English
Beginner
Artificial Intelligence (AI)
Real World AI Ethics for Data Scientists: Practical Case Studies
Nachshon (Sean) Goltz,Tracey Dowdeswell
4.5 / 5
0 reviews
2023
Published
143
pages
447
views
In the midst of the fourth industrial revolution, big data is weighed in gold, placing enormous power in the hands of data scientists – the modern AI alchemists. But great power comes with greater responsibility. This book seeks to shape, in a practical, diverse, and inclusive wa
About this book
In the midst of the fourth industrial revolution, big data is weighed in gold, placing enormous power in the hands of data scientists – the modern AI alchemists. But great power comes with greater responsibility. This book seeks to shape, in a practical, diverse, and inclusive way, the ethical compass of those entrusted with big data.Being practical, this book provides seven real-world case studies dealing with big data abuse. These cases span a range of topics from the statistical manipulation of research in the Cornell food lab through the Facebook user data abuse done by Cambridge Analytica to the abuse of farm animals by AI in a chapter co-authored by renowned philosophers Peter Singer and Yip Fai Tse. Diverse and inclusive, given the global nature of this revolution, this book provides case-by-case commentary on the cases by scholars representing non-Western ethical approaches (Buddhist, Jewish, Indigenous, and African) as well as Western approaches (consequentialism, deontology, and virtue).We hope this book will be a lighthouse for those debating ethical dilemmas in this challenging and ever-evolving field.
Ask this book
Your question is answered in the context of this title and author. Each answer uses 2 points.
Sign in to ask the book assistant.
Reader reviews
0 reviews · 4.5 average out of 5
No reviews yet
If you have read this book, help the next reader with your experience.
Write a review
Sign in to publish a review.
Reader questions and answers
Ask a focused question and learn from the community.
Sign in to ask or answer a question.
No questions yet
Be the first to ask a clear, useful question.
What to read next
Related references that continue this learning path.
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Alice Zheng,Amanda Casari
2018
View book