Regulating Algorithmic Assemblages: Exploring Beyond Corporate AI Ethics DOI Creative Commons
Md.Mafiqul Islam

Journal of Knowledge Learning and Science Technology ISSN 2959-6386 (online), Journal Year: 2024, Volume and Issue: 3(3), P. 9 - 28

Published: May 9, 2024

The rapid advancement of artificial intelligence (AI) systems, fueled by extensive research and development investments, has ushered in a new era where AI permeates decision-making processes across various sectors. This proliferation is largely attributed to the availability vast digital datasets, particularly machine learning, enabling systems discern intricate correlations furnish valuable insights from data on human behavior other phenomena. However, widespread integration into private public domains raised concerns regarding neutrality objectivity automated processes. Such despite their technological sophistication, are not immune biases ethical dilemmas inherent judgments. Consequently, there growing call for regulatory oversight ensure transparency accountability deployment, akin traditional frameworks governing analogous paper critically examines implications ripple effects incorporating existing social an 'AI ethics' standpoint. It questions adequacy self-policing mechanisms advocated corporate entities, highlighting limitations responsibility paradigms. Additionally, it scrutinizes well-intentioned initiatives, such as EU ethics initiative, which may overlook broader societal impacts while prioritizing desirability applications. discussion underscores necessity adopting holistic approach that transcends individual group rights considerations address profound AI, encapsulated concept 'algorithmic assemblage'.

Language: Английский

AI-Driven Cloud Security: The Future of Safeguarding Sensitive Data in the Digital Age DOI Creative Commons

Hassan Rehan

Deleted Journal, Journal Year: 2024, Volume and Issue: 1(1), P. 47 - 66

Published: Jan. 22, 2024

As organizations increasingly rely on cloud computing for storage, processing, and deployment of sensitive data, ensuring robust security measures becomes paramount. This paper explores the intersection artificial intelligence (AI) security, presenting AI-driven solutions as future safeguarding data in digital age. Leveraging AI algorithms machine learning techniques, can adapt evolve to counter emerging threats real-time, enhancing detection, prevention, response capabilities. discusses various approaches including anomaly threat analysis, behavior analytics, highlighting their effectiveness mitigating risks compliance with regulatory standards. Additionally, it addresses challenges ethical considerations associated emphasizing importance transparency, accountability, principles. By embracing solutions, fortify defenses against cyber maintain integrity confidentiality evolving landscape.

Language: Английский

Citations

33
AI-Driven Cloud Security: The Future of Safeguarding Sensitive Data in the Digital Age DOI Creative Commons

Hassan Rehan

Deleted Journal, Journal Year: 2024, Volume and Issue: 1(1), P. 132 - 151

Published: Jan. 22, 2024

As organizations increasingly rely on cloud computing for storage, processing, and deployment of sensitive data, ensuring robust security measures becomes paramount. This paper explores the intersection artificial intelligence (AI) security, presenting AI-driven solutions as future safeguarding data in digital age. Leveraging AI algorithms machine learning techniques, can adapt evolve to counter emerging threats real-time, enhancing detection, prevention, response capabilities. discusses various approaches including anomaly threat analysis, behavior analytics, highlighting their effectiveness mitigating risks compliance with regulatory standards. Additionally, it addresses challenges ethical considerations associated emphasizing importance transparency, accountability, principles. By embracing solutions, fortify defenses against cyber maintain integrity confidentiality evolving landscape.

