Sreenivasan, GopalAriturk, Deniz2020-02-102020-02-102019https://hdl.handle.net/10161/20077<p>A moral dilemma confronts criminal justice in unjust states. If the state punishes marginalized citizens whose crimes are connected to conditions of systemic injustice the state has failed to alleviate, it perpetuates a further injustice to those citizens. If the state does not punish, it perpetuates an injustice to victims of crime whose protection is the duty of the criminal justice system. Thus, no reaction to crime by the unjust state appears to avoid perpetuating further injustice. Tommie Shelby proposes a new solution to this old dilemma, suggesting that certain theoretical and practical qualifications can save the unjust state from perpetuating injustice. He argues that punishment can be just even as society remains unjust if it is: (a) administered through a fair criminal justice apparatus; (b) only directed at mala in se crimes; and (c) not expressive of moral judgment. In the first part of this thesis, I explore Shelby’s solution to show that certain aspects of his framework are superior to alternative ones, but that it nonetheless fails to resolve the dilemma. In Part 2, I use a novel technological reform that promises to make criminal justice fairer, the AI risk assessment, as a case study to show why even punishment that meets Shelby’s criteria will continue to perpetuate injustice as long as it operates under systemic social injustice. Punishment can only be just if society is.</p>PhilosophyCriminologyLawArtificial intelligenceCrimeCriminal justicePunishmentRisk assessmentSocial justiceMaster's thesis