It would be an interesting exercise to see how the addition of technology-assisted review outputs could potentially better the accuracy of these predictions.
Earlier this year, Institute for Law and Economics, a Joint Research Center of the Law School, the Wharton School, and the Department of Economics in the School of Arts and Sciences at the University of Pennsylvania published a paper outlining the development of models that predict both whether specific federal class action securities fraud litigations would settle and for how much. According to the paper, the predictions are “based exclusively on information available at the time of the lawsuit is filed.” Also discussed in the paper is the relationship of the evidentiary merits of the case to settlements. The authors argue persuasively that prior research has established that the merits of a case — meaning the amount of relevant evidence of e.g. fraud – is actually an important factor in determining the outcome of a case (apparently that assertion had been open to debate).
Given the fact that useful models have been developed that predict settlements and that evidence impacts settlements, it would be an interesting exercise to see how the addition of technology-assisted review outputs could potentially better the accuracy of these predictions. Specifically, it would be fascinating to know whether and how adding counsel’s evolving real-time evidence assessments, which could be facilitated by accelerated predictive coding, would refine the current prediction models as the case progresses through the litigation lifecycle.
Thanks to lexisnexis.com for highlighting the study.
