Speaker 1: 0:20

Hey, it's Mark and welcome back to another edition of the Employee Survival Guide. Today's topic is the hidden dangers of using AI to predict your employment discrimination case. In today's world, employees can turn to artificial intelligence AI for almost anything, even legal advice. Some employees are beginning to use AI tools to quote-quote predict whether they have enough strong employment discrimination case. Employees are tempted with the immediate feedback that AI provides in analyzing their legal cases. We have recently seen employees who conducted their own legal research and put together what they believe are the correct legal citations mostly statutes and prominent US Supreme Court decisions, along with their own legal analysis as to why they have a case of employment discrimination and retaliation. This is their pitch to employment lawyers like myself to review their cases, or their actual or they're actually filing it with the state and federal and city agencies regarding their claims of employment discrimination, hostile work environment and retaliation.

Speaker 1: 1:28

This came up, this topic came up because I receive quite a few of these type of emails where people are trying to pitch a story to me and they, you know, no fault of their own. I mean, ai is out there. It's new and novel and employers are using it, of course, but you know, people have access to a Google notebook or a chat, gdp or something, and the technology itself is relatively new and it's based upon you know, upon what you fed into the system. And so when I read these emails and they give me the cut and paste of what their research was and we all know what this looks like it's like this chat or this AI type of production of work product that it looks like it's usually a list or that's kind of stale, it's not very emotional, it's just a matter, or that's kind of stale, it's not very emotional, it's just matter of fact, whatever. But it's only based upon what the AI device has captured in its learning. And the funny thing about this topic is that the AI devices, these algorithms, can pick up information related to agency websites and devour that, but the case law material, the case decisions from our courts state courts, federal courts they're not readily accessible and they're behind paywalls where you have to go in and access it. Now there are cases reported out there that there are various websites that put it out, but the vast majority of case decisions are locked behind paywalls for websites called like Westlaw and LexisNexis, and so the AI devices that the employees are accessing don't have that wealth of depth so that information is missing from its analysis. Now, this is a technology in infancy, so you can imagine that if employees are putting together their case analysis today, it might get more robust as the technology increases, maybe what they feed into it.

Speaker 1: 3:39

I would love to see the AI devices like a chat GDP, have all of the case material decisions going back, let's say, just give it 10 years. I mean these devices can learn the machine learned very, very quickly device so the employees could access it, because the employees would basically be able to read public court decisions because courts are public and read these decisions and decide for themselves if they have a case or not. Remember, lawyers are not the only parties that have an angle or the access to the law. Everybody has access to the law and at least that's my opinion and that's why I try to put the slant I do on these podcast episodes. I give you case decisions. You can hear about it. So the so having employees access in case decisions is really uber important to leveling the playing field between employers and employees, which is not even, which is uneven, the whole at-will debate you hear about me raise all the time. So back to the point of employees trying to figure out their cases before they confirm a lawyer or file before an agency and they're doing their AI research.

Speaker 1: 5:05

I'm not saying don't do this. I'm saying just do this with the eyes wide open about what you're looking at, and it's okay to begin to learn like a lawyer, and this podcast is really designed to kind of tell you some pitfalls, but also tell you how do you approach it to do it better, but also tell you you know how do you approach it to do it better. You know how do you take your AI research and create it in such a way that it's more accurate than it wasn't. So first is you know, read I got a lot of material on my website and a lot of podcasts talk about it. For example, hostile at Work Environment Everybody loves that topic on my website and the podcast, and so there's a lot of information about it. For example, hostile work environment Everybody loves that topic on my website and in the podcast, and so there's a lot of information about that.

