Uncovering the Hidden Insights of the Government AI Readiness Index: Application of Fuzzy LMAW and Schweizer-Sklar Weighted Framework

Many are the promises of artificial intelligence (AI) and algorithms. Governments around the world are increasingly investing in AI and multiple voices have touted this seemingly unmatched revolution. Better performance, cost reduction, efficient management, crime prediction, and prevention are but a few of the pledges of the AI era. While such promises are recognized, research shows that AI benefits could be overstated. Issues of equity, ethics, justice, and fairness have raised concerns and have been seen as potentially threatening democratic principles. As countries get ready to tap into the AI power, researchers are asking whether preparedness is followed by responsibility checks. In this article, we use the Oxford Insights AI Readiness Index to explore why innovation and readiness in artificial intelligence are not always accompanied by accountability, even for some of the most advanced democracies around the world. Using the Fuzzy‐Set Qualitative Comparative Analysis (fsQCA) approach, we show that advancement in AI is not enough: privacy, transparency, inclusion, and accountability principles are key to ensuring governments tackle the AI challenge responsibly.

Crime prediction, prevention and counteraction with the use of modern technologies should, according to the authors, become a priority task for the state, along with the development of economy, education, medicine and the enhancement of defense capacity. The article describes the concepts of «artificial intelligence», «machine learning», «big data», «deep learning», «neural networks» from the standpoint of how they are used both by criminals and by law enforcement bodies and courts. The authors examine the application of technologies which use artificial intelligence, hi tech crime (fishing, drones, fake information, bots, and so on). They outline modern software solutions based on artificial intelligence and aimed at counteracting crime: software that analyzes big volumes of data, processing of stream videos, facial recognition, contextual searching platforms, etc. The authors also describe the existing resources for predictive analytics (in particular, inter-agency experimental software «Artificial Intelligence in Police Work and Investigation of Criminal Offences»; software for recognizing people based on fragments of their tattoos; facial recognition of people after plastic surgeries in pictures and stream videos, with the generation of variants of their original appearance; platform of contextual intelligence Nigel; system Mayhem and others) and how they can be used to predict both crimes in general and individual criminal behavior. The authors also outline ethical dilemmas connected with legal decisions made by artificial intelligence regarding specific people. They present examples of using artificial intelligence for crime prevention (software COMPAS, criminal community’s psychometric prediction system, Harm Assessment Risk Tool, analytical software complex CEG, crime prediction system PredPol, ePOOLICE system, Palantir software, Russian system «Artificial intelligence»). They also outline the indicators of the early crime prevention system: indicators of matching, lagging, cyclical and counter-cyclical indicators. The authors state that Russia is lagging behind other countries in its use of artificial intelligence in law enforcement and suggest adopting the Modern Strategy of Crime Counteraction, Prediction and Prevention. Possible directions of this strategy are described.

Focus issue: Artificial intelligence in medical physics.

Does Artificial Intelligence (AI) imply the end of criminal law and justice as we know it? This article submits that AI is a transformative technology that seemingly assumes and optimizes the rationalities of criminal law (the effective prevention of crime; the objective, neutral and coherent application of the law etc.), namely by replacing the counterfactual guarantees of the law with the factual guarantees of technology. As a consequence, AI must not be trivialized by criminal law theory. Likewise, it is not enough to subversively criticize the current weaknesses of AI (e.g. vis-à-vis the “bias in, bias out” problem). Rather, criminal law theory should draw on the highflying promises of AI to reflect upon the foundational premises of criminal law. For a criminal law that is mostly a governance tool in the administrative and/or welfare state, AI applications promise the culmination of the law’s very objectives (like the effective inhibition and prevention of crime, e.g. by means of predictive policing; or the political determi- nation of fuzzy sentencing rationales in sentencing algorithms that ensure equal sentences for comparable crimes). For a criminal law, however, that protects liberal freedoms and rests on inter-personal trust, AI may well lead to the passing of the law’s very ideals (e.g. of the presumption of innocence, which can no longer be upheld once everyone, ordinary citizens and judges alike, is deemed a possible risk). The question about “AI as the end of criminal law?” thus eventually raises the two-pronged question “Which criminal law for which society?”. Indeed, what is the status of freedom (especially in a surveillance society needed to power Big Data driven algorithms), trust (especially under the zero trust paradigm that underlies many risk assessment algorithms) and future (especially when algorithms make predictions based on past data) once AI enters into the administration of criminal justice? These are the questions, or so I respectfully submit, that criminal law theory needs to address today in order to come up with a criminal law that is both (for pragmatic reasons) open to technology as well as (for humane reasons) sensible. In all of this, we must take to heart Joachim Hruschka’s great legacy and remain intellectually honest.

