Electric scooters, especially the Ubers you see all over Albany, have definitely changed city transport, and with it, personal injury law. They’re convenient, sure, but they also create brand-new accident scenarios and make figuring out liability a lot more complicated. While plaintiffs are fighting for justice, something else is happening behind the scenes in the legal world: we’re starting to use artificial intelligence to pick juries. This is more than just number-crunching. AI is completely reshaping how lawyers build a trial strategy, and it has the power to tip the scales in high-stakes injury cases.
Key Takeaways
- AI systems can comb through massive public datasets, everything from social media rants to voting records, and predict a juror’s leanings with up to 80% accuracy.
- Putting AI to work in jury selection can shorten the whole voir dire process by 20-30%, which frees up the legal team to hammer down on the actual arguments of the case.
- For scooter accident cases, AI is invaluable for spotting jurors who already have a negative or positive bias toward shared mobility services, which has a direct line to how they’ll see fault and damages.
- Attorneys who use AI for jury selection are seeing about a 15% jump in favorable verdicts in complex personal injury lawsuits compared to doing it the old-fashioned way.
- The price tag for an AI jury analysis can be anywhere from $5,000 to $25,000 for a single trial. It’s a real investment, but in a high-value case, the return can be huge.
Case Study 1: The Downtown Albany Intersection Collision
We had a case in mid-2025 involving a 34-year-old marketing professional from the Pine Hills neighborhood. He was seriously hurt riding an Uber scooter downtown near State and Eagle Street. A delivery van making a left turn just didn’t yield and hit him. Our client ended up with a complex tibia fracture that needed multiple surgeries at Albany Medical Center, followed by a ton of physical therapy. He was out of work for almost eight months. Just the initial medical bills topped $120,000, and that’s before you even get to lost wages or his future pain and suffering.
Challenges and Strategy
The defense came at us with a comparative negligence argument, saying our client wasn’t wearing a helmet and was going too fast. They even tried to blame the scooter’s manufacturer for a supposed brake defect. With a defense that scattered, we knew our jury selection had to be surgical. We brought in an AI-powered jury analytics platform that sucked in publicly available data on the potential jurors: their social media activity, comments on local news sites, and even their donation histories. The system immediately flagged a few people who’d posted anti-scooter rants online or who had sat on juries that came back with defense verdicts in similar cases.
Our strategy was simple: use the AI profile to strike any juror who seemed unlikely to award real money for an accident involving a “non-traditional” vehicle. For example, the AI found one juror who had publicly griped about scooter riders clogging up sidewalks, a clear sign of bias against our client. On the flip side, it pointed out jurors who worked in healthcare or had shown empathy online for people dealing with serious injuries. The AI gave us a roadmap, letting us zero in our voir dire questions on specific red flags instead of asking vague, general questions. We were looking for jurors who got that driving in a city is dangerous and that scooter riders are vulnerable, and who understood a driver’s duty of care when turning left under New York Vehicle and Traffic Law Section 1141.
Outcome and Timeline
The trial went on for three weeks in Albany County Supreme Court. Because we were so careful with jury selection, thanks to the AI, the jury came back with a verdict for our client. The total award was $850,000, which covered all his medical bills, lost income, and a significant amount for pain and suffering. The jury put 90% of the fault on the van driver and 10% on our client for not having a helmet, a pretty standard defense tactic. We went from filing the case to getting a verdict in about 18 months, which is moving fast for a complex injury case of this size.
Case Study 2: The Sidewalk Collision in Washington Park
In late 2024, a 58-year-old retired schoolteacher was just taking a walk in Washington Park. An Uber scooter, driven by a college student, swerved onto the path and ran right into her. The collision left her with a fractured hip and a bad concussion. She spent four days in St. Peter’s Hospital and needed in-home care for weeks afterward. The student said he hit a bump and lost control, trying to dodge responsibility. The economic damages, between the hospital and home care, were already at $95,000.
Challenges and Strategy
The challenge here was different. We had to pin clear liability on an individual, not a big company, and manage the optics of a “young, irresponsible” rider versus an “elderly, vulnerable” pedestrian. The defense tried to say our client was distracted, even though there was no evidence for it. We knew juror sympathy could cut both ways. So we used the AI again for jury selection, this time to find jurors who might be biased against young people or, on the other hand, overly critical of pedestrians. The AI scanned public data for clues about their attitudes on personal responsibility, how young adults use technology, and pedestrian rights. It was great for flagging people who’d posted online about “entitled” kids or who were always pushing for stricter rules for pedestrians.
We specifically needed jurors who understood premises liability and the duty of care you have when you’re operating any vehicle, even a scooter, in a public place. The AI was a huge help in spotting people who had opinions on personal accountability, especially when it came to using recreational vehicles in shared areas. Our goal was to seat a jury that would stick to the facts of negligence, not get sidetracked by emotional arguments about the student’s age. Our case focused on his failure to control the scooter and the fact that he was riding where he shouldn’t have been which is a clear violation of city park rules.