Speaker 1: 5:54

But how do you interpret a hostile work environment with respect to the you know, a fact pattern that you, what you're currently going through. So what I encourage you to do is this why don't you write out your fact pattern and put it in long form you know your narrative from A to Z, chronological order, what happened, what happened next, who knew about it, whatever, and write it out? You might want to not include names of the employer and use fictional names of characters, because you're putting this information publicly out there into a domain AI device that's learning it and you have confidentiality provisions of your when you sign up for employment, so you would be careful what you're putting out there. I mean, it's like the black hole, this AI device. So just caution. You Just write a narrative of facts and then put it into the algorithm, the chat GDP, and ask it to assess based upon, let's say, you live in New York City and you say AI chat GDP, why don't you assess it under New York City law, human rights law, new York State human rights law and Title VII, 1964 Civil Rights Act in New York State, and let's see how that chat GDP device pushes out the outcome. Now we can't trust what it's going to say, but hopefully, if you're using your fact pattern, you might get a closer vantage point to illegality, to illegality.

Speaker 1: 7:28

Sometimes when I do the searches on chat GDP. I'll get warnings that they're not a lawyer and they can't make legal or give legal advice. You might see that as well, but play around with it and you might be able to come very close to, let's say, your fact pattern, directing you to the conclusion that you have a hostile work environment. And everybody knows what a hostile work environment is. It's, you know, a pervasive use of discriminatory comments or, you know, comparison of you versus others that are given you've received more favorable treatment. It's typically in a sexual connotation or sometimes it's in a racial connotation where you have, like you know, pictures of nooses for a racial case or lightning bolts for the KKK or some pervasive hostile like extreme situation which is, and maybe the computer can spit out that assessment for you.

Speaker 1: 8:20

So go through that exercise, begin to learn more. If you spend the time, you can figure out that there's information out there and I'm going to tell you this. The next chat GDP search should be give me notable let's say Southern District of New York cases in the last five years involving hostile work environment related to sex and see what those decisions come up with. And let's say you get five, six decisions and you push those case names back into a Google search and see if you can come across like a Cornell decision. They have a website that has case decisions. Justia is another site that has, you know, pdfs. You can read the actual decision, but use the devices, the search, in a way that you can use ChatGP to identify cases. You can use a variety of sources to identify cases on sexual or hostile environment in the Southern District of New York. It's a federal court and see if you can find the cases themselves to read through them and see if you can find the cases themselves to read through them.

Speaker 1: 9:27

What you're trying to do here is read through the narrative of fact in the decision, but then also look at the court's analysis of that, and that's where I really want to bring your attention. What's wrong with the AI use by employees today is that the employees don't have any understanding of the analysis as it relates to being a lawyer and giving the assessment. Now, I'm not saying that employees have to become quick, off-the-cuff attorneys to identify their cases. It's much simpler than that. I think you have the ability to look at here's what a case decision is from a judge and says here's the law I'm applying to this narrative of this case decision you just found using research, and you can see how the judge moves through that analysis, using the fact and then applying the law to the fact to establish the conclusion. Well, there you have it. You've just gone to law school. You've learned what the facts are, you've learned the law part of it and you've learned the analysis fact.

Speaker 1: 10:27

In law that's called issue spotting or I don't want to give anything to law school. It's just looking at facts, looking at law, seeing how the facts apply and how the law may apply, and it's a very generic way of looking at the strength, weakness of a case. So it's a very generic way of looking at the strength, weakness of a case. Now you would have to see a lot more of those cases to decide well, do I have a case or not, or is it how strong it is? I think you're better off writing a fact pattern and using your research, saying, ok, I found a sexual harassment case with a hostile work analysis by Judge so-and-so, and I put that into my analysis and maybe I'm going to search further because I want to find some more cases. And here's a trick Once I start to find a commonality of case decisions about the same thing in different court cases in the same district, let's say the Southern District of New York, which I'm admitted to.

Speaker 1: 11:23

I stop and I realize I have the basic foundation of what sexual harassment, hostile work environment is for the Southern District and you want to move further into the analysis and you look at possible other claims that might exist and use the case decision you're reading or researching to identify more cases. Now I'll give you a cheat okay, employment discrimination cases fall into the protected category. Okay, and it's age, sex, whatever race, religion, national origin, disability, and you know, if you're, by and large, you're going to fall into one of those buckets or retaliation or whistleblowing, and you want to do that search by those different categories of discrimination to find cases that are applicable to yours. Now, you're not going to find the exact case applicable on point. They call it. That takes a little bit deeper dive into the case law, spending hours trying to locate cases, but you're going to get a quick assessment using AI, using Google search, using Justia, the website, to locate cases to read. You have to read this stuff.