Language: Английский

Citations

12
Revolutionizing Regulatory Reporting through AI/ML: Approaches for Enhanced Compliance and Efficiency DOI Creative Commons
Harish Padmanaban

Deleted Journal, Journal Year: 2024, Volume and Issue: 2(1), P. 71 - 90

Published: Feb. 27, 2024

ISSN: 3006-4023 (Online), Vol. 2, Issue 1Journal of Artificial Intelligence General Science (JAIGS)journal homepage: https://ojs.boulibrary.com/index.php/JAIGSRevolutionizing Regulatory Reporting through AI/ML: Approaches forEnhanced Compliance and EfficiencyHarish Padmanaban Ph.D.Site Reliability Engineering lead Independent Researcher.AbstractIn the intricate regulatory landscape today, financial institutions encounter formidable hurdles in meeting reportingmandates while upholding operational efficacy. This study delves into transformative capacity ArtificialIntelligence (AI) Machine Learning (ML) technologies refining reporting procedures. Throughharnessing AI/ML, entities can streamline data aggregation, analysis, submission, thus fostering enhancedcompliance efficiency. Key strategies for integrating AI/ML frameworksare discussed, encompassing standardization, predictive analytics, anomaly detection, automation.Furthermore, paper explores advantages, obstacles, optimal approaches associated with deploying AI/MLsolutions reporting. Drawing on real-world illustrations case studies, this offers insights intohow redefine practices, empowering to adeptlynavigate intricacies optimizing resource allocation decision-making processes.

Language: Английский

Citations

11
Fostering Privacy in Collaborative Data Sharing via Auto-encoder Latent Space Embedding DOI Creative Commons

Vinayak Raja,

Bhuvi Chopra

Deleted Journal, Journal Year: 2024, Volume and Issue: 4(1), P. 152 - 162

Published: May 13, 2024

Securing privacy in machine learning via collaborative data sharing is essential for organizations seeking to harness collective while upholding confidentiality. This becomes especially vital when protecting sensitive information across the entire pipeline, from model training inference. paper presents an innovative framework utilizing Representation Learning autoencoders generate privacy-preserving embedded data. As a result, can distribute these representations, enhancing performance of models situations where multiple sources converge unified predictive task downstream.

Language: Английский

Citations

10
Electric Vehicle (EV) Review: Bibliometric Analysis of Electric Vehicle Trend, Policy, Lithium-Ion Battery, Battery Management, Charging Infrastructure, Smart Charging, and Electric Vehicle-to-Everything (V2X) DOI Creative Commons
Ibham Veza,

Mohd Syaifuddin,

Muhammad Idris

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(15), P. 3786 - 3786

Published: July 31, 2024

Electric vehicles (EVs) have seen significant growth due to the increasing awareness about environmental concerns and negative impacts of internal combustion engine (ICEVs). The electric vehicle landscape is rapidly evolving, with EV policies, battery, charging infrastructure vehicle-to-everything (V2X) at its forefront. This review study used a bibliometric analysis Scopus database investigate development technology. specifically focuses on analyzing trends, policy implications, lithium-ion batteries, battery management systems, infrastructure, smart technologies, V2X. Through this detailed discussion, we aim provide better understanding holistic technology inspire further research in vehicles. covers period from 1990 2022. underscores interplay technology, focusing developments possibility V2X In addition, suggests synchronization international policy, advancement promotion use systems. emphasizes that expansion EVs sustainable mobility relies comprehensive strategy encompasses infrastructure. recommends fostering collaboration between different sectors drive innovation advancements

Language: Английский

Citations

10
Exploring Ethical Considerations in AI-driven Autonomous Vehicles: Balancing Safety and Privacy DOI Creative Commons

Amaresh Kumar

Deleted Journal, Journal Year: 2024, Volume and Issue: 2(1), P. 125 - 138

Published: March 10, 2024

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 potentially reducing accidents increasing efficiency, concerns data privacy, liability, decision-making algorithms persist. This paper explores considerations surrounding AVs, focusing particularly on delicate equilibrium required ensure both Drawing upon existing literature case studies, examines dilemmas inherent in AV technology, including issues consent, collection, algorithmic bias. Additionally, it delves into regulatory frameworks industry standards aimed at addressing these concerns. By highlighting complexities navigating privacy this research contributes ongoing discourse AI development deployment.

