JuryTek is a data science company focusing on jury trials. We have a passion for applying scientific methods to assist trial lawyers during the various phases of jury selection. Our solutions include machine learning based evaluation of jury questionnaires, the application of game theory to the exercise of peremptory challenges, and probability theory analysis in support of, or to defend against, Batson-Wheeler motions.
Jury selection is a complex mixture of science, intuition and skill. JuryToolKit supports trial lawyers during jury selection by providing proven scientific analysis tools. The data-driven approach allows lawyers to base their jury selection work on solid ground. Good data makes good juries.
We are not psychologists. We do not predict how jurors will vote in your trial. We do, however, analyze hard data using well-founded scientific methods and we present it effectively to lawyers and trial consultants who can make such judgements. The improved workflow allows lawyers to focus on what they do best, rather than spend valuable time on data analysis tasks more suited to computer-based algorithms.
We understand that every venue is different. Some courts provide paper questionnaires while some provide juror responses in electronic format. different courts implement different versions of 'Strike and Replace' or 'Struck' selection systems. There is no one size fits all version of our data analysis tools. That is why we work together with you to fine tune our tools specifically to your needs in a given court and for a given trial.
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. JuryTek 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.