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Machine learning risk assessments in...

Berk, Richard.

Machine learning risk assessments in criminal justice settings

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning risk assessments in criminal justice settingsby Richard Berk.
Author:	Berk, Richard.
Published:	Cham :Springer International Publishing :2019.
Description:	ix, 178 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-02272-3
ISBN:	9783030022723$q(electronic bk.)

Machine learning risk assessments in criminal justice settings
Berk, Richard.

Machine learning risk assessments in criminal justice settings[electronic resource] /by Richard Berk. - Cham :Springer International Publishing :2019. - ix, 178 p. :ill., digital ;24 cm.

1 Getting Started -- 2 Some Important Background Material -- 3 A Conceptual Introduction Classification and Forecasting -- 4 A More Formal Treatment of Classification and Forecasting -- 5 Tree-Based Forecasting Methods -- 6 Transparency, Accuracy and Fairness -- 7 Real Applications -- 8 Implementation -- 9 Some Concluding Observations About Actuarial Justice and More.

This book puts in one place and in accessible form Richard Berk's most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk. Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than "predictive policing" for locations in time and space, which is a very different enterprise that uses different data different data analysis tools. The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations.

ISBN: 9783030022723$q(electronic bk.)

Standard No.: 10.1007/978-3-030-02272-3doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning risk assessments in criminal justice settings
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245 1 0 $a Machine learning risk assessments in criminal justice settings $h [electronic resource] / $c by Richard Berk.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a ix, 178 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1 Getting Started -- 2 Some Important Background Material -- 3 A Conceptual Introduction Classification and Forecasting -- 4 A More Formal Treatment of Classification and Forecasting -- 5 Tree-Based Forecasting Methods -- 6 Transparency, Accuracy and Fairness -- 7 Real Applications -- 8 Implementation -- 9 Some Concluding Observations About Actuarial Justice and More.
520 $a This book puts in one place and in accessible form Richard Berk's most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk. Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than "predictive policing" for locations in time and space, which is a very different enterprise that uses different data different data analysis tools. The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations.
650 0 $a Machine learning. $3 188639
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Probability and Statistics in Computer Science. $3 274053
650 2 4 $a Quantitative Criminology. $3 797354
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-3-030-02272-3
950 $a Computer Science (Springer-11645)