Ethical Issues In Ai For Bioinformatics And Chemoinformatics

This unique volume presents AI in relation to ethical points of view in handling big data sets. Issues such as algorithmic biases, discrimination for specific patterns and privacy breaches may sometimes be skewed to affect research results so that certain fields to appear more appealing to funding agencies. The discussion on the ethics of AI is highly complex due to the involvement of many international stakeholders such as the UN, OECD, parliaments, industry groups, professional bodies, and individual companies. The issue of reliability is addressed including the emergence of synthetic life, 5G networks, intermingling of human artificial intelligence, nano-robots and cyber security tools. Features Discusses artificial intelligence and ethics, the challenges and opportunities Presents the issue of reliability in the emergence of synthetic life, 5G networks, intermingling of human artificial intelligence, nano-robots, and cyber security tools Ethical responsibility and reasoning for using AI in Big Data Addresses practicing medicine and ethical issues when applying artificial intelligence

Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists’ ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.

The healthcare industry is grappling with numerous challenges, including rising costs, inefficiencies in service delivery, and the need for personalized treatment approaches. Traditional healthcare management and delivery methods must be improved in addressing these issues, leading to a growing demand for innovative solutions. Additionally, the exponential growth of medical data and the complexity of biomedical research and biotechnology presents a daunting challenge in harnessing this data effectively for improved patient care and medical advancements. There is a pressing need for a comprehensive understanding of how artificial intelligence (AI) can be leveraged to tackle these challenges and drive meaningful change in the healthcare sector. Future of AI in Biomedicine and Biotechnology offers a timely and insightful solution to the challenges faced by the healthcare industry. This book is not just a theoretical exploration; it is a practical roadmap for healthcare professionals, researchers, policymakers, and entrepreneurs seeking to navigate the complexities of AI in healthcare. By exploring the intersection of AI with biomedical sciences and biotechnology, this book provides a comprehensive guide to harnessing the power of AI for transformative healthcare innovation.

Increasingly, technology, the Internet and social media are playing a major part in our lives. What should we think about the ethical issues that arise, such as the changing role of intelligent machines in this Information Age? The impact of technology upon society is a perennial question, but the power of computing and artificial intelligence has ratcheted up the ethical implications of this relationship. It merits careful consideration. Ethics in the AI, Technology, and Information Age brings together a cohort of international scholars to explore the ethical ramifications of the latest technologies and their effects on our lives. This it does in three parts: (1) theoretical considerations, (2) practical applications, and (3) challenges. Beginning with theoretical essays, the book investigates the relationship between technology and nature, the limits of being “human” versus “machine,” and the moral implications of artificial intelligence. The book then examines key questions; such as ownership of technology, artificial intelligence’s replacement of human jobs and functions, privacy and cybersecurity, the ethics of self-driving cars, and the problematic aspects of drone warfare. With an appendix of films and documentaries to inspire further discussion on these topics, students and scholars will find Ethics in the AI, Technology, and Information Age an essential and engaging resource both in the classroom and in their daily technology-filled lives.

Artificial intelligence (AI) is beginning to appear in everything from writing, social media, and business to wartime or intelligence strategy. With so many applications in our everyday lives and in the systems that run them, many are demanding that ethical implications are considered before any one application of AI goes too far and causes irreparable damage to the personal data or operations of individuals, governments, and organizations. For instance, AI that is fed data sets that are influenced by human data collection method biases may be perpetuating societal biases with implicit bias that can create serious consequences. Applications of AI with implicit bias on recidivism prediction models as well as medical algorithms have shown biases against certain racial or ethnic groups, leading to actual discrimination in treatment by the legal system and the medical systems. Regulatory groups may identify the bias in AI but not the source of the bias, making it difficult to determine who to hold accountable. Lack of dataset and programming transparency can be problematic when AI systems are used to make significant decisions, and as AI systems become more advanced, questions arise regarding responsibility for the results of their implementation and the regulation thereof. Research on how these applications of AI are affecting interpersonal and societal relationships is important for informing much-needed regulatory policies. Investigating the Impact of AI on Ethics and Spirituality focuses on the spiritual implications of AI and its increasing presence in society. As AI technology advances, it raises fundamental questions about our spiritual relationship with technology. This study emphasizes the need to examine the ethical considerations of AI through a spiritual lens and to consider how spiritual principles can inform its development and use. This book covers topics such as data collection, ethical issues, and AI and is ideal for educators, teacher trainees, policymakers, academicians, researchers, curriculum developers, higher-level students, social activists, and government officials.

In the advancing fields of artificial intelligence (AI) and data science, a pressing ethical dilemma arises. As technology continues its relentless march forward, ethical considerations within these domains become increasingly complex and critical. Bias in algorithms, lack of transparency, data privacy breaches, and the broader societal repercussions of AI applications are demanding urgent attention. This ethical quandary poses a formidable challenge for researchers, academics, and industry professionals alike, threatening the very foundation of responsible technological innovation. Navigating this ethical minefield requires a comprehensive understanding of the multifaceted issues at hand. The Ethical Frontier of AI and Data Analysis is an indispensable resource crafted to address the ethical challenges that define the future of AI and data science. Researchers and academics who find themselves at the forefront of this challenge are grappling with the evolving landscape of AI and data science ethics. Underscoring the need for this book is the current lack of clarity on ethical frameworks, bias mitigation strategies, and the broader societal implications, which hinder progress and leave a void in the discourse. As the demand for responsible AI solutions intensifies, the imperative for this reliable guide that consolidates, explores, and advances the dialogue on ethical considerations grows exponentially.