Speaker 1: 12:30

Don't expect ChatGDP to push out the analysis for you like we're used to, because that's not the way it works. What are you missing? You're missing the subtleties that the law is. You're missing the subtleties to which an employment lawyer will give you. But I think you can come close. I think you can educate yourself about the laws, because I've seen clients do this before AI got involved. I've seen clients go and do the research at law schools and law libraries and they came pretty close to what the actual analysis is.

Speaker 1: 13:03

So there's a lot of commonality of case claims that you can pick up very quickly to put in. I know that ChatGDP will push out. Here's what sexual or hostile work environment will mean, and you'll start to. You know, because you put it in several times, you get the same answer. That's not the case. If you actually read a court decision. By doing your deep dive research, you can actually figure out how the courts are laying out the elements of the claim for hostile work environment.

Speaker 1: 13:32

Then the next stage of that is well, step back and look at your fact pattern in the analysis and see, you know, are there inferences in your fact pattern that support the possibility that you have a claim of hostile workment discrimination? We're looking for strong inferences. So we're going to take all. There's two types of evidence here. There's direct statements, statements we don't like you because you know you're an f and whatever, and uh, and they start to do extreme things in the office and there's uh, just hostility, whatever, uh, and then there's that's all direct statement, stuff from a boss, uh.

Speaker 1: 14:09

And then you have this and that's like five percent of the cases. So it's out there but it's smoking on the evidence and it's not really what you're targeting. You're targeting circumstantial evidence. It's how you're treated differently than other people who are treated more favorably, who are the different categories. So, if it's sex, hostile work environment you're looking at, and you're female, you're looking at someone who's a male, who's treated better, and you want to do this compare and contrast, assessment in your own fact pattern, looking for inferences of how they're treated differently. Assessment in your own fact pattern, looking for inferences of how they're treated differently. I mean, that's really where the part of I want you to pick up on.

Speaker 1: 14:46

You know, look at chat GDP, get your data, your information about what the law is, do some additional research to confirm you're correct, because AI is not perfect yet. Get an understanding of working knowledge of what hostile work environment is, look at your fact pattern and then begin to do your analysis, I think five things stand out. Five circumstantial pieces of evidence point out of compare and contrast, and I was fired because I didn't receive the same treatment they did. Let's not make it complicated. Okay, put your facts together. Do your research. What's going to happen to you is when you do your research and you spend the time reading, like lawyers do in these cases, or listening to a podcast about hostile work environment on my podcast, you're going to begin to become familiar, like with anything, the subject matter that you're working with, and you'll start to see the nuances of things. You'll start to see things pop up for you factually that you may want to put back into your narrative.

Speaker 1: 15:41

Spend some time. Don't just shotgun the approach of saying here's a stretch GDP, I'm going to put my information and, bang, I'm going to send that to the lawyer. Okay, I'm not kidding, that does happen. It irritates me. What I'm trying to say is you can figure out and come pretty darn close to what your case is if you go about this process of assessing what lawyers do. They ask people to write the fact pattern, write the narrative chronologically. Don't provide conclusions, just state the facts, ma'am, and then do some research and understanding what this is? Because if you're going to ask a lawyer to take a case on contingency which is even worse ask a lawyer. If you're going to ask a lawyer to take a case on contingency which is even worse you want to be convincing. And if you're not convincing in your initial you know email, it's going to take about two seconds for me to realize it and I'm going to basically dispense with the. You know I'm not going to write back to you, I'm just going to say that's not a case interest spending my time on.

Speaker 1: 16:37

If you're going to hire a lawyer on an hourly basis and you're throwing your own money at it, you want to know that you're hiring an employment lawyer and you're spending your money wisely. There you become a collaborative effort with the employment attorney and I've seen clients who are bright and they're pretty savvy and they can. They work in a kind of a collaborative effort to push along a case and they understand it and so you can age yourself in the process of understanding the legal services you're receiving and understand your case. But if you go about this aspect of the strategy of pushing things into chat GDP, it's going to be met with like false left and right red flags. People pick it up pretty quickly. Employers and their counsel can read it very quickly, say oh, that was obviously AI produced, et cetera. So people are doing that.

