In recent years, the practice of risk-assessment has started to utilize machine learning to take in larger data sets and improve prediction accuracy. Simultaneously, it has expanded into the sentencing stage of the criminal justice system. This paper analyzes the effect of these developments on the consideration of the theories of punishment during sentencing. After first going over key characteristics of both the theories of punishment and the practice of risk-assessment, it presents a series of connections between aspects of machine learning enabled risk-assessment and each of the theories of punishment. It then provides developers of such systems with both general advice and advice aimed specifically at the highlighted effects, rooted in various design methodologies such as Systemic Design and Value-Sensitive Design, in order to better account for these effects. Future research can expand upon this paper both in depth and in scope: in depth by carrying out the empirical research needed to verify and improve upon the ideas in this paper; in scope by extending this research to other ethical debates surrounding machine learning in criminal justice.