and the ability to personalize and inclusively create HR practices. Adopting AI is critical for HR professionals to remain competitive and effective in the ever-evolving business environment. In conclusion, it is shaping the future of human resource management through the combination of advanced technology and
trends. Organizations that embrace datadriven decision-making, embrace the gig
economy, prioritize DEI, improve employee experiences, and leverage automation and AI will be best suited to navigate the ever-changing HR landscape in to attract, retain, and attract talent. And compete in development.
Keywords: Emerging Trends, Technologies, HR Management, talent acquisition, chat box.
This chapter is dedicated to the consideration of the Cybernetic Revolution that is the last of the greatest technological revolutions in all history following the Agrarian and Industrial ones. It is a major transition from the industrial production to the production and service sector based on the implementation of self-regulating systems. The first phase of this revolution started in the 1950 and 1960s. Between the 2030 and 2070s, the final phase of this revolution will lead to a new level of self-operating control, namely the level of self-regulating/managing systems. They are systems that, by means of the embedded programs and a number of intelligent components, can regulate themselves to operate independently with no human intervention. These systems will become the major part of technological process during the forthcoming phase of the Cybernetic Revolution named the epoch of self-regulating/managing systems. But these systems are not only technical devices but a wider range of systems and control processes of a biological, physiological, techno-biological, social and other nature, with a high level of self-regulation, which are and will be implemented in various areas (including medicine, genetic engineering, robotics, social relations). Special sections discuss the differences between self-regulating systems and artificial intelligence.
This chapter focuses on exploring the implications of blockchain technology on the practice of financial auditing. It begins by explaining the fundamentals of financial auditing, and then provides a historical context for digitization within auditing. This is followed by a thorough examination of blockchain technology, including its architecture, characteristics, and categorizations. The chapter then highlights the integration of blockchain into financial auditing, highlighting benefits such as improved risk management, automated audit processes, fraud reduction, cost savings, and the emergence of a continuous audit model. The changing role of auditors in this technologically evolving landscape is also discussed. This synthesis provides an in-depth perspective on the transformative potential of blockchain to improve the efficiency and transparency of financial audit.
Does acquiring artificial intelligence (AI) technologies from the US or China render countries more authoritarian or technologically less advantageous? In this article, we explore to what extent importing AI/high-tech from the US and/or China goes parallel with importers' (a) democratization or autocratization, (b) state capacity, and (c) technological progress across a decade (2010-2020). Our work demonstrates that not only are Chinese AI/high-tech exports not congruous with importers' democratic backsliding, but autocratization attributed to Chinese AI is also visible in importers of US AI. In addition, for most indicators, we do not observe any significant effect of acquiring AI from the US or China on importers' state capacity or technological progress across the same period. Instead, we find that the story has a global inequality dimension as Chinese exports are clustered around countries with a lower GDP per capita, whereas US high-technology exports are clustered around relatively wealthier states with slightly weaker capacity over territorial control. Overall, the article empirically demonstrates the limitations of some of the prevalent policy discourses surrounding the global diffusion of AI and its contribution to democratization, state capacity, and technological development of importer nations.
This article explores the potential ways in which artificial intelligence (AI) can support veganism, a lifestyle that aims to promote the protection of animals and also avoids the consumption of animal products for environmental and health reasons. The first part of the article discusses the technical requirements for utilizing AI technologies in the mentioned field. The second part provides an overview of potential use cases, including facilitating consumer change with the help of AI, technologically augmenting undercover investigations in factory farms, raising the efficiency of nongovernment organizations promoting plant-based lifestyles, and so forth. The article acknowledges that the deployment of AI should not happen in a “solutionist” manner, meaning to always consider nontechnical means for achieving desired outcomes. However, it is important for organizations promoting veganism to realize the potential of modern data-driven tools and to merge and share their data to reach common goals.
Purpose
This study aims to predict artificial intelligence (AI) technology development and the impact of AI utilization activity on companies, to identify AI strategies dealing with the broad innovation activity of AI, and to construct the strategic decision-making framework of AI strategies for a small- and medium-sized enterprise (hereafter SME), to improve strategic decision-making practices of AI strategy in SMEs.
Design/methodology/approach
This study used the multiple methods on the design of two data collection stages. The first stage is an expertise-based approach. It organized the three groups of expert panels and conducted the Delphi survey on them in combination with the brainstorming of technology, innovation and strategy in the fourth industrial revolution. The second stage is in the complement approach of expertise-based results. It used the literature review to involve the analysis of academic and practical papers, reports and audio materials relating to technology development, innovation types and strategies of AI. Additionally, it organized the four semi-structured interviews. Finally, this study used the mind-map and decision tree to conduct each analysis and synthesize each analytical result.