Speaker 1: 17:34

But here's the things to avoid. To get down to the nitty gritty and it's you know, when you're creating your narrative and you're doing your research and you're going the extra mile to figure out what it is and it is not sexual harassment, hostile environment. Beware of the following issues. Okay, ai oversimplifies the law. Implement discrimination is complex, fact-specific and constantly evolving. Complex, fact-specific and constantly evolving. Now I told you kind of the basic primer of how to look at these cases in terms of your claims, because I'm doing that deliberately, I want to dumb it down, because I want to make it more accessible to you. The law has a tendency to make it really complex, but I'm trying to make it easier to absorb and there are a lot of commonalities amongst claims and you can only see that when you look at it long enough experiencing it.

Speaker 1: 18:30

So AI tends to be applied broad, rigid rules, missing exceptions or subtleties that I would understand as an employment lawyer, and they are exceptions that might make or break your case. I'll give you an example. So you have a claim. You are 365 days, 366 days, since you were fired, and it's well, and you decide finally to do something about it. What you don't know is that when you filed your UOC charge, you were a little bit late because the statute of limitations says 300 days, so you wouldn't know is that when you filed your USC charge, you were a little bit late because the statute of limitations says 300 days, so you wouldn't know that. So that's an exception. So just AI oversimplifies the law. So be very, very careful. Next thing is wrong predictions, wrong decisions.

Speaker 1: 19:56

I start this off with the famous set of cases that came out of the Southern District of New York. This involved lawyers. It's a true story. It was reported in the press Two cases of lawyers who were before the federal judges in two separate cases. Both had conducted AI research on legal cases, had conducted AI research on legal cases and the judge got a little bit upset in both cases and said and they were sanctioned because the AI that they were using whatever that was had made up case names and made up case decisional analysis, meaning made it up. I don't know what they were using, but both lawyers in both cases got reprimanded. And it's very embarrassing. The judges look at every single citation that we give to them because we're looking at the cases and saying this is supportive of our client's case. So, likewise, I'm asking you to do the same thing when you do a research through ChatGDP Follow up and read the case. You know these lawyers in that case that I'm citing the two cases they did not read the decisions that this research chatbot had created for them. It's embarrassing, but it proves the point that even lawyers could do the same screw-up mistake. So don't do that. So that's an example of non-existing cases and non-existing case decision analysis.

Speaker 1: 21:24

So AI isn't trained on your unique facts or the latest court rulings. That means it can give false confidence quote I will win the case or unnecessary discouragement. I don't have a case leading you to make damaging choices. So AI is in its infancy. Like I explained before, it doesn't have all the thousands and hundreds of thousands of case decisions across the country in its data bank because it's beyond a paywall. Yeah, you're like. Well, that's unfair. Why is that? Courts are open. Why can't we access it? Well, it's a larger question. I think that we should have access to all the case decisions in both state and federal courts. But maybe there's a reason why that's not the case, right, because more information is power the more power you have, the more damage you can do on a corporation, so maybe that's the case. There's also Westlaw and Lexis who serve this services, and I pay a good dollar for that service my lawyers myself.

Speaker 1: 22:24

The next item for what AI is not to be trusted is missed deadlines and lost rights, like the example I gave you. If you come up with your hey, I want to file a new EOC case 365 days after the adverse action meaning you got fired you're too late. 365 days after the adverse action meaning you got fired, you're too late. You got to file your decision or your claim to exhaust your administrative remedies in 300 days or within 300 days, so easiest is missed claims because you had. You didn't understand the filing requirements in terms of deadlines. I'll give you a basic primer 180 days and 300 days on employment discrimination cases. Okay, let's just follow that. So the adverse actions have to occur within that. Most state and federal agencies work on what's called a work-sharing agreement and so you get the benefit of a 300-day window, and I would always encourage you to file with the EOC first and then have the EOC file the claim with the local state agency. So you get the 300-day window time period.

