Ethical Data Use
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Ethical Data Use
In an era dominated by data, "Ethical Data Use" delves into the critical intersection of politics and technology, offering a framework for responsible innovation. It tackles the pressing need for ethical guidelines in a world where data collection frequently surpasses both regulation and ethical considerations. The book focuses on key areas such as data gathering practices, informed consent, and ethical platform design, highlighting how biases can creep into algorithms and potentially violate individual privacy. The book argues that ethical data use isn't just about compliance; it's essential for building trustworthy technological systems. It begins by laying out the core concepts of data ethics, then explores challenges in data gathering and platform design, using case studies to illustrate ethical and unethical practices. By integrating ethical theory with practical guidance, "Ethical Data Use" provides actionable strategies for policymakers, technologists, and anyone interested in fostering a more ethical data ecosystem, emphasizing the importance of data governance.
Artificial Intelligence Ethics
Artificial intelligence (AI) has permeated every aspect of life. Like every other technology, AI poses risk and raises questions on ethics related to its design, development, deployment, use, and retirement. While a completely ethical AI may not be possible to achieve, it is possible to assess the maturity of the ethics of certain AI-based system, or that of an organization that employs AI. This book presents a comprehensive framework designed to guide organizations in assessing and enhancing the ethical maturity of their AI systems. It provides a structured approach to evaluating AI ethics across multiple dimensions, including governance, transparency, accountability, fairness, and privacy. By using this framework, organizations can identify areas of strength and opportunities for improvement, enabling them to develop AI systems that are not only technically robust but also ethically sound. This book is just the beginning of a whole new domain of AI ethics maturity assessment in which the author plans to establish a certification body for certifying systems and organizations on the maturity of their AI ethics. The author may be approached for partnership in this regard at [email protected].
AI in Business Analytics and Decision-Making
Author: S. Mohan kumar
language: en
Publisher: Jupiter Publications Consortium
Release Date: 2025-11-12
AI in Business: Analytics and Decision-Making provides a comprehensive and forward-looking examination of how artificial intelligence, machine learning, and advanced analytics are transforming strategic decision-making and organizational performance. Edited by S. Mohan Kumar, G. Balakrishnan, K. P. Yadav, and Joseph Bamidele Awotunde, this volume brings together multidisciplinary perspectives and research contributions from global scholars and practitioners. Positioned at the intersection of data science, management strategy, and digital transformation, the book explores the full spectrum of AI-driven business analytics—from predictive and prescriptive modeling to real-time decision intelligence. It covers topics such as sentiment-based e-commerce marketing, organizational readiness for AI adoption, big-data-driven behavioral targeting, chatbot-enabled commerce, IoT-integrated quality monitoring, AI-accelerated strategic leadership, and workforce transformation through AI literacy. Each chapter blends conceptual depth with empirical evidence, offering case studies, methodological reviews, and practical decision frameworks. A recurring theme throughout the volume is the strategic translation of AI capabilities into measurable business value. Readers will find detailed discussions on data governance, model interpretability, ethical AI, privacy considerations, enterprise change management, and the dynamics of human–AI collaboration. The book also examines emerging technologies—including large language models, multimodal analytics, reinforcement learning, and edge AI—highlighting their implications for the future of intelligent business ecosystems. Designed for executives, data analytics leaders, researchers, policymakers, and graduate students, this open-access publication serves as both a scholarly reference and a strategic guide. Its synthesis of research and practice equips organizations to build AI-enabled capabilities, accelerate decision-making, and achieve competitive resilience in an increasingly data-driven world. Key Themes: Artificial Intelligence; Machine Learning; Predictive and Prescriptive Analytics; Business Intelligence; Digital Transformation; Organizational Change; Ethical AI; Decision-Making Science; Customer Insights; IoT and Edge AI; Workforce Readiness. License: Open Access under the Creative Commons Attribution 4.0 International license.