Pub Date : 2024-04-02DOI: 10.60087/jaigs.vol03.issue01.p26
Harish Padmanaban
Scalability is a critical aspect of deploying machine learning (ML) algorithms on large-scale data infrastructure. As datasets grow in size and complexity, organizations face challenges in efficiently processing and analyzing data to derive meaningful insights. This paper explores the strategies and techniques employed to scale ML algorithms effectively on extensive data infrastructure. From optimizing computational resources to implementing parallel processing frameworks, various approaches are examined to ensure the seamless integration of ML models with large-scale data systems.
可扩展性是在大规模数据基础设施上部署机器学习（ML）算法的一个关键方面。随着数据集的规模和复杂性不断增加，企业在高效处理和分析数据以获得有意义的见解方面面临着挑战。本文探讨了在大规模数据基础设施上有效扩展 ML 算法所采用的策略和技术。从优化计算资源到实施并行处理框架，本文研究了各种方法，以确保 ML 模型与大规模数据系统的无缝集成。
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Pub Date : 2024-03-30DOI: 10.60087/jaigs.v2i1.p228
José Gabriel Carrasco Ramírez, Md.mafiqul Islam
Navigating the complexities of scaling AI/ML infrastructure unveils a terrain rife with challenges and opportunities. This exploration delves into the multifaceted landscape, addressing key aspects such as resource expansion, data management, parallel processing, algorithmic optimization, orchestration, monitoring, streamlined pipelines, automation, financial considerations, and security. By embracing innovation and resilience, organizations can effectively harness the potential of AI and ML technologies while mitigating scalability hurdles.
探索扩展人工智能/ML 基础设施的复杂性揭示了一个充满挑战和机遇的领域。本文将深入探讨多方面的问题，涉及资源扩展、数据管理、并行处理、算法优化、协调、监控、简化管道、自动化、财务考虑因素和安全性等关键方面。通过拥抱创新和弹性，企业可以有效利用人工智能和 ML 技术的潜力，同时缓解可扩展性障碍。
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Pub Date : 2024-03-29DOI: 10.60087/jaigs.v2i1.p208
Hassan Rehan
Cloud computing represents a transformative approach to delivering IT services via an interconnected network of servers, collectively referred to as the "Cloud." This virtualized environment seamlessly integrates networks, servers, applications, storage, and services, facilitating convenient access for users with minimal administrative overhead. This comprehensive review article centers on two fundamental pillars of cloud computing: virtualization and containerization. It examines their groundbreaking influence on resource management and deployment efficiency. Additionally, the paper explores upcoming trends and challenges expected to shape the cloud computing landscape from 2025 to 2030.An emphasis is placed on the anticipated adoption of hybrid and multi-cloud strategies, providing organizations with tailored solutions while reducing the risks associated with vendor lock-in. The emergence of edge computing is highlighted as a key solution to address latency concerns and foster a competitive environment for the Internet of Things (IoT). Furthermore, the integration of artificial intelligence (AI) and machine learning within cloud frameworks is poised to unlock new avenues of innovation and optimization, propelling digital transformation. The article underscores the critical need for enhanced security measures to protect sensitive data and ensure user privacy. Ongoing price competitions among cloud providers and heightened regulatory scrutiny are also examined, underscoring the dynamic nature of cloud computing. By offering insights into the past, present, and future trajectory of cloud computing, this article affirms its pivotal role in driving digital innovation and empowering organizations to thrive in an interconnected world. In conclusion, the article provides recommendations for businesses to leverage emerging technologies and effectively navigate evolving challenges in the realm of cloud computing.