Speaker 1: 23:26

The next item I spoke about before is when you create your written narrative and you put it into the chat GDP to figure it out. Use generic names. Don't use company information that's identifying of the company. When you type your story into a public AI system, you expose sensitive details names, dates, medical history that you can't take it back. Ai doesn't guarantee a privacy and you sign most likely confidentiality agreements with your employers not to share the information publicly. You're putting the information in a chat GDP or an AI device. You have disseminated information out there publicly. So be very careful because it's not about if. It's about when these systems and employers catch up that they can figure out who disclosed the information. So be generic with your names and company information and what you're talking about, so it can't be disclosed in the narrative when you write it, before you submit it, what your company name is and who the players are. The next item on what chat GDP or AI devices can't do for you they can't provide strategy or advocacy.

Speaker 1: 24:42

Ai can identify discrimination, but it can't file an EOC case. You need to have a working knowledge of the EOC process, which I'll basically give it to you. Go to the EOCgov, grab the Form 5, fill it out and notarize it. Say, see attached affidavit, write your fact pattern, get it notarized and then submit that that's your filing of the UOC charge. Anything else beyond that it's like leave the agency to figure it out. You know how to deal with it. But generally they will request mediation and you want to submit to that.

Speaker 1: 25:14

They can't negotiate with your employer. Well, you need somebody to do that for you. An employment lawyer can do that. I have to encourage people to negotiate directly with their employers and people do it and successfully do it. It's just simply you know you get your severance package and they say, no, we're not going to revise that. They give it to you in a PDF and it's intentional, and you basically say well, you know, here's my fact pattern, I have claims here and I'm going to try to leverage this and push on you.

Speaker 1: 25:40

You know, make believe to the employer that you have an attorney. You don't have to disclose the name of your attorney to your employer. You can just. You know, if you go through this process I've described, you can come down to a very professionally written thing on your own. You may not just use your affidavit with a Form 5 charge affixed to it, notarize it. You can convince your employer because the facts in the affidavit are the most important. It's not what the lawyer is doing, it's not what I'm writing about, it's what the client says in the fact pattern. That's damaging to the employer. You know, not disclosing. You have an employment attorney and just writing a very cohesive and methodical and detailed format from A to Z, chronologically speaking, about what the claims were. I've seen clients negotiate for money and close out a deal without having to hire an attorney and I love that because laws, you know it should be free and open. Why not? Before there were lawyers, there were just the law. So lawyers like to curb the market. I'm like I'm saying enough of the guardrails, let you're trying to do. Think smart about it.

Speaker 1: 26:49

Next item the AI can't represent you in court. So at some jumping off point you need to hire an employment lawyer. If you want to file a lawsuit, yes, the courts are set up. The federal courts are set up for you to file a pro se action. State courts the same way, and they will work with you to do that. It's a very simplified approach, but you can actually file your own lawsuit, but you don't want to avoid that because filing means notoriety on Google no-transcript, so be careful. So at some point you're going to have to step off and hire an employment lawyer if you feel you really need to do that.

Speaker 1: 27:35

So next item is hidden claims left on the table. This one is easy. Employment lawyers have a variety of claims in addition to employment claims employment discrimination claims. I'm sorry that they are looking at when they're looking at the same fact pattern. It's just by experience and so if you don't use an attorney, you're not going to get that analysis to help your situation and you're going to leave the claim on the table. The employer will see it because they know about it. The employers know what they do. If you don't clue into that aspect, they know exactly what they're doing. They want to see if you know and if you're better at spotting the issue and seeing what happened. That gives you leverage and that narrative can spell out that you do know what they did. That's what the narrative does. It has to do that.

