LEADER 03715nam 22006375 450 001 9910741137403321 005 20251008163509.0 010 $a9781484293065 010 $a1484293061 024 7 $a10.1007/978-1-4842-9306-5 035 $a(MiAaPQ)EBC30702989 035 $a(Au-PeEL)EBL30702989 035 $a(DE-He213)978-1-4842-9306-5 035 $a(PPN)272270954 035 $a(OCoLC)1394118956 035 $a(OCoLC-P)1394118956 035 $a(CKB)27991703400041 035 $a(CaSebORM)9781484293065 035 $a(Perlego)4515912 035 $a(EXLCZ)9927991703400041 100 $a20230816d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBuilding Responsible AI Algorithms $eA Framework for Transparency, Fairness, Safety, Privacy, and Robustness /$fby Toju Duke 205 $a1st ed. 2023. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2023. 215 $a1 online resource (196 pages) 311 08$a9798868806254 311 08$a9781484293058 311 08$a1484293053 327 $aPart 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. 330 $aThis 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. 606 $aMachine learning 606 $aTechnology$xMoral and ethical aspects 606 $aArtificial intelligence 606 $aMachine Learning 606 $aEthics of Technology 606 $aArtificial Intelligence 615 0$aMachine learning. 615 0$aTechnology$xMoral and ethical aspects. 615 0$aArtificial intelligence. 615 14$aMachine Learning. 615 24$aEthics of Technology. 615 24$aArtificial Intelligence. 676 $a006.3 700 $aDuke$b Toju$01423935 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910741137403321 996 $aBuilding Responsible AI Algorithms$93552641 997 $aUNINA