Findings
This study identifies the precondition and four paths of AI technological development classifying into specialized AI, AI convergence with other technologies, general AI and AI control methods. It captures the impact of non- and technological innovation through AI on companies. Second, it identifies and classifies the six types of AI strategy: the bystander, capability-building, capability-holding, management-enhancing, market-enhancing and new-market-creating strategy. By using the decision tree, it constructs the strategic decision-making framework containing six AI strategies. Actionable points, strategic priorities and relevant instruments are suggested.
Research limitations/implications
The strategic decision-making framework covering from AI technology development to utilization in a SME can help understand the strategic behaviours in SMEs. The typology of six AI strategies implies the broad innovation behaviours in SMEs. It can lead to further research to understand the pattern of strategic and innovation behaviour on AI.
Practical implications
This practical study can help executives, managers and engineers in SMEs to develop their strategic practices through the strategic decision framework and six AI strategies.
Originality/value
This practical study elicits the six types of AI strategy and constructs the strategic decision-making framework of six AI strategies from AI technology development to utilization. It can contribute to improving the practices of strategic decision-making in SMEs.
This chapter examines technologies’ current and future development in the framework of the Cybernetic Revolution—the third of the largest production (or technological) revolutions after the Agrarian and Industrial ones. The Cybernetic Revolution is a fundamental transition from industrial production to the production of services and goods based on the widespread implementation of self-regulating systems, that is, systems that can function in the absence or with minimum involvement of people and independently make complex decisions. This transition has already started and will continue up to the 2070s. The Cybernetic Revolution began its active development in the 1950s and has now finished its modernization phase. At the moment, the key technologies are information and communication technology and artificial intelligence, whose role in society is gradually increasing, and they come with benefits and potential risks. However, Grinin & Grinin assume that from the 2030s, the new—final—phase of the Cybernetic Revolution will start. Its major technological breakthroughs will lead to self-regulating systems’ formation and widespread implementation. So, Grinin & Grinin assume that new technologies will emerge. They forecast that it will be a set of technological spheres, and the MANBRIC complex/convergence is taking shape and will actively develop in the final phase of the Cybernetic Revolution (in the 2030s–2070s). The MANBRIC is an abbreviation formed from the initial letters of the seven breakthrough areas: Medicine-Additive-Nano-Bio-Robotics-Info-Cognitive technologies. These technological fields closely interact and corroborate each other and will continue to do so increasingly in the future. Due to its specific characteristics, medicine will be an integral part of the MANBRIC complex. Grinin & Grinin also offer some scenarios for further technological development. They significantly depend on the areas where technological breakthroughs will start. The main developmental scenario is presented as a breakthrough that will occur in the 2030s in the field of medicine, especially at the nexus of its new directions and some areas of the MANBRIC. There will be the introduction of innovations based on self-regulating systems in various fields of social activity (economy, medicine, biology, and socio-administrative structures). Grinin & Grinin describe the most favorable scenario and recommend how to move toward this scenario.
The potential of artificial intelligence (AI) to constitute a general-purpose technology with diverse algorithmic specifications makes it challenging to assess its overall impact on existing socio-economic regimes. Leveraging the multi-level perspective, we seek to depict the trajectory of micro-, meso-, and macro-level forces and their interactions to characterize AI transition pathways in industry. We treat business and information systems literature as a proxy capturing business practices that relate to factors influencing AI transitions on all three different levels. Based on 10,036 publications over 25 years, we map the topic landscape of AI-related research, longitudinal patterns of topics, and structural changes of topic networks. The results indicate a strong and myopic focus on technological capabilities and efficiency rationales. Topic network structures indicate that transition pathways may diverge between a symbiotic and stabilizing transformation process and a more radical pathway of regime substitution. Based on these findings, we argue that sociotechnical transition pathways may not only occur in sequence, but simultaneously and ambiguously. This highlights the need for a nuanced understanding of convergent and divergent transition pathways for emerging digital general-purpose technology that do not tend to settle on one dominant design. We propose to leverage paradox theory to reconcile these tensions.
JEL: M000, O310, O320, 033
This paper pioneers an interdisciplinary framework integrating behavioral finance, ethics, and cutting-edge machine learning to elucidate the promise and perils as AI proliferates in finance. Novel taxonomies are proposed classifying biases as cognitive, social, or data-driven. The paper reconceptualizes market efficiency as aspectrum using adaptive systems modeling. It constructs innovative optimization frameworks synthesizing prospect theory and principal-agent constructs to model human-AI alignments and misalignments. Multi-agent simulations offer pathways to understand emergent volatility from AI ecosystems. The paper presses for retrofitting orthodox theories and developing novel theoretical paradigms to address challenges like black box governance, synchronized algorithmic behavior, andhuman-AI value misalignments. It offers proposals like regulatory sandboxes, retail representation inoversight boards, and AI-centric financial literacy programs. Mathematical modeling of bias interactions and amplification provides tools to audit algorithms.