Artificial Intelligence (AI) has emerged as a transformative technology, reshaping operational efficiencies and strategic business management across industries. This study employs a bibliometric analysis using VOSviewer to explore the intellectual structure, global collaboration, and thematic trends in AI research from 2000 to 2024. The findings reveal AI’s pivotal role in enhancing operational processes, particularly in cost reduction, efficiency improvement, and data-driven decision-making. Furthermore, AI’s integration into diverse fields such as healthcare, energy management, and cybersecurity underscores its multidisciplinary impact. The visualizations highlight the strong global collaboration among nations, with China, India, and the United States as major contributors to AI research. Despite these advancements, challenges such as ethical concerns, data privacy, and workforce displacement persist. This study emphasizes the need for ethical frameworks, workforce reskilling, and robust international cooperation to maximize AI's benefits while mitigating its challenges. By mapping current trends and identifying future directions, this research contributes to a deeper understanding of AI’s transformative potential in operational and strategic domains.

Artificial Intelligence (AI) has emerged as a transformative technology with significant potential to drive sustainability efforts across various sectors. This chapter examines the transformative power of artificial intelligence (AI) in propelling sustainability across diverse industries. It elucidates the types of AI applications, their underlying components, and their multi-dimensional impact on environmental, social, and economic sustainability. This also analyze industry-specific applications to illustrate how AI is being utilized to create more sustainable practices. Through in-depth case studies, the chapter showcases how leading companies are leveraging AI to revolutionize resource optimization, supply chain management, and decision-making processes, paving the way for a more sustainable future

Recent developments have shown an increase in the use of artificial intelligence in various domains, including medical field, banking, commerce etc. Not only this, artificial Intelligence is being used by various enforcement agencies in India for crime detection and prevention and the reason for the same is to curb the undue burden which is on the enforcement agencies. Green crimes or crime against the environment. Protection of wildlife and environment is of utmost importance and requires immediate attention. Green crime is rather on rise. Even though we have various laws for the protection of environment, yet the enforcement of the same is highly challenging due to vast forest areas and less personal in enforcement agencies. Artificial Intelligence can be a saving grace for the enforcement agencies for the prediction and prevention of green crimes in India. For instance, AI’s potential of Machine Learning and Deep Learning can be extremely helpful in detection of epicentre of green crimes, increasing surveillance and policing in such areas. AI based technologies can be used for monitoring, management and the conservation of biodiversity and forest resources just like it is used by policing agencies of various states for the prediction and prevention of crimes in the city. The paper focuses on the implementation of AI induced technologies for prevention of green crimes in India.

Artificial intelligence speakers, artificial intelligence secretaries, and artificial intelligence translations are all now naturally incorporated into many people’s daily lives. Artificial intelligence, which is used in various fields from simply increasing the convenience of work daily to creating artwork using artificial intelligence technology, is being actively studied in relation to the human emotional field. This study will examine the process in which artificial intelligence newly stimulates human emotions by learning emotions. going beyond just recognizing human emotions. Also, the study will examine the definition, technology, and cases of artificial emotional intelligence that can be used for emotional control or decision-making based on emotional information recognized and learned by artificial intelligence. The study has meaning in suggesting what elements are needed for the industrialization of artificial emotional intelligence.

Purpose The study aims to examine how AI contributes to food waste reduction and improves operational efficiency in the hospitality sector. In the context of sustainability, the research investigates AI’s role in inventory management, process automation and waste tracking. The findings provide insights into the potential of AI technologies to optimize kitchen operations, reduce environmental footprints and enhance resource utilization. The results can assist industry stakeholders in developing AI-driven strategies to improve efficiency and business sustainability. Design/methodology/approach This study employs a quantitative approach and structural equation modeling (SEM) to analyze the impact of artificial intelligence (AI) on food waste reduction in the hospitality industry. A total of 234 managers and head chefs from 117 hospitality establishments in Serbia and Montenegro participated in the survey. The data were analyzed using SmartPLS, focusing on AI applications in inventory management, menu planning, process automation, waste tracking and recycling. The study also incorporates exploratory factor analysis and regression models to assess the significance of AI in optimizing food management and enhancing operational efficiency. Findings The results confirm that AI significantly reduces food waste through improved inventory control, personalized menu planning and automated waste tracking. The modeling demonstrates a positive impact of AI on waste reduction, while real-time monitoring enables swift corrective actions. The findings highlight the economic and environmental benefits of AI, emphasizing its crucial role in optimizing hospitality business operations. Empirical evidence supports AI as a strategic tool for more efficient food management and a more sustainable hospitality sector. Research limitations/implications The study is limited to the hospitality industry in Serbia and Montenegro, which may reduce its applicability to other regions with different economic conditions. The research focuses on managerial perspectives, with less emphasis on consumer habits and behaviors. Although AI yields positive outcomes, challenges such as high implementation costs, staff training and technical integration require further analysis. Future research should include longitudinal studies to assess AI’s long-term impact and explore cultural differences in its adoption. Practical implications The findings suggest that hospitality establishments should invest in AI for inventory tracking, kitchen process automation and real-time waste monitoring to reduce costs and improve efficiency. AI can enhance portion control, demand forecasting and sustainable food sourcing. Additionally, AI analytics can help identify inefficiencies and develop waste reduction strategies, enabling managers to make data-driven decisions, increase profitability and promote environmentally responsible practices. Originality/value This study is among the first to empirically assess the role of AI in food waste reduction in the hospitality sector. It provides novel insights into kitchen operation optimization and sustainability through AI implementation. The study contributes to the literature by integrating various AI functionalities – such as inventory management, process automation and waste tracking – into a comprehensive framework. The findings are valuable for professionals, policymakers and researchers interested in the application of AI for efficient food management and sustainable hospitality services.

