Jury questionnaires and supplemental questionnaires contain a wealth of information about prospective jurors. However, in most trials, little time is provided by the court for questionnaire evaluation. As a result, trial lawyers typically focus on the few responses that they feel are most important, potentially missing other critical information. JuryToolKit provides tools for questionnaire evaluation based on recent machine learning techniques. These tools automatically detect similarities among prospective jurors responses as well as identifying outliers. Prospective jurors are automatically grouped according to the similarity of their questionnaire responses. These groups can then be used as a basis for assigning juror ratings, saving hours of work as compared with the usual one-by-one questionnaire evaluation approach.
Should you strike a bad juror now or should you conserve peremptory challenges in case you have to strike an even worse juror later? Game theory will tell you how to use your peremptory challenges to arrive at the best possible jury. Game theory is used throughout economics, international relations, negotiation, and numerous other fields. It can also be applied to jury selection.
It seems like the opposing side has struck jurors based on gender and/or race. How many such strikes constitute purposeful bias? Is it possible that the appearance of group-based bias is simply the result of random chance? JuryToolKit does the necessary probability theory calculations to back up Batson-Wheeler claims. JuryToolKit can also be used to defend against Batson-Wheeler challenges by showing that your strikes were based on neutral grounds, and any appearance of group-based bias is simply due to random chance.