Ethical data science (103 résultats)

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Paperback. Etat : new. Paperback. What Happens When the Data Is Wrong but the Decision Looks Right?Artificial intelligence can process enormous amounts of information, identify patterns, automate decisions, and support organizations in ways that were once difficult to imagine. But technical capability alone does not make a data-driven system trustworthy.What happens when an algorithm produces an unfair result? What if a dataset quietly contains historical bias? What if a model is highly accurate overall but performs poorly for a particular group? And how can decision-makers trust an analytical system when they cannot clearly understand how it reached its conclusion?Ethical Data Science provides a practical framework for addressing these questions and building more responsible data-driven systems.CONFLICT - The Problem Is Bigger Than the AlgorithmEthical problems can enter a data science project long before an algorithm is trained.They can begin with the way information is collected, the people represented in a dataset, the assumptions used during analysis, the features selected for a model, the performance measures chosen for evaluation, or the way results are communicated to decision-makers.This book examines these issues from the perspective of the complete data science lifecycle.You will explore responsible data collection, data quality, privacy protection, ethical governance, bias detection, fairness evaluation, responsible AI development, human oversight, explainable analytics, transparent reporting, machine learning evaluation, security, compliance, organizational implementation, and continuous improvement.STAKES - Why This Book MattersA technically impressive model can still create serious problems when ethical considerations are overlooked.Ethical Data Science shows readers how to: Identify common sources and types of data bias.Examine datasets for representation and quality problems.Apply practical bias mitigation strategies.Evaluate fairness across different data groups.Build responsible AI systems with meaningful human oversight.Assess AI risks, errors, and unintended outcomes.Monitor AI systems after deployment.Improve explainability and transparent analytical reporting.Document data sources, methods, assumptions, and limitations.Protect sensitive information throughout the data lifecycle.Establish responsible data governance policies and standards.Balance accuracy, fairness, transparency, and practical requirements.Create secure and responsible data workflows.Develop multidisciplinary teams for ethical analytics.Prepare organizations for emerging AI technologies and changing expectations.Rather than treating ethics as a final checklist, the book presents responsible data science as an ongoing discipline that should influence planning, development, deployment, evaluation, governance, and long-term system management.It is designed for data professionals, analysts, AI practitioners, technology managers, business leaders, students, and professionals who need a practical understanding of how responsible principles can be incorporated into real-world analytical work.CALL TO ACTIONThe future of artificial intelligence will not be determined only by how powerful our models become. It will also depend on how responsibly those systems are designed, evaluated, governed, and used.If you want to develop stronger judgment around bias, fairness, privacy, transparency, accountability, and responsible AI, Ethical Data Science gives you a practical foundation for doing so.Buy your copy today and start building data-driven systems that are not only capable, but responsible, explainable, and worthy of t Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Etat : New. As artificial intelligence (AI) permeates day-to-day life-influencing who is granted what type of healthcare, determining who is granted an employment interview or academic enrollment, or delivering what information to whom-it frames individuals and society. AI has far-reaching powers to improve lives, but it is not without risks. Owing to its limitations, AI may prevent a person from receiving needed healthcare, block a deserving candidate from employment, or present fake and inaccurate information. How to trust that AI systems will improve lives and not create greater inequality and strife? How to foster the benefits of AI and mitigate the harms from AI? Ethical AI and Data Science: Building Trustworthy and Transparent Systems answers these questions.A vital and ongoing dialogue surrounding data, accessibility, and the multifaceted challenges posed by AI in ethics, safety, and societal impact, the book features diverse perspectives and practical approaches written by a panel of experts. Highlights of the book include:Ensuring safety in agentic AI through decision traceabilityPromoting responsible and ethical AI in financial servicesBridging human-machine knowledge for responsible AIResponsible agentic AI for effective cybersecurity and ethical safetyThe book offers valuable insights into navigating the complexities of developing AI systems that are not only innovative but also transparent, equitable, and secure. This volume aims to advance understanding and foster collaboration among researchers, practitioners, and policymakers, helping to shape AI technologies that responsibly serve society while addressing critical concerns around fairness, accountability, and human well-being.…

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Paperback. Etat : New. As artificial intelligence (AI) permeates day-to-day life-influencing who is granted what type of healthcare, determining who is granted an employment interview or academic enrollment, or delivering what information to whom-it frames individuals and society. AI has far-reaching powers to improve lives, but it is not without risks. Owing to its limitations, AI may prevent a person from receiving needed healthcare, block a deserving candidate from employment, or present fake and inaccurate information. How to trust that AI systems will improve lives and not create greater inequality and strife? How to foster the benefits of AI and mitigate the harms from AI? Ethical AI and Data Science: Building Trustworthy and Transparent Systems answers these questions.A vital and ongoing dialogue surrounding data, accessibility, and the multifaceted challenges posed by AI in ethics, safety, and societal impact, the book features diverse perspectives and practical approaches written by a panel of experts. Highlights of the book include:Ensuring safety in agentic AI through decision traceabilityPromoting responsible and ethical AI in financial servicesBridging human-machine knowledge for responsible AIResponsible agentic AI for effective cybersecurity and ethical safetyThe book offers valuable insights into navigating the complexities of developing AI systems that are not only innovative but also transparent, equitable, and secure. This volume aims to advance understanding and foster collaboration among researchers, practitioners, and policymakers, helping to shape AI technologies that responsibly serve society while addressing critical concerns around fairness, accountability, and human well-being.…

Etat : New. In English.

Etat : New. GRANT FLEMING is a Data Scientist at Elder Research Inc. His professional focus is on machine learning for social science applications, model interpretability, civic technology, and building software tools for reproducible data science.PETER BRUCE is the Se.

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