Inducing Predictive Models for Decision Support in Administrative Adjudication

Quantifying Legal Entropy

“…Moreover, new approaches in legal analyticslike those in sports analytics-are likely to be paradigmshifting in the ability to predict the outcomes of disputes and related aspects of the law more generally (cf. Katz et al [32]; Branting et al [33]).…”

“…Indeed, attorneys regularly the estimates the odds of winning and losing cases. Moreover, recent developments in legal artificial intelligence have vastly expanded the promise of more automated approaches to predicting legal outcomes (e.g., Katz et al [32]; Branting et al [33]). Second, aside from practical interest, it may be theoretically illuminating to devise mathematical models of the operation of the law.…”

“…Of course, practically quantifying these types of entropy is no simple feat, but a combination of human-coded and automated methods-including those using advances in AI (e.g., Branting et al [70]; Katz et al [32]; Branting et al [33]) 26 could certainly provide a precise quantitative metric for at least a particular field or doctrine in the law. As machine learning and other automated techniques in legal document classification and analysis continue to improve, arguably, the previous impasse among scholars regarding the "indeterminacy" of the law should 26 Branting et al [33] uses "maximum entropy" classification models to predict outcomes of a variety motions in federal district court. Although Branting et al [33] does not discuss legal entropy as that term is used here, maximum entropy models as applied to legal disputes implicitly concern interpretive and applicative entropy.…”

“…As machine learning and other automated techniques in legal document classification and analysis continue to improve, arguably, the previous impasse among scholars regarding the "indeterminacy" of the law should 26 Branting et al [33] uses "maximum entropy" classification models to predict outcomes of a variety motions in federal district court. Although Branting et al [33] does not discuss legal entropy as that term is used here, maximum entropy models as applied to legal disputes implicitly concern interpretive and applicative entropy. Specifically, Branting et al [33] attempt to predict outcomes for three different types of motions using the following features: "the party filing the motion, the judge ruling on the motion, the sub-type of motion, and alphanumeric character sequences having non-alphanumeric characters on both the left and right sequence borders that occur in the text of the motion."…”

“…Although Branting et al [33] does not discuss legal entropy as that term is used here, maximum entropy models as applied to legal disputes implicitly concern interpretive and applicative entropy. Specifically, Branting et al [33] attempt to predict outcomes for three different types of motions using the following features: "the party filing the motion, the judge ruling on the motion, the sub-type of motion, and alphanumeric character sequences having non-alphanumeric characters on both the left and right sequence borders that occur in the text of the motion." As Berger et al [50] explain, a maximum entropy approach "model[s] all that is known and assume[s] nothing about that which is unknown."…”