云计算是一种通过服务器互连网络（统称为 "云"）提供 IT 服务的变革性方法。这种虚拟化环境无缝集成了网络、服务器、应用程序、存储和服务，方便用户访问，同时将管理开销降至最低。这篇综合评论文章围绕云计算的两大基本支柱展开：虚拟化和容器化。文章探讨了它们对资源管理和部署效率的突破性影响。此外，文章还探讨了即将到来的趋势和挑战，预计这些趋势和挑战将塑造 2025 年至 2030 年的云计算格局。文章强调了混合云和多云战略的预期采用，为企业提供量身定制的解决方案，同时降低与供应商锁定相关的风险。报告强调，边缘计算的出现是解决延迟问题和促进物联网（IoT）竞争环境的关键解决方案。此外，人工智能（AI）和机器学习在云框架内的整合有望开辟创新和优化的新途径，推动数字化转型。文章强调了加强安全措施以保护敏感数据和确保用户隐私的迫切需要。文章还探讨了云计算提供商之间正在进行的价格竞争和监管审查的加强，强调了云计算的动态性质。通过对云计算过去、现在和未来发展轨迹的深入分析，本文肯定了云计算在推动数字创新和增强企业在互联世界中蓬勃发展的能力方面所发挥的关键作用。最后，文章为企业利用新兴技术和有效驾驭云计算领域不断变化的挑战提供了建议。
{"title":"Revolutionizing America's Cloud Computing the Pivotal Role of AI in Driving Innovation and Security","authors":"Hassan Rehan","doi":"10.60087/jaigs.v2i1.p208","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p208","url":null,"abstract":"Cloud computing represents a transformative approach to delivering IT services via an interconnected network of servers, collectively referred to as the \"Cloud.\" This virtualized environment seamlessly integrates networks, servers, applications, storage, and services, facilitating convenient access for users with minimal administrative overhead. This comprehensive review article centers on two fundamental pillars of cloud computing: virtualization and containerization. It examines their groundbreaking influence on resource management and deployment efficiency. Additionally, the paper explores upcoming trends and challenges expected to shape the cloud computing landscape from 2025 to 2030.An emphasis is placed on the anticipated adoption of hybrid and multi-cloud strategies, providing organizations with tailored solutions while reducing the risks associated with vendor lock-in. The emergence of edge computing is highlighted as a key solution to address latency concerns and foster a competitive environment for the Internet of Things (IoT). Furthermore, the integration of artificial intelligence (AI) and machine learning within cloud frameworks is poised to unlock new avenues of innovation and optimization, propelling digital transformation. The article underscores the critical need for enhanced security measures to protect sensitive data and ensure user privacy. Ongoing price competitions among cloud providers and heightened regulatory scrutiny are also examined, underscoring the dynamic nature of cloud computing. By offering insights into the past, present, and future trajectory of cloud computing, this article affirms its pivotal role in driving digital innovation and empowering organizations to thrive in an interconnected world. In conclusion, the article provides recommendations for businesses to leverage emerging technologies and effectively navigate evolving challenges in the realm of cloud computing.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"60 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-22DOI: 10.60087/jaigs.v2i1.p188
Md.mafiqul Islam
The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications. AI's impact is observed across various domains, including detecting clinical conditions in medical imaging, early diagnosis of coronavirus disease 2019 (COVID-19), virtual patient care utilizing AI-powered tools, electronic health record management, enhancing patient engagement and treatment compliance, reducing administrative burdens for healthcare professionals (HCPs), drug and vaccine discovery, identification of medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. However, the integration of AI in healthcare encounters several technical, ethical, and social challenges, such as privacy concerns, safety issues, autonomy and consent, cost considerations, information transparency, access disparities, and efficacy uncertainties. Effective governance of AI applications is imperative to ensure patient safety, accountability, and to bolster HCPs' confidence, thus fostering acceptance and yielding significant health benefits. Precise governance is essential to address regulatory, ethical, and trust concerns while advancing the adoption and implementation of AI in healthcare. With the onset of the COVID-19 pandemic, AI has sparked a healthcare revolution, signaling a promising leap forward to meet future healthcare demands.
{"title":"Exploring the Impact of Artificial Intelligence in Healthcare","authors":"Md.mafiqul Islam","doi":"10.60087/jaigs.v2i1.p188","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p188","url":null,"abstract":"The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications. AI's impact is observed across various domains, including detecting clinical conditions in medical imaging, early diagnosis of coronavirus disease 2019 (COVID-19), virtual patient care utilizing AI-powered tools, electronic health record management, enhancing patient engagement and treatment compliance, reducing administrative burdens for healthcare professionals (HCPs), drug and vaccine discovery, identification of medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. However, the integration of AI in healthcare encounters several technical, ethical, and social challenges, such as privacy concerns, safety issues, autonomy and consent, cost considerations, information transparency, access disparities, and efficacy uncertainties. Effective governance of AI applications is imperative to ensure patient safety, accountability, and to bolster HCPs' confidence, thus fostering acceptance and yielding significant health benefits. Precise governance is essential to address regulatory, ethical, and trust concerns while advancing the adoption and implementation of AI in healthcare. With the onset of the COVID-19 pandemic, AI has sparked a healthcare revolution, signaling a promising leap forward to meet future healthcare demands.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140214960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of DevOps practices within healthcare systems has emerged as a promising approach to enhance agility, efficiency, and reliability in delivering healthcare services. This systematic review explores the implementation of DevOps methodologies within healthcare contexts, focusing on its impact on quality of care, operational efficiency, and overall system performance. Through a comprehensive analysis of existing literature, this review synthesizes key findings, challenges, and best practices associated with DevOps adoption in healthcare. The review highlights successful case studies, identifies common patterns in DevOps implementation, and examines the role of cultural transformation, automation, and collaboration in fostering successful DevOps practices within healthcare organizations. Additionally, this review discusses the potential benefits and limitations of applying DevOps principles in healthcare settings, offering insights for practitioners, researchers, and policymakers seeking to leverage DevOps to improve healthcare delivery.