The Ethics of Artificial Intelligence in Education identifies and confronts key ethical issues generated over years of AI research, development, and deployment in learning contexts. Adaptive, automated, and data-driven education systems are increasingly being implemented in universities, schools, and corporate training worldwide, but the ethical consequences of engaging with these technologies remain unexplored. Featuring expert perspectives from inside and outside the AIED scholarly community, this book provides AI researchers, learning scientists, educational technologists, and others with questions, frameworks, guidelines, policies, and regulations to ensure the positive impact of artificial intelligence in learning.

The development of large language models has resulted in artificial intelligence advancements promising transformations and benefits across various industries and sectors. However, this progress is not without its challenges. The scale and complexity of these models pose significant technical hurdles, including issues related to bias, transparency, and data privacy. As these models integrate into decision-making processes, ethical concerns about their societal impact, such as potential job displacement or harmful stereotype reinforcement, become more urgent. Addressing these challenges requires a collaborative effort from business owners, computer engineers, policymakers, and sociologists. Fostering effective research for solutions to address AI ethical challenges may ensure that large language model developments benefit society in a positive way. Challenges in Large Language Model Development and AI Ethics addresses complex ethical dilemmas and challenges of the development of large language models and artificial intelligence. It analyzes ethical considerations involved in the design and implementation of large language models, while exploring aspects like bias, accountability, privacy, and social impacts. This book covers topics such as law and policy, model architecture, and machine learning, and is a useful resource for computer engineers, sociologists, policymakers, business owners, academicians, researchers, and scientists.

This book covers a range of topics on exploiting Nigeria’s mega biodiversity for food security and health; DNA forensic science and its applications; medical biotechnology and biopharmaceutics; medicinal and underutilized plants; impact and mitigation of antibiotic resistance; bioinformatics applications; medical insect biotechnology; etc. The book will be useful reference material for the scientists and researchers working in the fields of nutraceuticals, molecular diagnostics and DNA forensics, biopharmaceuticals and medical biotechnology, nanotechnology, antimicrobials from indigenous plant species, bioinformatics, etc. Emphasizes recent advances in biotechnologies that will help in tackling emerging global health challenges Provides detailed information on how to harness indigenous bioresources including microorganisms and plants for healthcare delivery Introduces new frontiers in the areas of molecular diagnostics and DNA forensic science and bioinformatics with case studies, recent advances in medical insect biotechnology and molecular genetics of pest use towards the exploitation of arthropod midgut components to develop interventions against infectious diseases Reviews bioactive molecules derived from commonly used and underutilized medicinal plants that could be used to develop novel drugs for improved healthcare delivery Discusses current approaches in medical and biopharmaceutical biotechnology, deployment of inexpensive genomics-based vector surveillance for effective disease outbreak prediction and control of mosquito-borne viruses Hajiya Mairo Inuwa, Ph.D., is Professor in the Department of Biochemistry and Formerly Director, Centre for Biotechnology Research and Training (CBR&T), Ahmadu Bello University, Zaria, Nigeria. Ifeoma Maureen Ezeonu, Ph.D., is Professor of Medical Microbiology and Molecular Genetics in the Department of Microbiology, University of Nigeria, Nsukka, Nigeria. Charles Oluwaseun Adetunji, Ph.D., is Associate Professor of Microbiology and Biotechnology and Director of Intellectual Property and Technology Transfer, Edo State University, Uzairue, Nigeria. Abubakar Gidado, Ph.D., is Professor of Biochemistry and Director of North-East Zonal Biotechnology Centre of Excellence at the University of Maiduguri. Emmanuel Olufemi Ekundayo, Ph.D., is Associate Professor of Medical Microbiology and Microbial Genetics, Michael Okpara University of Agriculture, Umudike, Nigeria. Abdulrazak B. Ibrahim, Ph.D., is a Capacity Development Expert at the Forum for Agricultural Research in Africa (FARA) and Associate Professor of Biochemistry, Ahmadu Bello University, Zaria, Nigeria. Benjamin Ewa Ubi, Ph.D., is a Professor of Plant Breeding and Biotechnology and Director, Biotechnology Research and Development Centre, Ebonyi State University, Abakaliki, Nigeria.

Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences brings together two very important fields in pharmaceutical sciences that have been mostly seen as diverging from each other: chemoinformatics and bioinformatics. As developing drugs is an expensive and lengthy process, technology can improve the cost, efficiency and speed at which new drugs can be discovered and tested. This book presents some of the growing advancements of technology in the field of drug development and how the computational approaches explained here can reduce the financial and experimental burden of the drug discovery process. This book will be useful to pharmaceutical science researchers and students who need basic knowledge of computational techniques relevant to their projects. Bioscientists, bioinformaticians, computational scientists, and other stakeholders from industry and academia will also find this book helpful. Provides practical information on how to choose and use appropriate computational tools Presents the wide, intersecting fields of chemo-bio-informatics in an easily-accessible format Explores the fundamentals of the emerging field of chemoinformatics and bioinformatics