Assessing perspectives on artificial intelligence applications to gastroenterology

Outline for a German Strategy for Artificial Intelligence

A meta-analysis of AI and machine learning in project management: Optimizing vaccine development for emerging viral threats in biotechnology.

Hybrid intelligence in procurement: Disillusionment with AI’s superiority?

We are glad to introduce you that the 2021 3rd International Conference on Artificial Intelligence Technologies and Applications (ICAITA 2021) was successfully held on September 10-12, 2021. In light of worldwide travel restriction and the impact of COVID-19, ICAITA 2021 was carried out in the form of virtual conference to avoid personnel gatherings. Because most participants were still highly enthusiastic about participating in this conference, we chose to carry out ICAITA 2021 via online platform according to the original schedule instead of postponing it.ICAITA 2021 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence Technologies and Applications to a common forum. The primary goal of the conference is to promote research and developmental activities in Artificial Intelligence Technologies and Applications and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Artificial Intelligence Technologies and Applications and related areas.This scientific event brings together more than 100 national and international researchers in artificial intelligence technologies and applications. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.We were pleased to invite three distinguished experts to present their insightful speeches. Our first keynote speaker, Prof. Yau Kok Lim, from Sunway University, Malaysia. His research interests include Applied artificial intelligence, 5G networks, Cognitiveradio networks, Routing and clustering, Trust and reputation, Intelligent transportation system. And then we had Prof. Peter Sincak, from Technical University of Kosice, Slovakia. His research includes Artificial Intelligence and Intelligent Systems. Lastly, we were glad to invite Chinthaka Premachandra, from Shibaura Institute of Technology, Sri Lanka. His research interests include Artificial Intelligence, image processing and robotics. In the last part of the conference, all participants were invited to join in a WeChat group to discuss and explore the academic issues after the presentations. The online discussion was lasted for about 30-60 minutes. The first two parts were conducted via online collaboration tool, Zoom, while the online discussion was carried out through instant communication tool, WeChat. The online platform enabled all participants to join this grand academic event from their own home.We are glad to share with you that we still received lots of submissions from the conference during this special period. Hence, we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Artificial Intelligence Applications & Technologies, Computing and the Mind, Foundations of Artificial Intelligence and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.Lastly, we would like to express our sincere gratitude to the Chairman, the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy the gain some valuable knowledge from the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.The Committee of ICAITA 2021List of titles Committee member, General Conference Chair, Technical Program Committee Chair, Academic Committee Chair, Technical Program Committee Member, Academic Committee Member are available in this Pdf.

For the field of telecommunications, the artificial intelligence (AI) has revolutionized the way of networking, due to improved scalability, reliability and efficiency networks. This research provides an insight into the role of artificial intelligence in telecom companies concentrating on its effectiveness on cost reduction, service quality, and network performance analysis. As traditional network growth they are seeing in customer demand for faster issue resolution and better management solutions. Artificial intelligence is one of the key enablers with the help of lowering the cost of operation and decreased energy usage while reducing the reliance on redundant manual actions. Involving objectifying the incident resolution in Bizagi by using simulations of two types of scenarios including artificial intelligence and scenario excluding the artificial intelligence. These scenarios were assessed both on cost, throughput and resource usage. Respondent level data were also collected from a survey that was administered to staff, supported the findings of the case study, data were analysed using Pearson and Tests of the equivalence of the proportions were performed via Chi-square tests in IBM SPSS Statistics. The simulation results reveal that AI based schemes improve the network resource consumption and we also find that energy consumption. speed up customer response times and improve demand management at peak times. Moreover, the incorporation of AI helps telecom companies to foreknow the issues in the service to effectively prevent the future failures. AI adopting plays important in telecom. This paper draws attention to the transformative power of artificial intelligence in sensing achiever in the engineering. Further, a questionnaire that were completed by with the company’s employees helped support the case study results whose analysis was done with Pearson and Chi-square analyses in IBM SPSS Statistics. The findings show that AI- enabled methods maximize network resources and maximize the inflation function. speedup customer reply times but also improve demand management in peak time. Moreover, the The embedded AI integration allows telecom operators to predict and prevent potential failures and hence Reduce the both downtime and downtime. reducing disruptions. This paper emphasizes the important power of artificial intelligence to process telecommunication optimization, which may provide useful for the companies who wish to increase functioning effectiveness and enhance customer satisfaction.