Speaker 1: 28:30

The other aspect of AI whether it comes in your own individual research like this but AI reinforces bias, ai relies on historical case data. It may undervalue claims by women, minorities or other groups who already face systemic underreporting. The result bad predictions rooted in past injustices. Now you know AI. I don't know if it's smart enough to realize you know claims that case decisions occurred a long time ago. It's not. You know whether it's using current information. I don't know if it's smart enough to realize claims that case decisions that occurred a long time ago. It's not whether it's using current information. I don't know how they train it, but don't believe the presumption that they did train it correctly, because you have no idea. I mean they. I don't know of a product that's out there yet to let's provide AI-related efforts to case decisions to help employees like yourself. I don't think that exists. I'd love to create that to harden your skill set with knowledge based on cases. If you want to dive into it, you can, like I said before, you can research using ChatGDP. You can go to justiacom and find reported decisions. There are other sites that provided reported PF decisions for free of charge.

Speaker 1: 29:37

But the AI reinforces bias issue. That's the whole argument that humans created. The machine learning gave it what its source data to absorb, but there's inherent bias built into what it's reading. The other part of a bias has to do with algorithmic interviewing. There's some recent cases we've talked about before where the programming itself, the actual coding, is biased in some way, and there's some court decisions, some cases currently ongoing right now Emotional blind spots.

Speaker 1: 30:18

This issue has to do with you know if you're going to go all the way you know, or, to any extent, with your legal case. It really is about the issue of handling your emotions. People don't understand this and they get tripped up really easily and I have to redirect them back to center AI. When you're researching and developing your case, it gives you cold, technical answers. I mean it's not saying I'm sorry this happened to you. I've seen other examples where AI has gotten more emotionally connected to the user and that's kind of a development I'm seeing. I guess the more it knows you, it can get more predictable. It depends on what device AI device you're using.

Speaker 1: 31:10

So the you're trying to navigate the issue of your employment discrimination case with your employer and you get a lot of fear, anger, anxiety about what they're doing to you. The same goes when you hire me as an employment lawyer. It's very emotionally charged. I mean, you name it. I've seen it. I've seen great people go through the process, gracefully, delegate to us to do something, manage it, and they understand and they're managing and they're managing expectations. They're absorbed with their anxiety about what happened to them, their own personal issues.

Speaker 1: 31:53

I mean this truism that I know why you got fired. When I interact with you over a period of time or look at your cases, I can see why your employer fired you. If you don't know that, that you may be exhibiting behaviors that tell others and you can't see it. I've seen it. I mean this is like 28 years. I've seen exactly what, the behavior, why people acted the way they did and they got fired.

Speaker 1: 32:19

So you got to look at the emotionality of what you're about, to engage, what you're doing currently, if you're still working, take a hard look at it. Don't just assume like you're perfect and you're normal and everybody else is effing wrong and they got issues. So it's something that needs to be checked. And I try to draw people into the transactional analysis of their situation. Like, okay, something happened to you. Yes, I know it's emotional, but you're going to dump it all down in your narrative.

Speaker 1: 32:52

But when you come to the transaction, using AI to research and help you research and present your case. You want to do it in a very transactional mindset of, hey, I'm asking for a year's pay here. Ok, I'm using this piece of narrative that I wrote as leverage against the employer, that I wrote as leverage against the employer and I'm doing some research here to help me pinpoint that I'm correct and they're incorrect about what happened to me and see if you can influence them. Now I can't help this issue. This one issue is that if you show up at the table and there's no reference to who your attorney is, you might get called out on it because the employer is expecting to see your attorney show up and you have to fluff this one. You have to say, well, I'm working with an attorney and you're going to go through me directly. The employer is going to assume that you don't have an attorney and they're going to alter their dynamics as they're negotiating with you based upon that and I know that sucks, but employers do that. You have to figure out a way to make your narrative very, very convincing, based on actual facts. In your research about what's taking place, you may get their attention and they may want to bury that affidavit that you provided to them in exchange for a settlement of claims. So you're very powerful and you have leverage to work with.

Speaker 1: 34:16

Just slow down. Take a hard look at what you're being offered in terms of your chat GDP production results and think transactionally about this. Take the emotions out of it. If you're wanting more severance and you think you have a claim to do it on, take your time and research things. Maybe there are law clinics and law schools. These folks have students with skills who can assess what you're doing, possibly help you Ghost rate stuff for you. I've seen this done. I've seen a lot of stuff done. Go beyond just what doing possibly help you Ghostwrite stuff for you. I've seen this done. I've seen a lot of stuff done. Go beyond just what GDP has given to you.

