The Compassionate Computer Algorithms, Sentencing, and Mercy

Netanel Dagan, Shmuel Baron

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Sentencing scholarship has largely neglected the possibility of applying algorithms to mercy. This doesn’t come as a surprise: Is there any greater contradiction than between algorithmic decision-making and the compassionate, human, and interpersonal nature of mercy? Such polarity brings some theorists and policymakers to reject algorithm-based sentencing altogether. In this chapter, we offer a preliminary attempt at integrating mercy within algorithmic sentencing. First, we distinguish between two main concepts of mercy—justice and pure—and different types of algorithms—deductive and inductive. Second, we argue the following: (a) as long as justice mercy can be reduced to a proportionality-related calculus (e.g., extra harsh suffering), it can be introduced through a deductive algorithm; (b) pure mercy, being unpredictable, and deviating from justice, can be incorporated mainly through an inductive algorithm. This is true, at least to some extent, even for theories that permit deviation from equality when exercising mercy.

Original languageAmerican English
Title of host publicationSentencing and Artificial Intelligence
PublisherOxford University Press
Number of pages20
ISBN (Electronic)9780197539538
StatePublished - 1 Jan 2023

Bibliographical note

Publisher Copyright:
© Oxford University Press 2022. All rights reserved.


  • Algorithms
  • Compassionate release
  • Inductive/deductive algorithms
  • Mercy
  • Proportionality
  • Sentencing


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