Titlebook: Machine Learning Risk Assessments in Criminal Justice Settings; Richard Berk Book 2019 Springer Nature Switzerland AG 2019 Risk Assessment

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https://doi.org/10.1007/978-3-030-02272-3Risk Assessment; machine learning; risk forecasting; criminal justice; future dangerousness; fair algorit

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Real Applications,applications that led to procedures adopted by criminal justice agencies. As such, they combine a number of technical matters with the practical realities of criminal justice decisions-making. For the reasons already addressed, random forests will be the machine learning method of choice with one exception near the end of the chapter.

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Implementation,iefly to porting new forecasting procedures to the settings in which they will be used. There are technical issues, but often the major obstacles are interpersonal and organizational. Implementation can be the most challenging and time consuming step that must be anticipated as the risk algorithm is being developed.

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Getting Started, of risk assessment become convenient vehicles to raise broader issues around social inequality. There are, in short, always political considerations, ethical complexities, and judgement calls for which there can be no technical fix. The recent controversy about “racial bias” in risk instruments is

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Richard Berklten. Das schimmert z. B. in der eigenwilligen Bezeichnung von Sozialarbeitern als ?Sozialbetreuer“ (XI) durch. Mit einiger Plausibilit?t k?nnte vermutet werden, da? der Zwang, immer wieder neuen Amtspersonen die Familiensituation er?ffnen zu müssen, von den Müttern als belastend bzw. ?rgerlich empf

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Richard Berkm sie aus der Sicht einer zentralen Figur im Bezie- hungsgefüge eines Strafgefangenen (immer ein Mindestma? an Kontakten unterstellt) jene Schwierigkeiten beschreibt und analy- siert, die typischerweise für die soziale Umgebung eines Strafgefan- genen im Verlauf seiner kriminellen Karriere auftreten