Blockchain is transforming not only the way of recording, processing, and storing financial transactions and information, but also the way audit firms can practise their profession. The purpose of this article is to examine how this technology will affect the audit process quality. An empirical study was conducted on a sample of Egyptian banks that use Blockchain Technology in the period from (2017) to (2021). The Conceptual Framework and Literature Review concluded that this technology could affect audit firms at six key levels. Blockchain will allow an auditor to: (1) Save time and improve the efficiency of their audit, (2) Favour an audit covering the whole population instead of an audit based on sampling techniques, (3) Focus the audit on testing controls rather than testing transactions, (4) Set up a continuous audit process, (5) Play a more strategic audit role, and (6) Develop new advisory services. Furthermore, the empirical study concluded that there is a significant relationship between Blockchain and Audit quality in the banking sector. The results underline the need for the establishment of a clear and coherent legislative system and new audit standards, allowing auditors to embed this technology and enhance audit practices.
The Internet of things emerges in the world’s smart evolution through Artificial Intelligence (AI) and Blockchain Technology in Cyber-Physical Systems, which increases the exponential rate of smart manufacturing devices. As more industrial devices are getting connected day by day. Obtaining that massive amount of data transfer easy efficiency and data centralization is much more difficult to process by any human. Blockchain technology has proposed AI-based Cyber-Physical Systems (CPS) for the industry, conferring a secure and efficient financial transaction interoperability, communication security, and processing of a large amount of data. However, there is a wide deployment of industrial compatibility through the Internet of Things (IoT), but this technology will benefit the industrial devices to maintain, predict and awareness on their own. On the one hand, Blockchain Technology advances the device’s authentication security enhancement; on the other hand, AI will provide adaptive learning toward cyber attacks during low batteries. Blockchain will also provide load balancing of Edge devices. We explained Blockchain implementation to fabricate a particular AI-based hybrid medical device and Smart manufacturing based on the proposed design. AI authentication will leverage the features of the security systems, and Blockchain Technology will validate the data privacy and Secure data transfer. With Smart monitoring and supervision sub-systems, the CPS can decrease expert technicians’ needs and diminish manufacturing limits during implantation. Therefore, CPS integration could also lower the cost of fabrication. This chapter discussed the integration of Blockchain Technology and AI in a cyber-physical system. The integration of CPS in Smart Manufacturing and Medical Devices is well explained. The challenges for CPS are also described in this chapter.KeywordsInternet of Things (IoT)Cyber-Physical System (CPS)Artificial Intelligence (AI)Blockchain Technology
The Internet of Things (IoT) allows city officials to monitor the city in real time and communicate smoothly with the community. Smart cities need to provide the best possible service to their citizens on the most basic infrastructure. A successful smart city should make any service available to all in general and equally. In this paper, an artificial intelligence (AI) based smart sustainable IoT model was proposed to enhance the different services in the smart city environment. Where the list of services has begun to play a vital role in the construction of smart cities. The service tracks route priority and non-priority services to various locations. The proposed model provides a prominent place for both services. At a cut-off level, the proposed model achieved 94.97% of service recognition, 3.3% of service rejection, 94.06% of service accuracy, 95.86% of service precision, 94.34% of service recall, and 95.68% of F1-Score while compared with the existing models.KeywordsSmart citiesElectronic methodsSensorsInternet of thingsArtificial intelligencePriority routing
The advent of advanced machine learning and the creation of increasingly complex Artificial Intelligence (AI) has become a core constitutive element of what has been termed as the Fourth Industrial Revolution. Its potential impact, —not only on worldwide economic development but also on key civilizational factors, like the conceptualization of work as essential to the idea of the human being— offers substantial challenges to be faced in the near future, both by the academia and decision makers around the world. Considering the potential impact of this technology, this paper seeks to understand the way that information about it has been delivered to relevant decision makers through the use of legislative research services and what is the best way to improve this process through the use of the Opening Up analytical framework.
The purpose of this study is to investigate consumers’ emotional responses to AI defeating people. Meanwhile, we investigate the negative spillover effect of AI defeating people on consumers’ attitudes toward AI companies. We also try to alleviate this spillover effect.
Using four studies to test the hypotheses. In Study 1, we use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers’ emotions. In Studies 2 to 4, we test the effect of AI defeating people on consumers’ attitudes, the mediating effect of negative emotions, and the moderating effect of different intentions.
We find that AI defeating people increases consumers’ negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers’ unfavored attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.
Our findings remind the governments, policymakers, and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. We help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.
This paper is the first study to examine the adverse effects of AI defeating humans. We contribute to research on the dark side of AI, the outcomes of competition matches, and the method to analyze emotions in user-generated content.
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