{"title":"Implementation of DevOps in healthcare systems","authors":"Omolola Akinola, Omowunmi Oyerinde, Akintunde Akinola","doi":"10.60087/jaigs.v2i1.p170","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p170","url":null,"abstract":"The integration of DevOps practices within healthcare systems has emerged as a promising approach to enhance agility, efficiency, and reliability in delivering healthcare services. This systematic review explores the implementation of DevOps methodologies within healthcare contexts, focusing on its impact on quality of care, operational efficiency, and overall system performance. Through a comprehensive analysis of existing literature, this review synthesizes key findings, challenges, and best practices associated with DevOps adoption in healthcare. The review highlights successful case studies, identifies common patterns in DevOps implementation, and examines the role of cultural transformation, automation, and collaboration in fostering successful DevOps practices within healthcare organizations. Additionally, this review discusses the potential benefits and limitations of applying DevOps principles in healthcare settings, offering insights for practitioners, researchers, and policymakers seeking to leverage DevOps to improve healthcare delivery.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"27 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-16DOI: 10.60087/jaigs.v2i1.p166
Imran Khan
This research article explores the role and significance of Real-Time RIC (RAN Intelligent Controller) in the context of Open RAN architecture. Open RAN represents a paradigm shift in the telecommunications industry, aiming to disaggregate and virtualize network functions to promote interoperability, flexibility, and innovation. The Real-Time RIC, as a pivotal software component within Open RAN, plays a crucial role in orchestrating and optimizing radio resources in real-time. This article delves into the functionalities, architecture, and implementation considerations of the Real-Time RIC, highlighting its capabilities in enabling dynamic network optimization, intelligent traffic steering, and efficient resource utilization. Furthermore, the article discusses the challenges and opportunities associated with deploying Real-Time RICs in diverse network environments, emphasizing the need for standardization, interoperability, and performance optimization. Through a comprehensive analysis, this research article aims to provide insights into the design, deployment, and impact of Real-Time RICs in advancing the evolution of Open RAN architectures
本文探讨了实时 RIC（RAN 智能控制器）在开放 RAN 架构中的作用和意义。开放式 RAN 代表了电信行业的模式转变，旨在分解和虚拟化网络功能，以促进互操作性、灵活性和创新性。实时 RIC 作为开放 RAN 中的关键软件组件，在实时协调和优化无线电资源方面发挥着至关重要的作用。本文深入探讨了实时 RIC 的功能、架构和实施注意事项，重点介绍了它在实现动态网络优化、智能流量引导和高效资源利用方面的功能。此外，文章还讨论了在不同网络环境中部署实时 RIC 所面临的挑战和机遇，强调了标准化、互操作性和性能优化的必要性。本文旨在通过全面分析，深入探讨实时 RIC 的设计、部署及其对推动开放 RAN 架构演进的影响。
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Pub Date : 2024-03-10DOI: 10.60087/jaigs.v2i1.p150
Amaresh Kumar
In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases. While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications
{"title":"Machine Learning Algorithms for Predictive Maintenance in Industrial Environments: A Comparative Study","authors":"Amaresh Kumar","doi":"10.60087/jaigs.v2i1.p150","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p150","url":null,"abstract":"In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases. \u0000 \u0000While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140255632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-10DOI: 10.60087/jaigs.v2i1.p110
Damián Tuset Varela
As artificial intelligence (AI) continues to permeate various aspects of society, its impact on diplomacy and international relations becomes increasingly profound. This paper explores the challenges and opportunities presented by the intersection of diplomacy and AI. It examines how AI technologies are reshaping traditional diplomatic practices, influencing decision-making processes, and altering power dynamics among nation-states. Additionally, it discusses the ethical implications and governance frameworks necessary to navigate this evolving landscape. Despite the challenges, AI offers numerous opportunities for enhancing diplomatic efforts, fostering collaboration, and addressing global challenges in a more efficient and effective manner. By understanding and harnessing the potential of AI, diplomats can adapt to the changing landscape of international relations and leverage technology to advance diplomatic objectives.
