“Noise,” AI, And The Black Box Of Legal Judgment

A Nobel Prize winning psychologist and a noted legal scholar have teamed up to clarify the way legal judgments are made and how the process can be improved. Their research can be applied to any type of legal judgment, but in particular the researchers – psychologist Daniel Kahneman and legal scholar Cass Sunstein – looked at how experts valuate insurance claims. What they found were huge disparities: Even tapping a cohort of similarly-experienced and highly-credentialed experts, results varied by at least 50 percent. The researchers characterize these variations using the mathematical/engineering term “noise,” and with this analysis they attempt to shed light on the relationship between intuition and more ostensibly rule-based decision making, and what can be done to improve the process. “It will be interesting to see the analysis and solutions offered by Kahneman and Sunstein and their impact on how we evaluate insurance claims and other disputes,” writes the author of this post, insurance recovery attorney Charles P. Edwards. “In the meantime, parties and their attorneys should focus on the identification and reduction of noise through consistent procedures and proper forecasting techniques for successfully resolving insurance claims and other disputes. (That is, until the algorithms come for us.)”