Bright Data and Distributional Justice
Courts promote the concept of equal justice under law. (208) Inequality in financial resources, however, has made that promise a hollow one for many litigants. The fact that some litigants can hire jury consultants to obtain a tactical advantage over others has always been an example of a distributional inequity of the system. (209) Bright data can change that by providing court approved bright data profiles (again appropriately moderated) to all litigants. If the information is already being collected for purposes of the jury venire, then court systems can make it available for the parties in trial.
As will be discussed in the next Section, this personal information about potential jurors will be incredibly helpful for litigants trying to pick a favorable jury.
Litigants and Bright Data
Bright data offers great advantage to litigants for the simple reason that most lawyers would like as much information about potential jurors as possible. (210) As a tactical matter, lawyers seek any available advantage, and, some times, knowledge is power when it comes to voir dire. (211) Whether or not more data equates to better juror selection, more information about interests, inclinations, and past acts will result in more informed jury selection.
As set out above, the information held by big data companies can expose attributes about ourselves that we may not even acknowledge. While imperfect in many ways, the clues about our interests and past do hold some value in deciding how we might view a particular legal case. The data available may well be superior to traditional oral questioning in voir dire, or formal questionnaires, because it avoids embarrassing admissions or self-censorship. (212) Implicit biases and explicit biases might be revealed without having to ask a direct question. (213) Further, litigants can use the information to ask follow up questions, test veracity of answers, and sort jurors in a more efficient manner.
The Availability of Bright Data
Big data information systems exist and are collecting and analyzing personal data which could be made available to courts. Big data companies already possess all the information that courts use for jury summons, as these public databases (DMV lists, voter lists) form the core of the information generated about individuals. (214) In addition, these companies already contact the same individuals for other reasons. Adding the specific task of coordinating jury summons with existing information is well within existing capabilities. Big data companies thus may see a financial incentive in partnering with court systems that could hire them to select jurors. (215)
While the cost of contracting with big data companies is unknown, courts may find the arrangement actually results in a cost saving for them. Currently, courts must hire, support, and fund entire juror offices to coordinate the thousands of citizens arriving for jury duty. Outsourcing the selection to big data firms might save both personnel costs and technology costs. Further, a more targeted system will avoid waste in terms of over-summonsing jurors because of high juror no-show rates. Courts may thus have an incentive to outsource this service to a cheaper, more efficient, and arguably more effective mechanism for summoning jurors.
If courts do adopt such a big data inspired system, they would need to answer a few practical questions. First, do courts want to address the traditional fair cross-section problem? If jury venires do not represent the existing population in terms of race or ethnicity, should courts attempt to summon a perfectly representative jury? Would doing so require altering algorithms to correct for expected juror no-shows? Would this targeting necessarily overburden members of minority groups who might be summoned at a disproportionate frequency? (216)
Courts would also need to decide whether they wish to go beyond traditional diversity. With big data metrics, the choices of which categories are really quite open. One could program the summons-generating algorithm to target age, income, political affiliation, sexual orientation, religion, other demographic characteristics, etc. In particularly complex or technical cases, one even could select for a certain level of technical knowledge. Individual jurisdictions would have to consider such decisions, but, as a technical matter, the additional measures of diversity are nearly unlimited.
Depending on the categories chosen about jurors, courts will also need to decide what type of information they will share with the litigants. Big data dossiers include far more personal information than need to be shared with the parties. Health information, Internet search queries, reading lists, and other areas should be off-limits (or not collected by the courts). As will be discussed in Part III, real privacy issues exist and must be addressed.
In addition to what information, courts must determine when they will provide the information to parties. (217) Obviously, courts know which individual jurors are scheduled to show up to court several weeks or months in the future. This timeframe provides ample opportunity to collect information on the jurors. However, in the ordinary course of trial, the court does not know which courtroom (or to which case) a juror will be assigned until the day of service. (218) As it makes little sense to reveal personal juror information to lawyers who do not need that information, courts will need to create a system to provide that information at the correct time, and to the correct courtroom. Plainly, courts already share juror lists with parties, but with big data there will be more information to share. Providing the parties with such a list with enough time to prepare might make the actual jury selection much more efficient, at least in large civil trials or death penalty cases with numerous summoned jurors. (219)
("Instead of waiting until after a verdict is rendered to look into the background of jurors selected to hear the case, suggests Circuit Judge Anthony Rondolino, lawyers in complex litigation could be given time at the outset and encouraged to perform social media
Finally, courts will need to make a decision of what to do with the information once the juror has been excused from service. (220) While one easy solution would be to purge the information, or anonymize it, the possibility of future study may tempt courts to keep the information. As big data companies know, data can be a valuable commodity. (221) If courts began studying juror composition, jury verdicts, and cases, very interesting (and potentially valuable) correlations could be generated.
These practical questions pale in comparison to the difficult theoretical questions that arise from big data technologies' ability to offer the tempting possibility of precision jury selection. The next Part addresses these fundamental questions about whether courts should adopt big data in their jury selection systems.
THE BIG DATA DILEMMA
New data technologies present a dilemma for courts. The informational gaps in jury selection are real and unsolved. Big data offers innovative solutions to improve at least some portion of these problems. Yet, as with the use of many new technologies, real risks exist. The dilemma, in fact, strikes at the very core of the jury system--why we have it, what it symbolizes, and how courts should respond to new technological advances.
This Part explores five fundamental questions that courts must answer before considering the use of bright data in jury selection. First, even if bright data could provide a perfectly representative jury venire, would searching for a diverse jury pool change the role of the jury in society? Second, how would the invasion of privacy that results from using big data technologies affect juror participation in the jury system? Third, should courts, as governmental institutions, be in the business of collecting vast troves of personal information about potential jurors, and, if so, what are the risks involved? Fourth, would access to additional personal information about potential jurors reduce unconstitutional peremptory challenges based on racial or gender stereotypes or would it create more sophisticated pretextual strikes? Finally, would the affirmative use of racial or gender considerations in selection systems run afoul of existing equal protection rules prohibiting consideration of race or gender in selection systems?
These big questions of role, privacy, power, pretext, and discrimination involve longstanding, contested issues. Each will be addressed in turn.
The Question of Jury Role
Perhaps the most fundamental question presented by the big data dilemma is what does society want the jury to be? Sorting technology might allow us to have a fully representative jury, algorithmically designed to represent not only the racial and gender makeup of a community, but proportionately represent the community's political affiliation, employment, income, and personal interests, etc. However, this algorithmic accuracy only matters if the goal of the jury venire is to create a truly representative cross-section as opposed to a fair cross-section. If the jury instead serves some other role, then this technological innovation may be unnecessary.
The question of the jury role has been well-considered and oft-contested since the Founding. (222) Depending on how one views the jury's role--as a fact finder, a check on government, a representative democratic institution, a moral conscience, or a space of civic education--the answer to whether bright data technologies assist or hinder the jury's role(s) may vary. This Section examines the competing visions of the jury as impacted by the possibility of bright data technologies. Each potential role of the jury will be briefly set forth with a broader discussion of bright data's impact to follow.
The big data jury.
|Author:||Ferguson, Andrew Guthrie|
|Position:||II. Big Data and Bright Data C. Bright Data and Jury Selection 1. Court Systems and Bright Data d. Bright Data and Distributional Justice through Conclusion, with footnotes, p. 971-1006|
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COPYRIGHT GALE, Cengage Learning. All rights reserved.