Combating Discrimination in Personal Injury Cases: Impact of Race, Gender, and Ethnicity on Liability and Damages

Course Details
- smart_display Format
On-Demand
- signal_cellular_alt Difficulty Level
- work Practice Area
Personal Injury and Med Mal
- event Date
Tuesday, August 18, 2020
- schedule Time
1:00 p.m. ET./10:00 a.m. PT
- timer Program Length
90 minutes
-
This 90-minute webinar is eligible in most states for 1.5 CLE credits.
This CLE course will focus on how lawyers in personal injury cases can account for and address race, gender, and ethnicity assumptions in themselves and in others and how those assumptions impact the court system in general and case resolution in particular. Stereotypes and assumptions may be express or implicit and expressed in words or actions. Attorneys must understand how these prejudices can profoundly affect perceptions of justice as well as settlement terms and recoveries.
Faculty

Mr. Balmer has represented thousands of people in all kinds of litigation, including jury trials, workers’ compensation trials and hearings, social security disability hearings, and appeals in those areas. He has handled car crash, slip-and-fall, work site injuries, defective product claims, dog attack, liquor liability claims and other kinds of bodily injury matters. Mr. Balmer has lectured at various seminars, primarily in the areas of trial tactics and workers’ compensation issues.

Ms. Cárdenas has more than 20 years of experience representing adults and minors who have suffered debilitating injuries in every area of personal injury. Ms. Cárdenas is a member of the highly esteemed Multi-Million Dollar Advocates Forum due to her repeated success in winning multi-million dollar settlements for her clients. She has tried numerous cases in state and federal court and has argued before the Supreme Court of Illinois.
Description
Stereotypes permeate the building blocks of every tort case. Proof of the defendant's duty to the plaintiff, breach of that duty, causation, and damages are defined, described, and understood in terms of a "standard" or reasonable person, which is created by culture, including its prejudices. Traditional wisdom even advises the astute litigator to exploit these biases and to assess people by presumed preferences based on race, gender, age, and ethnicity.
Moreover, prejudices and stereotypes play a significant role in liability and damages. Recent cases involve awarding more significant damages for an unborn boy who died in utero than for girls with educated parents and promising futures. Statistics and data institutionalize and normalize earnings disparities, with lower damage awards to women and minorities.
There are ways, however, to recognize and handle offending conduct from other attorneys or court personnel. Likewise, practitioners are working creatively to exclude inherently racist and sexist "data" that decrease damages for various segments of the population.
Listen as the experienced panel explains how and why the conduct and attitudes of all participants in the personal injury case--judges, lawyers, witnesses, parties, and jurors--can profoundly affect the outcome of the case and create dissatisfaction with the judicial system and best practices to address these issues.
Outline
- How bias and prejudice present in the judicial system among participants
- Interaction with the court and other attorneys
- Perception of injury and damages
- Negotiations, settlement, damages
- Effective ways to uncover and counter unfair prejudices
- How bias and prejudice impact the outcome of specific cases
- Liability
- Damages
- Excluding "data" that decreases damages based on gender
- California's S.B. 41 barring reduction of damages based on race, gender, ethnicity
- Motions and other tactics
Benefits
The panel will review these and other issues
- What bias triggers exist in particular cases?
- What tools equip attorneys to see bias in dealing with witnesses on cross and on direct?
- When do biases exclude some of the most favorable jurors?
- When is it ever appropriate to leverage bias?
- How can biased data be excluded from the case?
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