Language: Английский

Citations

8
Cybersecurity Threat Detection using Machine Learning and Network Analysis DOI Creative Commons

Amaresh Kumar

Deleted Journal, Journal Year: 2024, Volume and Issue: 1(1), P. 38 - 46

Published: Jan. 22, 2024

Cybercriminals continually develop innovative strategies to confound and frustrate their victims, necessitating constant vigilance protect the availability, confidentiality, integrity of digital systems. Machine learning (ML) has emerged as a powerful technique for intelligent cyber analysis, enabling proactive defenses by studying recurring patterns successful attacks. However, two significant drawbacks hinder widespread adoption ML in security analysis: high computing overheads need specialized frameworks. This study aims quantify extent which hub can enhance ecosystem safety. Typical cyberattacks were executed on an Internet Things (IoT) network within smart house validate hub's efficacy. Furthermore, resistance intrusion detection system (IDS) adversarial machine (AML) attacks was investigated, where models are targeted with samples exploiting weaknesses pre-trained detector.

Language: Английский

Citations

5
Exploring the Impact of Artificial Intelligence in Healthcare DOI Creative Commons

Md. Mafiqul Islam

Deleted Journal, Journal Year: 2024, Volume and Issue: 2(1), P. 171 - 188

Published: March 22, 2024

The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, research drug discovery, engagement compliance, rehabilitation, administrative applications. AI's impact is observed across various domains, detecting clinical conditions imaging, early diagnosis coronavirus disease 2019 (COVID-19), care utilizing AI-powered tools, electronic health record management, enhancing treatment reducing burdens for healthcare professionals (HCPs), vaccine identification prescription errors, extensive data storage analysis, technology-assisted rehabilitation. However, encounters several technical, ethical, social challenges, such as privacy concerns, safety issues, autonomy consent, cost considerations, information transparency, access disparities, efficacy uncertainties. Effective governance imperative ensure safety, accountability, bolster HCPs' confidence, thus fostering acceptance yielding significant benefits. Precise essential address regulatory, trust concerns while advancing adoption implementation With onset COVID-19 pandemic, sparked revolution, signaling promising leap forward meet future demands.

Language: Английский

Citations

5
Examining Ethical Aspects of AI: Addressing Bias and Equity in the Discipline DOI Creative Commons

Jeff Shuford

Deleted Journal, Journal Year: 2024, Volume and Issue: 3(1), P. 262 - 280

Published: April 7, 2024

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness bias embedded within AI systems. This is particularly crucial sectors like healthcare, employment, criminal justice, credit scoring, emerging field of generative models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes perpetuate existing inequalities, including biases ingrained data representation individuals.This survey paper provides a concise yet comprehensive examination AI, encompassing their origins, ramifications, potential mitigation strategies. We scrutinize sources bias, data, algorithmic, human decision biases, shedding light on emergent issue where may replicate amplify societal stereotypes. Assessing impact biased systems, we spotlight perpetuation inequalities reinforcement harmful stereotypes, especially gains traction shaping public perception through generated content.Various proposed strategies are explored, with an emphasis ethical considerations surrounding implementation. stress necessity interdisciplinary collaboration ensure effectiveness these Through systematic literature review spanning multiple academic disciplines, define its types, delving into nuances bias. discuss adverse effects individuals society, providing overview current approaches mitigate preprocessing, model selection, post-processing. Unique challenges posed by highlighted, underscoring importance tailored address them effectively.Addressing necessitates holistic approach, involving diverse representative datasets, enhanced transparency, accountability exploration alternative paradigms prioritizing considerations. contributes ongoing discourse developing fair unbiased outlining sources, impacts, related particular focus burgeoning AI.

Language: Английский

Citations

3
Assessing Technological-Driven Challenges and Policies Associated with Electric Vehicle (EV) Adoption DOI

A Ghani,

Norulsamani Abdullah,

B. Kalidasan

et al.

Transport Policy, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0