國立虎尾科技大學 |

Building responsible AI algorithms = a framework for transparency, fairness, safety, privacy, and robustness /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Building responsible AI algorithms/ by Toju Duke.
Reminder of title:	a framework for transparency, fairness, safety, privacy, and robustness /
Author:	Duke, Toju.
Published:	Berkeley, CA :Apress : : 2023.,
Description:	xvii, 190 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Moral and ethical aspects. -
Online resource:	https://doi.org/10.1007/978-1-4842-9306-5
ISBN:	9781484293065

Building responsible AI algorithms = a framework for transparency, fairness, safety, privacy, and robustness /
Duke, Toju.

Building responsible AI algorithmsa framework for transparency, fairness, safety, privacy, and robustness /[electronic resource] :by Toju Duke. - Berkeley, CA :Apress :2023. - xvii, 190 p. :ill., digital ;24 cm.

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.

ISBN: 9781484293065

Standard No.: 10.1007/978-1-4842-9306-5doiSubjects--Topical Terms:

796961
Artificial intelligence
--Moral and ethical aspects.

LC Class. No.: Q334.7

Dewey Class. No.: 174.90063

Building responsible AI algorithms = a framework for transparency, fairness, safety, privacy, and robustness /
LDR:02863nam a2200325 a 4500 001 1116461
003 DE-He213
005 20230816154218.0
006 m d
007 cr nn 008maaau
008 240123s2023 cau s 0 eng d
020 $a 9781484293065 $q (electronic bk.)
020 $a 9781484293058 $q (paper)
024 7 $a 10.1007/978-1-4842-9306-5 $2 doi
035 $a 978-1-4842-9306-5
040 $a GP $c GP
041 0 $a eng
050 4 $a Q334.7
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 174.90063 $2 23
090 $a Q334.7 $b .D877 2023
100 1 $a Duke, Toju. $3 1429763
245 1 0 $a Building responsible AI algorithms $h [electronic resource] : $b a framework for transparency, fairness, safety, privacy, and robustness / $c by Toju Duke.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2023.
300 $a xvii, 190 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Artificial intelligence $x Moral and ethical aspects. $3 796961
650 0 $a Machine learning $x Moral and ethical aspects. $3 1429764
650 1 4 $a Machine Learning. $3 1137723
650 2 4 $a Ethics of Technology. $3 1387804
650 2 4 $a Artificial Intelligence. $3 646849
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-9306-5
950 $a Professional and Applied Computing (SpringerNature-12059)