This book is for anyone concerned about human and fiscal costs of jails and prisons. It shows how properly-developed algorithms can force the adoption of more legally sophisticated bail and sentencing practices that reduce incarceration, minimize racially biased decision-making, and maximize the use of resources, without sacrificing public safety.
This book is for anyone concerned about human and fiscal costs of jails and prisons. It shows how properly-developed algorithms can force the adoption of more legally sophisticated bail and sentencing practices that reduce incarceration, minimize racially biased decision-making, and maximize the use of resources, without sacrificing public safety.
Christopher Slobogin holds the Milton Underwood Chair at Vanderbilt University Law School. He has authored or co-authored eight books and over 150 articles on criminal justice issues. He is one of the most heavily cited law professors in the criminal justice field and is the only law professor to have received Distinguished Scholar awards from both the American Psychology-Law Society and the American Board of Forensic Psychology.
Inhaltsangabe
Preface 1. Rationale: what risk algorithms can do for the criminal justice system 2. Fit: why and when data about groups are relevant to individuals 3. Validity: figuring out when risk algorithms are sufficiently accurate 4. Fairness: avoiding unjust algorithms egalitarian injustice 5. Structure: limiting retributivism and individual prevention 6. Moving forward: the need for experimentation.
Preface 1. Rationale: what risk algorithms can do for the criminal justice system 2. Fit: why and when data about groups are relevant to individuals 3. Validity: figuring out when risk algorithms are sufficiently accurate 4. Fairness: avoiding unjust algorithms egalitarian injustice 5. Structure: limiting retributivism and individual prevention 6. Moving forward: the need for experimentation.
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