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Ross, L. Mock Juries, Real Trials: How to Solve (some) Problems with Jury Science.
Ross, L. Mock Juries, Real Trials: How to Solve (some) Problems with Jury Science.

(Systems like eJury claim to highlight issues for prep. The states that host them may have some controversial legislation that invites reactions. Separately, within organizations, shadow AI is considered to be extraneous information, possibly pernicious although formerly heroic for surviving the Winter.)

#Decision-Making#Research#Legal
·philpapers.org·
Ross, L. Mock Juries, Real Trials: How to Solve (some) Problems with Jury Science.
Gordon, M. L., Lam, M. S., Park, J. S., Patel, K., Hancock, J., Hashimoto, T., & Bernstein, M. S. (2022, April). Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-19).
Gordon, M. L., Lam, M. S., Park, J. S., Patel, K., Hancock, J., Hashimoto, T., & Bernstein, M. S. (2022, April). Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-19).
(How is a juror instructed to eliminate implicit bias? What would be the specifics of a course that changed their minds? This is fairly easy to trigger in practice, e.g. as subtext to invoke irony.)
#Machine Learning#Legal#Bias#Paper#PDF
·dl.acm.org·
Gordon, M. L., Lam, M. S., Park, J. S., Patel, K., Hancock, J., Hashimoto, T., & Bernstein, M. S. (2022, April). Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-19).