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The world of work is being impacted by a fourth industrial revolution, transformed by artificial intelligence and other emerging technologies. With forecasts suggesting large shares of workers, displaced by automation, in need of upskilling/reskilling, the design of active skills policies is necessary. Conventional methods used to anticipate technological change and changing skill needs, such as skill surveys and forecasting, have limited scope to provide insights into emerging trends. With the increasing use of big data and AI methods, analysts have new 'real-time' tools at their disposal. Skill foresight techniques are also increasingly used to gauge in-depth stakeholder information about future technologies and skill needs. A series of Cedefop guides aims to inform analysts and policy-makers about available skills anticipation methods used to navigate through the uncertainty of changing technologies and skill demands. This second practical guide focuses on automated skills intelligence methods: big data and AI-driven analyses. [Cedefop wishes to acknowledge the research and services of its contracted institution, the Fondazione Giacomo Brodolini (FGB) (lead partner) in consortium with Economix Research and Consulting, Cambridge Econometrics Ltd, and Panteia B.V. For Cedefop Practical Guide 1, see ED613871. For Cedefop Practical Guide 3, see ED613874.]

In the era of accelerated digitalization, the use of artificial intelligence (AI) and big data analysis has become a necessity in crime prevention and combat. This paper explores how AI is used for analyzing operational data, identifying crime patterns, and optimizing law enforcement strategies. In an interconnected world where data volume is growing exponentially, traditional investigative methods are often overwhelmed. Machine learning algorithms, neural networks, and natural language processing enable the rapid analysis of unstructured data, enhancing the ability to predict and prevent crimes. The article presents AI applications in security, including predictive policing, facial recognition, and combating cybercrime. Case studies highlight AI implementations in the U.S., the EU, and China, demonstrating both efficiency and controversies related to privacy and algorithmic discrimination. However, the use of AI raises significant ethical challenges, such as algorithmic biases and the risk of excessive surveillance. In conclusion, the future of AI in security depends on balancing innovation and regulation. The development of explainable predictive models, the reduction of biases, and the adoption of clear international regulations are essential. AI can become a crucial ally for law enforcement, but its use must be transparent, responsible, and focused on respecting fundamental citizen rights. 

More and more, the decisions that rule our lives are made by algorithms. From the news we see in our feeds, to whether or not we qualify for a mortgage, to the rates we pay for health insurance. And while there are demonstrable biases against marginalized communities caused by algorithms, some say the machines are innocent — they’re just doing math. But as more systems rely on big data and artificial intelligence, bias and discrimination is rampant. How do we protect ourselves when we’re at the mercy of the few who create the algorithms? Should there be a universal set of ethical standards? Is regulation needed? How can we be more savvy about the models that increasingly, and imperceptibly, rule our lives? Speakers: Jason Pontin, Surya Mattu, Virginia Eubanks

This research’s primary objective is to investigate the impact of artificial intelligence, big data analytics, and business intelligence on digital transformation in UAE telecommunications companies. Following the completion of the sample checking procedure, 200 samples were collected. The Amos program was used to process all the collected data in the research study. The findings of the research demonstrate a set of relationships and linkages that can enhance digital transformation. Moreover, a summary of the findings revealed that all three hypotheses H1, H2, and H3 were found to be valid and significant. This study concluded that artificial intelligence, big data analytics, and business intelligence have a positive impact on developing and enhancing for digital transformation.

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This scoping review investigates how healthcare data and/or AI policy documents address diversity in datasets to mitigate algorithmic bias. It explores challenges in representing data on socially constructed categories of difference. The review also examines requirements for equitable representation of identities, specifically race/ethnicity, sex/gender, and SES mandated by these policy documents. Additionally, it explores how governance proposes collecting and categorising data on marginalised identities of socially constructed categories of difference in healthcare AI applications.

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance.

Digital transformation through the application of artificial intelligence (AI) and big data has become a key strategy in improving organizational performance in various sectors. The implementation of these technologies enables increased operational efficiency, accelerated decision-making, and innovation in services and products. However, on the other hand, organizations face various challenges, including technology infrastructure readiness, employee resistance to change, and data security risks. Therefore, an effective change management strategy is needed to ensure the successful implementation of AI and big data in supporting digital transformation. This research uses a qualitative approach with case study method and literature review. Case studies were conducted on organizations that have implemented AI and big data to analyze their impact on operational performance and business strategy. Data was collected through in-depth interviews with key stakeholders, observation of work processes, and analysis of organizational documents. In addition, a literature review was used to identify findings from previous research on the enablers and barriers of digital technology implementation.  The results show that the implementation of AI and big data has a positive impact on organizational performance, especially in improving efficiency, decision-making accuracy, and innovation. However, successful implementation is strongly influenced by the organization's readiness to face technical and cultural challenges. Organizations that have visionary leadership, effective change management strategies, and training programs for employees tend to be more successful in adopting digital technologies.

Pharmacovigilance PV is essential for monitoring the safety and efficacy of medications, ensuring public health protection. With the advent of precision medicine, which tailors treatments to individual patient characteristics, the complexity of PV has increased, necessitating advanced methodologies. Harnessing big data and artificial intelligence AI offers transformative potential for enhancing PV in precision medicine. Big data, encompassing diverse sources such as electronic health records EHRs , genomic data, and social media, provides a comprehensive view of drug effects across varied populations. AI techniques, including machine learning ML and natural language processing NLP , enable the efficient processing and analysis of this vast and complex data, facilitating early detection of adverse drug reactions ADRs , prediction of drug interactions, and personalized risk assessment. This review explores the current state and potential of big data and AI in PV, highlighting their applications, benefits, and challenges. Key topics include the integration of heterogeneous data sources, development of predictive models, real time monitoring, and ethical considerations. The review concludes with recommendations for future research and practice to fully leverage these technologies in achieving safer and more effective pharmacotherapy in the era of precision medicine. Mahaboob Begum | Syed Abdul Khadeer Ali | Mohammad Afreed | N. Teja | Kotagiri Rishitha "Harnessing Big Data and Artificial Intelligence for Pharmacovigilance in Precision Medicine" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-4 , August 2024, URL: https://www.ijtsrd.com/papers/ijtsrd67204.pdf Paper Url: https://www.ijtsrd.com/pharmacy/other/67204/harnessing-big-data-and-artificial-intelligence-for-pharmacovigilance-in-precision-medicine/mahaboob-begum

The Big Data embodies a technology that permits the storage, processing, and management of broad and complex data sets in which traditional data processing applications are not applicable. These sets of data are usually characterized by a substantial volume of information that they carry, variety, versatility in terms of the format in which they are written, as well as a high-speed ingress which is often greater than the speed of processing. A particular challenge is the data which are coming from the Internet of Things (IoT) “world” that is constantly expanding and which already consists of several billion devices that can measure various parameters in the environment, communicate, process and transmit information. The data streams emanating from these devices are changing traditional approaches to data management and contribute to the emergence of the Big Data paradigm. This paper discusses the characteristics of the IoT infrastructure in terms of vast scale sensor applications and a possibility of connectivity of sensor networks, as well as various techniques of collection, storage, archiving and processing of the data on the cloud.