The Compassionate Computer: Algorithms, Sentencing, and Mercy

Netanel Dagan, Shmuel Baron

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


Sentencing scholarship largely neglects 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 policy makers 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: (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 languageEnglish
Title of host publicationSentencing and Artificial Intelligence
EditorsJesper Ryberg, Julian V. Roberts
PublisherOxford University Press
ISBN (Print)9780197539538
StatePublished - 2022

Publication series

NameIn: Principled Sentencing and Artificial Intelligence (working title) J.V. Roberts & J. Ryberg eds., OUP, ‎Forthcoming


  • sentencing
  • mercy
  • algorithms
  • proportionality
  • compassionate release
  • inductive/deductive ‎algorithms


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