logo
Product categories

EbookNice.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link.  https://ebooknice.com/page/post?id=faq


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookNice Team

(Ebook) Ethics in Artificial Intelligence: Bias, Fairness and Beyond by Animesh Mukherjee, Juhi Kulshrestha, Abhijnan Chakraborty, Srijan Kumar ISBN 9789819971831, 9819971837

  • SKU: EBN-54689108
Zoomable Image
$ 32 $ 40 (-20%)

Status:

Available

4.7

35 reviews
Instant download (eBook) Ethics in Artificial Intelligence: Bias, Fairness and Beyond after payment.
Authors:Animesh Mukherjee, Juhi Kulshrestha, Abhijnan Chakraborty, Srijan Kumar
Pages:155 pages.
Year:2024
Editon:1
Publisher:Springer
Language:english
File Size:2.65 MB
Format:pdf
ISBNS:9789819971831, 9819971837
Categories: Ebooks

Product desciption

(Ebook) Ethics in Artificial Intelligence: Bias, Fairness and Beyond by Animesh Mukherjee, Juhi Kulshrestha, Abhijnan Chakraborty, Srijan Kumar ISBN 9789819971831, 9819971837

This book is a collection of chapters in the newly developing area of ethics in artificial intelligence. The book comprises chapters written by leading experts in this area which makes it a one of its kind collections. Some key features of the book are its unique combination of chapters on both theoretical and practical aspects of integrating ethics into artificial intelligence. The book touches upon all the important concepts in this area including bias, discrimination, fairness, and interpretability. Integral components can be broadly divided into two segments – the first segment includes empirical identification of biases, discrimination, and the ethical concerns thereof in impact assessment, advertising and personalization, computational social science, and information retrieval. The second segment includes operationalizing the notions of fairness, identifying the importance of fairness in allocation, clustering and time series problems, and applications of fairness in software testing/debugging and in multi stakeholder platforms. This segment ends with a chapter on interpretability of machine learning models which is another very important and emerging topic in this area.
*Free conversion of into popular formats such as PDF, DOCX, DOC, AZW, EPUB, and MOBI after payment.

Related Products