Abstract
The law values fairness, proportionality, and predictability. Accordingly, in the context of criminal law, punishments should be carefully calibrated to reflect the harm caused by an offense and the culpability of the offender. Yet, while this would suggest the dominance of “smooth” input/output relationships—for example, such that a minuscule increase in culpability would result in a correspondingly small increase in punishment—in fact, the law is laden with “bumpy” input/output relationships. Indeed, a minuscule change in input (be it of harm, culpability, or any number of other measures) may result in a drastic change in output, creating significant discontinuities.
Leading scholars have argued that smooth input/output relationships, which feature careful gradation and calibration, better accord with dominant theories of punishment than do bumpy relationships, which lack fine-tuning. Accepting as a starting premise that smooth input/ output relationships are to be preferred in the criminal law, this Article focuses on the significant doctrinal and practical impediments to smoothing out these relationships. This analysis reveals challenges to smoothing out relationships between inputs and outputs, as well as the difficulties associated with addressing discontinuous relationships among inputs and outputs. Specifically, it exposes the law’s classification of inputs and outputs itself as contestable and responsible for a range of hard-to-resolve discontinuities. In doing so, this Article begins the task of laying the groundwork for further analysis and possible reforms.
Statistical detection and classification of background risks affecting inputs and outputs
