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Group-to-individual (G2i) inferences: challenges in modeling how the U.S. court system uses brain data.
- Source :
- Artificial Intelligence & Law; Mar2020, Vol. 28 Issue 1, p51-68, 18p
- Publication Year :
- 2020
-
Abstract
- Regardless of formalization used, one on-going challenge for AI systems that model legal proceedings is accounting for contextual issues, particularly where judicial decisions are made in criminal cases. The law assumes a rational approach to rule application in deciding a defendant's guilt; however, judges and juries can behave irrationally. What should a model prize: efficiency, accuracy, or fairness? Exactly whether and how to incorporate the psychology of courtroom interactions into formal models or expert systems has only just begun to be examined in a serious fashion. Here, I outline data from the United States which suggest that trying to incorporate psychological biases into formal models of legal decision-making will be challenging. I focus on the use of neuroscience data in criminal trials, homing in on so-called group-to-individual (G2i) inferences. I argue that data which should be the most effective at swaying judicial decisions are in fact those most likely not to make a difference in the disposition of the case. I conclude that judges often assign culpability by ignoring what our best science regarding how human decision-making occurs. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09248463
- Volume :
- 28
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Artificial Intelligence & Law
- Publication Type :
- Academic Journal
- Accession number :
- 142165033
- Full Text :
- https://doi.org/10.1007/s10506-018-9234-0