{"title":"Diplomacy in the Age of AI: Challenges and Opportunities","authors":"Damián Tuset Varela","doi":"10.60087/jaigs.v2i1.p110","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p110","url":null,"abstract":"As artificial intelligence (AI) continues to permeate various aspects of society, its impact on diplomacy and international relations becomes increasingly profound. This paper explores the challenges and opportunities presented by the intersection of diplomacy and AI. It examines how AI technologies are reshaping traditional diplomatic practices, influencing decision-making processes, and altering power dynamics among nation-states. Additionally, it discusses the ethical implications and governance frameworks necessary to navigate this evolving landscape. Despite the challenges, AI offers numerous opportunities for enhancing diplomatic efforts, fostering collaboration, and addressing global challenges in a more efficient and effective manner. By understanding and harnessing the potential of AI, diplomats can adapt to the changing landscape of international relations and leverage technology to advance diplomatic objectives.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"12 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140255348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-10DOI: 10.60087/jaigs.v2i1.p124
Damián Tuset Varela
The rise of Artificial Intelligence (AI) technology presents vast transformative possibilities across various sectors, encompassing economic, industrial, social, political, intelligence, and military realms. Consequently, governing the development and deployment of AI has garnered significant attention not only from policymakers and decision-makers but also from the general public. Given AI's potential to shape state power and its dual strategic applications, the governance of AI has become an integral part of global discussions, falling under the purview of cyber diplomacy. This article delineates key issues surrounding AI governance, discusses the evolving role of the EU as a normative force in this arena, and underscores the importance of transatlantic collaboration amid broader global technological competitions.
{"title":"Navigating Cyber Diplomacy in the Governance of Emerging AI Technologies: Lessons from Transatlantic Cooperation","authors":"Damián Tuset Varela","doi":"10.60087/jaigs.v2i1.p124","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p124","url":null,"abstract":"The rise of Artificial Intelligence (AI) technology presents vast transformative possibilities across various sectors, encompassing economic, industrial, social, political, intelligence, and military realms. Consequently, governing the development and deployment of AI has garnered significant attention not only from policymakers and decision-makers but also from the general public. Given AI's potential to shape state power and its dual strategic applications, the governance of AI has become an integral part of global discussions, falling under the purview of cyber diplomacy. This article delineates key issues surrounding AI governance, discusses the evolving role of the EU as a normative force in this arena, and underscores the importance of transatlantic collaboration amid broader global technological competitions.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"28 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140254913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-10DOI: 10.60087/jaigs.v2i1.p138
Amaresh Kumar
The deployment of autonomous vehicles (AVs) powered by artificial intelligence (AI) raises profound ethical questions regarding the balance between safety and privacy. While AI-driven AVs promise to revolutionize transportation by potentially reducing accidents and increasing efficiency, concerns regarding data privacy, liability, and decision-making algorithms persist. This paper explores the ethical considerations surrounding AI-driven AVs, focusing particularly on the delicate equilibrium required to ensure both safety and privacy. Drawing upon existing literature and case studies, the paper examines the ethical dilemmas inherent in AV technology, including issues of consent, data collection, and algorithmic bias. Additionally, it delves into the regulatory frameworks and industry standards aimed at addressing these concerns. By highlighting the complexities of navigating safety and privacy in AI-driven AVs, this research contributes to the ongoing discourse on ethical AI development and deployment.
{"title":"Exploring Ethical Considerations in AI-driven Autonomous Vehicles: Balancing Safety and Privacy","authors":"Amaresh Kumar","doi":"10.60087/jaigs.v2i1.p138","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p138","url":null,"abstract":"The deployment of autonomous vehicles (AVs) powered by artificial intelligence (AI) raises profound ethical questions regarding the balance between safety and privacy. While AI-driven AVs promise to revolutionize transportation by potentially reducing accidents and increasing efficiency, concerns regarding data privacy, liability, and decision-making algorithms persist. This paper explores the ethical considerations surrounding AI-driven AVs, focusing particularly on the delicate equilibrium required to ensure both safety and privacy. Drawing upon existing literature and case studies, the paper examines the ethical dilemmas inherent in AV technology, including issues of consent, data collection, and algorithmic bias. Additionally, it delves into the regulatory frameworks and industry standards aimed at addressing these concerns. By highlighting the complexities of navigating safety and privacy in AI-driven AVs, this research contributes to the ongoing discourse on ethical AI development and deployment.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"58 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140255021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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