About the Journal
The Journal of Knowledge Learning and Science Technology (JKLST) is a reputable, peer-reviewed academic journal that operates on an Open Access model. The journal aims to provide a comprehensive and reliable source of information on groundbreaking discoveries and the latest advancements in various areas of the field. It welcomes original research papers, review papers, case reports, short communications, and other relevant contributions.
The review process of JKLST is rigorous and ensures the highest quality of published content. Manuscripts are reviewed by the journal's editorial board members or external experts who are knowledgeable in the respective fields. The review process involves obtaining approval from at least three independent reviewers, followed by final approval from the editor. This meticulous evaluation guarantees the validity, significance, and credibility of the published work.
The editorial team of JKLST is committed to managing the entire submission, review, revision, and publication process efficiently. They work closely with authors to ensure a smooth and timely publication experience. The journal's online platform provides unrestricted access to its content, allowing researchers and scholars from around the world to benefit from valuable insights and findings without any barriers or subscription requirements.
JKLST strives to maintain its reputation as a reliable and influential platform for disseminating knowledge and promoting scholarly discussions in the fields of knowledge acquisition, learning methodologies, and science and technology. By fostering collaboration and facilitating the exchange of ideas, the journal contributes to the advancement of the scientific community and the development of innovative solutions in these domains.
Current Issue
Vol. 3 No. 3 (2024): Exploring Ethical Dilemmas in Artificial Intelligence and Machine Learning
Volume 3, Issue 3 of the Journal of Knowledge Learning and Science Technology delves into the complex terrain of ethical considerations in Artificial Intelligence (AI) and Machine Learning (ML). As AI and ML applications continue to proliferate across various sectors, from healthcare to finance, questions surrounding their ethical implications become increasingly paramount. This edition aims to explore the multifaceted ethical dilemmas arising from the adoption and deployment of AI and ML technologies. Articles in this issue will scrutinize issues such as algorithmic bias and fairness, privacy concerns in data-driven decision-making, the accountability of autonomous systems, and the societal impacts of AI-driven automation. Additionally, the issue will feature contributions on the ethical responsibilities of AI developers, the regulation of AI technologies, and the ethical frameworks guiding AI research and development. Through this comprehensive examination of ethical challenges in AI and ML, the journal seeks to foster critical dialogue, raise awareness, and provide guidance for navigating the ethical complexities inherent in the advancement of AI and ML technologies. By addressing these issues, the journal endeavors to contribute to the responsible development and deployment of AI and ML systems for the betterment of society.
Published:
25-09-2024
Copyright: © The Author(s), 2024. Published by 