03715nam 22006375 450 991074113740332120251008163509.09781484293065148429306110.1007/978-1-4842-9306-5(MiAaPQ)EBC30702989(Au-PeEL)EBL30702989(DE-He213)978-1-4842-9306-5(PPN)272270954(OCoLC)1394118956(OCoLC-P)1394118956(CKB)27991703400041(CaSebORM)9781484293065(Perlego)4515912(EXLCZ)992799170340004120230816d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierBuilding Responsible AI Algorithms A Framework for Transparency, Fairness, Safety, Privacy, and Robustness /by Toju Duke1st ed. 2023.Berkeley, CA :Apress :Imprint: Apress,2023.1 online resource (196 pages)9798868806254 9781484293058 1484293053 Part I. Foundation -- 1. Responsibility -- 2. AI Principles -- 3. Data -- Part II. Implementation -- 4. Fairness -- 5. Safety -- 6. Humans in the Loop -- 7. Explainability -- 8. Privacy -- 9. Robustness -- Part III. Ethical Considerations -- 10. Ethics of AI and ML -- Appendix A: References.This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts – that in some cases have caused loss of life – and develop models that are fair, transparent, safe, secure, and robust. The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers. What You Will Learn Build AI/ML models using Responsible AI frameworks and processes Document information on your datasets and improve data quality Measure fairness metrics in ML models Identify harms and risks per task and run safety evaluations on ML models Create transparent AI/ML models Develop Responsible AI principles and organizational guidelines.Machine learningTechnologyMoral and ethical aspectsArtificial intelligenceMachine LearningEthics of TechnologyArtificial IntelligenceMachine learning.TechnologyMoral and ethical aspects.Artificial intelligence.Machine Learning.Ethics of Technology.Artificial Intelligence.006.3Duke Toju1423935MiAaPQMiAaPQMiAaPQBOOK9910741137403321Building Responsible AI Algorithms3552641UNINA