Hope, Hype, and Fear: The Promise and Potential Pitfalls of Artificial Intelligence in Criminal Justice

By William S. Isaac. 2018

23 citations recorded by the source corpus.

How researchers see this paper

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

1. Luca Oneto
2. Silvia Chiappa
3. Simon Caton
4. Ray Jiang
5. David R Ha
6. Bernhard Schölkopf
7. Yuxi Li
8. Karol Gregor

Citations in the corpus

• A Causal Bayesian Networks Viewpoint on Fairness (2018, neutral): “This uneven distribution of police attention, as well as other factors such as funding for pretrial services [30, 43], means that differences in base rates be-tween racial groups are not reflective of ground truth rates.”
• Fairness in Machine Learning (2020, background): “In response, Equivant published a technical report [41] refuting the claims of bias made by ProPublica and concluded that COMPAS is sufficiently calibrated, in the sense that it satisfies Predictive Parity at key thresholds.”