Responsible Data Science
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Form:Einzelkauf Download
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Sprache:Englisch
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eBook Format:ePUB 3
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25,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
21.04.2021
Verlag
John Wiley & SonsSeitenzahl
304 (Printausgabe)
Dateigröße
25502 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9781119741640
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.
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.
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