✳flâneur — a map of the web's best reading

Machine Learning and the Rule of Law — SFI Press

sfipress.org · 1,245 words · saved by 1 readers

Predictive judicial analytics holds the promise of increasing the fairness of law. Much empirical work observes inconsistencies in judicial behavior. By predicting judicial decisions—with more or less accuracy depending on judicial attributes or case characteristics—machine learning offers an approach to detect when judges are most likely to allow extralegal biases to influence their decision-making. In particular, low predictive accuracy may identify cases of judicial “indifference,” where case characteristics (interacting with judicial attributes) do not strongly dispose a judge in favor of one or another outcome. In such cases, biases may hold greater sway, implicating the fairness of the legal system. There is ample social scientific evidence documenting arbitrariness, unfairness, and discrimination in the US legal system. To give just a flavor of the relevant research: United States federal appeals court judges become more politicized before elections and more unified during war (

Machine Learning and the Rule of Law - SFI Press 0 Machine Learning and the Rule of Law Law as Data pp. 433–441 DOI: 10.37911/9781947864085.16 16: Machine Learning and the Rule of Law Author: Daniel L. Chen, Toulouse School of Economics Excerpt Predictive judicial analytics holds the promise of increasing the fairness of law. Much empirical work observes inconsistencies in judicial behavior. By predicting judicial decisions—with more or less accuracy depending on judicial attributes or case characteristics—machine learning offers an approach to detect when judges are most likely to allow extra

Explore this link on the map →

related reading