About the book
Explore the most serious prevalent ethical issues in data science with this insightful new resource
The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair.
Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to:
- Improve model transparency, even for black box models
- Diagnose bias and unfairness within models using multiple metrics
- Audit projects to ensure fairness and minimize the possibility of unintended harm
Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.
Editions of Responsible Data Science
- Paperback
- ISBN 9781119741756
- EBook
- ISBN 9781119741770
- EBook
- ISBN 9781394177479
- EBook
- ISBN 9781119741640
Read an Excerpt
Explore the most serious prevalent ethical issues in data science with this insightful new resource
The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair.
Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to:
- Improve model transparency, even for black box models
- Diagnose bias and unfairness within models using multiple metrics
- Audit projects to ensure fairness and minimize the possibility of unintended harm
Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.
Frequently Asked Questions
What is Responsible Data Science about?
Explore the most serious prevalent ethical issues in data science with this insightful new resource
The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair.
Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to:
- Improve model transparency, even for black box models
- Diagnose bias and unfairness within models using multiple metrics
- Audit projects to ensure fairness and minimize the possibility of unintended harm
Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.
What core themes, tropes, or subjects are explored in Responsible Data Science?
Computing and Information Technology > Databases / Data management > Data capture and analysis
Where can I read a sample of Responsible Data Science?
You can read an official preview of the few pages here https://www.book2look.com/book/9781119741756
Who is/are the Author/s of the book Responsible Data Science?
Grant Fleming, Peter C. Bruce
Who is the Publisher of the book Responsible Data Science?
Wiley
What are the ISBN numbers for the physical and digital editions?
Responsible Data Science is available as paperback(ISBN 9781119741756) and ebook(ISBN 9781119741770) and ebook(ISBN 9781394177479) and ebook(ISBN 9781119741640)
Where can I buy Responsible Data Science online or support local independent bookshops?
You can buy Responsible Data Science from below
Where can I buy Responsible Data Science?