Outcome and Timeline
The case ended up settling in mediation right before trial was set to start in Albany City Court, but only after we had selected a jury using the AI insights. The defendant’s parents’ homeowner’s insurance policy agreed to a $275,000 settlement. This covered all of her medical bills, in-home help, and pain and suffering. The AI’s role was critical in helping us build voir dire questions that screened for jurors who would hold the operator accountable, which gave us a much stronger negotiating position. We got it resolved in 14 months from the accident date, a very good timeline when you’re dealing with the complexities of individual liability.
Case Study 3: The Pothole Incident on Central Avenue
Here’s a tough one from early 2026. A 28-year-old grad student was on an Uber scooter on Central Avenue near North Allen Street when she hit a monster pothole and got thrown off. She ended up with a broken wrist and a concussion, which cost her a lot in medical bills and set back her academic work. The city of Albany just denied any responsibility, falling back on governmental immunity and claiming they never had prior notice about that specific pothole. Her economic damages were around $70,000.
Challenges and Strategy
Suing a municipality is always an uphill battle, especially with the “prior notice” requirement under New York General Municipal Law Section 50-e. The city’s lawyers were adamant that they had no written notice of the pothole before the accident. Our job was to find jurors who would be open to the idea of “constructive notice”, jurors who believe the city has a fundamental duty to keep its roads safe. This is where AI was incredibly useful. The AI analysis dug into potential jurors’ attitudes about government responsibility, public infrastructure, and personal injury claims against the government. It flagged people who were reflexively anti-government and, just as importantly, those who were too quick to defend government agencies no matter what.
We wanted jurors who were willing to take a hard look at the city’s maintenance efforts and who understood the legal concept of a “dangerous condition.” The AI helped us find people with backgrounds in engineering or public works, or who had complained online about local infrastructure, people who would get what we were talking about. Our strategy was to prove that the pothole had been there for a long time. We used a history of citizen complaints from the city’s own 311 service in the months leading up to the accident. While that wasn’t “formal” notice, it painted a powerful picture of neglect. We argued that the city had constructive notice because of the pothole’s size and how long it had been there, and that their inspection system just wasn’t good enough.
Outcome and Timeline
The case went to trial in Albany County Supreme Court. The jury, which we had selected very carefully with the AI’s help, found the City of Albany liable and awarded our client $160,000. This was a huge win, especially considering how hard it is to beat a municipal liability defense. The AI’s ability to identify jurors who were actually open to finding the government negligent was a huge factor in winning this case. The whole thing took about 22 months from accident to verdict, which is pretty standard when you’re litigating against a government body.
The Future of Jury Selection: AI’s Evolving Role
Make no mistake, AI in jury selection is a fundamental shift in how we practice law. Some critics worry about algorithmic bias or losing the art of traditional voir dire, but the truth is that when you use these AI tools ethically, they give you an incredible level of insight into a jury pool. These platforms churn through millions of data points from public records, social media, and demographics to create predictive models of juror behavior. It enhances an attorney’s intuition and experience, making jury composition more strategic and efficient. You can’t create a “perfect” jury (that’s impossible). The goal is to mitigate risks and find jurors who are more likely to be fair about the specific facts and arguments you’re presenting. As the data gets better and the algorithms get smarter, you can bet these tools are going to become standard practice in any complex personal injury case, especially those involving new technologies like Uber scooters.
Bottom line: AI gives legal teams a powerful edge in the complex world of personal injury litigation, particularly as urban mobility accidents evolve. By getting a clearer, data-backed picture of juror biases and tendencies, attorneys can build much more effective trial strategies. That’s how we aim for more just outcomes for our clients.
How does AI analyze potential jurors?
AI platforms dig through enormous amounts of public data, social media posts, public records, voting histories, demographics, and even past jury service. Machine learning algorithms then process all that information to find patterns and predict how a juror might lean on specific facts, legal arguments, or even a particular witness.
Is using AI for jury selection ethical or legal?
Yes, it’s generally considered both ethical and legal, but with a big condition: the data must be publicly available, not gathered through illegal snooping. The American Bar Association (ABA) has guidelines on using tech in trial prep, and they stress being transparent and following professional conduct rules. It’s really just an extension of traditional jury research, but with much better tools.
Can AI guarantee a favorable verdict?
No, and anyone who says it can is selling something. It’s a predictive tool that makes jury selection smarter by spotting potential biases. The trial outcome still comes down to the evidence, witness credibility, the lawyers’ skills, and the simple fact that jurors are human. AI just gives you a more informed starting point for your strategy in voir dire.
What kind of data does AI use for jury analysis?
It uses data you could find yourself if you had enough time: public social media profiles (like a public Facebook page or LinkedIn), comments on news articles, voter registration info, property records, political campaign donations, and public court records. The system looks for anything that might reveal a person’s attitudes or experiences relevant to the case, all from publicly accessible sources.
How does AI help in Uber scooter accident cases specifically?
In scooter cases, AI is looking for hidden biases. It helps spot jurors who have strong feelings about shared scooters, city traffic, or personal safety (like wearing a helmet). It can flag people who are predisposed to blame the scooter rider no matter what, or maybe the pedestrian or the car driver, which allows us to ask very direct questions about those biases during jury selection.
