Risk Assessment in Criminal Sentencing.

By J. Monahan, J. Skeem. 2016

191 citations recorded by the source corpus.

Subfields: Clinical Psychology, Sociology and Political Science, AI Ethics, Fairness & Society, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Clinical Psychology

1. Sam Corbett-Davies
2. Sharad Goel
3. Alexandra Chouldechova
4. Francesca Dominici
5. Madars Virza
6. Karthik Dinakar
7. Chelsea Barabas
8. Joichi Ito

In Sociology and Political Science

1. Sam Corbett-Davies
2. Emma Pierson
3. Ben Green
4. Cynthia Rudin
5. Sharad Goel
6. Francesca Dominici
7. Aaron Fisher
8. Aziz Z Huq

In AI Ethics, Fairness & Society

1. Alexandra Chouldechova
2. Sam Corbett-Davies
3. Sharad Goel
4. Emma Pierson
5. Cynthia Rudin
6. Ben Green
7. Madars Virza
8. Karthik Dinakar

In Social Sciences & Humanities

1. Sam Corbett-Davies
2. Cynthia Rudin
3. Sharad Goel
4. Alexandra Chouldechova
5. Madars Virza
6. Karthik Dinakar
7. Chelsea Barabas
8. Joichi Ito

Citations in the corpus

• Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment (2017, background): “Today risk assessments are used for two primary purposes,which Monahan and Skeem deem “prediction-oriented” and “reductionoriented” approaches to assessment [53].”
• Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2016, background): “Hannah-Moffat [20], Skeem [21], and Monahan and Skeem [22] examine legal and ethical issues relating specifically to the use of risk assessment instruments in sentencing, citing the potential for race and gender discrimination as a major concern.”
• Algorithmic Decision Making and the Cost of Fairness (2017, background): “Judges nationwide use algorithms to help decide whether defendants should be detained or released while awaiting trial [13, 31].”
• All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (2018, background): “being used to inform judges’ decisions on pretrial release, sentencing, and parole (Monahan and Skeem, 2016; Picard-Fritsche et al., 2017).”
• The false promise of risk assessments: epistemic reform and the limits of fairness (2020, background): “endorsements of evidence-based sentencing are similarly grounded in the goal of reducing incarceration [115, 148].”
• All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance (2018, background): “In an aim to counter this bias, machine learning models trained to predict recidivism are increasingly being used to inform judges’ decisions on pretrial release, sentencing, and parole (Monahan and Skeem, 2016; Picard-Fritsche et al., 2017).”