國立虎尾科技大學 |

Machine Learning Risk Assessments in Criminal Justice Settings

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning Risk Assessments in Criminal Justice Settings/ by Richard Berk.
作者:	Berk, Richard.
面頁冊數:	IX, 178 p. 32 illus., 27 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-02272-3
ISBN:	9783030022723

Machine Learning Risk Assessments in Criminal Justice Settings
Berk, Richard.

Machine Learning Risk Assessments in Criminal Justice Settings[electronic resource] /by Richard Berk. - 1st ed. 2019. - IX, 178 p. 32 illus., 27 illus. in color.online resource.

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

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

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Machine Learning Risk Assessments in Criminal Justice Settings
LDR:02640nam a22003855i 4500 001 1009955
003 DE-He213
005 20200701012700.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030022723 $9 978-3-030-02272-3
024 7 $a 10.1007/978-3-030-02272-3 $2 doi
035 $a 978-3-030-02272-3
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Berk, Richard. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 891839
245 1 0 $a Machine Learning Risk Assessments in Criminal Justice Settings $h [electronic resource] / $c by Richard Berk.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a IX, 178 p. 32 illus., 27 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 Artificial intelligence. $3 559380
650 0 $a Mathematical statistics. $3 527941
650 0 $a Criminology. $3 563146
650 0 $a Research. $3 558735
650 0 $a Data mining. $3 528622
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Quantitative Criminology. $3 1197308
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030022716
776 0 8 $i Printed edition: $z 9783030022730
856 4 0 $u https://doi.org/10.1007/978-3-030-02272-3
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)