Speaker 1: 34:55

The AI device is in its infancy and you need to. Basically, this is about you and your career and what's happening to you. So if you're going down that pipeline, that rabbit hole of using AI, this whole podcast was designed to make you say wait a minute, stop, take a breath. Look what you're using. Recognize that it's in an infancy state. Learn about cases in your jurisdiction. That's what lawyers do, you know. Supreme Court case decisions are available for all to see on the US Supreme Court's website. Other case decisions come out of various kind of wonky websites like Justia, but the information is out there and read about these cases and begin to learn about how law is applied to your set of specific facts and maybe you might come out with what the possibilities are, you know.

Speaker 1: 35:50

Just to give you a little further cheat, I'm not going to know the result of every single case that I propositioned to an employer in terms of what potentially might happen. You know the case might just settle. I never know. There's thousands of cases. I never know the result of what could have should have been of that case. That's the cheat. You don't really need to know that answer. What you do need to know is what do your facts say? Pick at AI for a second. What do your facts say after you've spent the time learning about what sexual harassment is and what hostile work environment is in the Southern District of New York under New York State law and New York City code? By the way, the premier area to be in both New York City Southern District, new York State, meh, but New York City definitely. And if you really do your research, you can be a powerful device for yourself in your negotiations with the employer I've seen it done and forget about the future of the case, what may happen as a result of what a jury will do. It's not about that. It's about influencing your employer to pay you money before the lawsuit ever gets to the light of day. So hopefully that answers something for you.

Speaker 1: 36:59

You do have to have a set of facts. Those facts govern the entirety of the case. You know, take your time. All the information is there. You've witnessed things and you know, put it together, make it compelling, but don't make it this drama-infused. You know thing of what you watch on TV. It's just matter of fact. Just say the facts A through Z, chronologically speaking. Quote people what they said to you. You know and go over it and just you know. Perfect that fact pattern. If you're going to do that, use AI to help you, but just have all these kind of safeguards. Just you know. Perfect that fact pattern. If you're going to do that, use AI to help you, but just have all these kind of safeguards that you know you're going to do your research and double check what you're doing. And if eventually you need an employment lawyer, well, you can always hire one. There's lots of them around. So I hope this helps you understand. You know, using AI from the employee's perspective some hidden problems with it, but you still can use it as a tool to help you get to where you want to go.

Speaker 1: 37:57

And really, at the end of the day, it's about severance. Okay, it's not about getting your justice, and people say it to me. I'm like I'm sorry. You know I go about this issue in discussions with judges as well. This is the best we can do. Okay, our justice system. And it's really about awarding money to people through negotiations and settlement.

Speaker 1: 38:18

Okay, because people, you can't afford the legal process. Unfortunately, it's made it too cumbersome financially to do it. So there's a way we go through this. Even if you go through litigation, we still end up settling cases before a jury trial. Ok, the courts are not. There's very few jury trials. You need to understand that. There's more probably in state court, less or so in federal court. So you do have access to the system in a way I've been describing. You do have tools at your disposal, which I have gone through. You can use AI, but with caution.

Speaker 1: 38:51

But it's really, at the end of the day, it's about you you writing your facts and you looking up the law. Maybe you want to go to law school? Hey, here's a shout out to going to law school. Okay, it's rewarding. You can help other people like you in the future if you do it, and people go to law school all the time. But understand, if you want to go to law school for employment law, you know a worthwhile profession. You know there's more need for them. Okay, lawyers are aging out, more lawyers are coming in, but so employment law is very valuable to help people like yourself. So it's just, you know, maybe that's maybe your calling, maybe that's what sparked something here for you and you go run with it. So, with all that said, that's what sparked something here for you and you go run with it. So, with all that said, use AI with caution, know the pitfalls, but you know, do your research, and it's okay. You're not going to get all the answers, but that's okay, and so hope you found it interesting. I'll talk to you soon.