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The COVID-19 pandemic has profoundly impacted every aspect of our lives, from economic to the social facets of contemporary society. While the new COVID-19 waves may not be anticipated to be as severe as previous ones, it would be unreasonable to assume that they will cease any time soon. Consequently, forecasting the number of future infections, recovered patients, and death cases remains a very much logical step in trying to fight against further waves, in conjunction with ongoing vaccination efforts. In this paper, we investigate the efficiency of three intelligent machine learning algorithms, namely GMDH, Bi-LSTM, and GA + NN, for COVID-19 forecasting, with an application to Iran and the United Kingdom. The experimental results show that the algorithms can be used to forecast the next six months of COVID-19 in terms of confirmed, recovered, and death cases, which gives a more ample timeframe for using the results to make better practical yet strategic decisions and take appropriate actions or measures to deploy resources effectively to contain or curb the spread of the coronavirus. Despite the distinct dynamics observed in the data, our analysis proves the robustness of the employed models. [ABSTRACT FROM AUTHOR]

Many governments find it challenging to set up a regulatory regime to govern rapidly developing Autonomous Vehicles (AVs) technologies empowered by Artificial Intelligence. This paper analyzes flexible regulation as a tool for assessing regulatory reforms that govern disruptive innovations such as AVs. After defining flexible regulation as regulation that gives regulated entities choices for how to comply with the regulatory objectives, this paper develops Regulatory Flexibility Indicators (RFI) for rule structure, enforcement structure, and regulatory feedback. We study AVs regulatory reforms that took place recently in the United Kingdom and South Korea, focusing on how such reforms have enhanced regulation flexibility. This paper finds that regulatory governance in the United Kingdom is more flexible than in South Korea, indicating aspects of further reforms for improving regulatory flexibility. [ABSTRACT FROM AUTHOR]

The global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care. [ABSTRACT FROM AUTHOR]

This paper considers legal contestation in the UK as a source of useful reflections for AI policy. The government has published a 'National AI Strategy', but it is unclear how effective this will be given doubts about levels of public trust. One key concern is the UK's apparent 'side-lining' of the law. A series of events were convened to investigate critical legal perspectives on the issues, culminating in an expert workshop addressing five sectors. Participants discussed AI in the context of wider trends towards automated decision-making (ADM). A recent proliferation in legal actions is expected to continue. The discussions illuminated the various ways in which individual examples connect systematically to developments in governance and broader 'AI-related decision-making', particularly due to chronic problems with transparency and awareness. This provides a fresh and current insight into the perspectives of key groups advancing criticisms relevant to policy in this area. Policymakers' neglect of the law and legal processes is contributing to quality issues with recent practical ADM implementation in the UK. Strong signals are now required to switch back from the vicious cycle of increasing mistrust to an approach capable of generating public trust. Suggestions are summarised for consideration by policymakers. [ABSTRACT FROM AUTHOR]

There is a lack of research on the use of Artificial Intelligence (AI) within the construction sector in the UK. Therefore, this research seeks to explore AI uses and its benefits on the UK construction industry. Given the new and unexplored nature of the research problem, a qualitative case study research methodology was adopted. Construction projects, which have adopted some of the AI forms all around the UK, are investigated extracting the use of the technology and how processes within the construction industry can benefit from the adoption of AI. Developing an AI system can benefit the construction industry in terms of the planning process, organizations have implemented AI systems to accomplish tasks such as tunnel inspections, safety hazard identifying, and risk management. These implementations have proved to be successful and efficient in improving production within the organizations. This paper highlights the key uses and benefits of AI-based systems within the construction industry. The business model was developed based on current work practices with AI and without AI. It is concluded that the industry as a whole should enhance coordination and cooperation across the value chain and agree on common goals and standards for the adoption of AI. [ABSTRACT FROM AUTHOR]

The background to this article is the seeming revival of a sandbox approach to FinTech regulation within the EU, as epitomised by legislative initiatives on AI and DLT. The article contributes to existing literature on the topic by highlighting how the current design of sandboxes is not being informed by sufficiently comprehensive empirical evidence from international examples. As well as examining relevant provisions of the European Commission's proposals on AI sandboxes and introduction of a DLT pilot regime, the article refers to UK and Australian examples to demonstrate the varying features of sandboxes. Even if there is continued ambiguity as to the characteristics of sandboxes, broader regulatory supports, such as innovation hubs, can be vital. However, this should not diminish the need for improved clarity and transparency on sandboxes' operations. The ability to make necessary refinements will determine how the EU's regulatory approach to FinTech generally will evolve. [ABSTRACT FROM AUTHOR]

This article presents the first-ever big data study of the student evaluation of teaching (SET) using artificial intelligence (AI). We train natural language processing (NLP) models on 1.6 million student evaluations from the US and the UK. We address two research questions: (1) are these models able to predict student ratings from the student textual feedback, and (2) can AI-powered SET eliminate the problems of the traditional SET based on Likert scale surveys. We test these NLP models on a new dataset of 12,386 university reviews from the UK and on 155 SET reviews from a university that agreed to run a pilot AI project. The trained NLP models exhibited high prediction accuracy, and they learnt two biases from humans, those of extreme responding and assigning higher ratings to less demanding courses. In the future, universities will likely adopt many AI-based tools that have proved successful in client management in other sectors. However, our results make a strong case against using AI as a black box for performativity purposes. It should remain a useful tool for university administrators who are aware of the AI weaknesses documented here. [ABSTRACT FROM AUTHOR]