Unfolding the Eightfold Lawsuit

Is the "black box" of AI hiring finally being pried open? In this episode of The Chad & Cheese Podcast, hosts Joel Cheesman and Chad Sowash sit down with Rachel Dempsey of Towards Justice to dissect a landmark class-action lawsuit against Eightfold AI. Filed in early 2026, the suit alleges that the platform creates secret "consumer reports" on job seekers—scoring everything from personality to performance potential without the applicant’s knowledge or consent. From the personal struggles of barred candidates to the legal muscle of the Fair Credit Reporting Act (FCRA), we explore why there is no "AI exemption" for transparency and how this case could fundamentally redefine worker rights in the age of algorithmic recruitment. Think you know why you didn't get that last job? The truth might be hidden in a data profile you never authorized. Chapters 00:00 - Introduction to Rachel Dempsey and Towards Justice 02:59 - Understanding the Eightfold Case and AI Hiring Practices 06:10 - The Fair Credit Reporting Act Explained 08:58 - The Implications of AI in Hiring and Data Privacy 11:59 - Class Action Lawsuit Dynamics and Its Importance 16:03 - Legal Precedents and Future Implications 20:04 - Employer Responsibilities and Risks in AI Hiring 24:01 - The Role of LinkedIn and Other Platforms in Hiring 27:54 - Conclusion and How to Reach Towards Justice

• 0:00Speaker 00

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  Now we return you to our regular scheduled program in progress.

• 1:29Speaker 03

  Hides your kids, lock the doors. You're listening to HR's most dangerous podcast. Chad Sowash and Joel Cheesman are here to punch the recruiting industry right where it hurts. Complete with breaking news, brash opinion, and loads of snark. Buckle up, boys and girls. It's time for the Chad and Cheese Podcast.

• 1:51Speaker 00

  Oh, yeah. Two guys whose favorite class in high school was detention. Hey, kids, it's the Chad and Cheese Podcast. I'm your co-host Joel Cheesman. Joined as always, riding shotgun is Chad Sowash. As we welcome... Rachel Dempsey, Associate Director at Towards Justice, a nonprofit legal organization that defends workers on their way to being knee deep in the eightfold lawsuit. Rachel, welcome to HR's Most Dangerous Podcast.

• 2:24Speaker 04

  Thank you. It's great to be here.

• 2:25Speaker 01

  Rachel, how do you become an Esquire?

• 2:27Speaker 00

  Go to law school.

• 2:28Speaker 04

  Go to law school and pass the bar, I think. You might just have to go to law school. I'm not sure.

• 2:34Speaker 00

  And in her case, Yale Law School, which means this is rock bottom for her career, which it can only go up from here. Thanks for taking a bet on us, Rachel. We appreciate it.

• 2:43Speaker 04

  This is, uh, this is more interesting than any law school class I ever went to.

• 2:48Speaker 00

  Oh, oh, love that. Love that. So our listeners likely won't know you, although they will be probably hearing more of you as the eightfold case unfolds. But give us a little taste of who is Rachel?

• 3:00Speaker 04

  So I'm an attorney at Towards Justice. I'm the Associate Director here. We're a nonprofit law firm based in Denver, Colorado, but we do national work. We represent workers and consumers in litigation and in other advocacy that builds worker power and advances economic justice in Colorado and across the country. And we do both. Our original focus, I think, was on, on workers and we've expanded a little bit to more consumer competition law. And this case is really at the intersection of, of a lot of those legal spaces.

• 3:29Speaker 01

  It definitely is. What about you, though, Rachel? Taking a little time on the bunny hill there in Colorado. Tell us a little bit about you.

• 3:36Speaker 04

  Sure. I have a three-year-old who went skiing for the first time this weekend. She took her very first ski lesson. I think she went down the bunny hill one time in three hours. And her report was that the hot chocolate was the best part. It was a great success.

• 3:54Speaker 00

  ACL's fine.

• 3:55Speaker 04

  All good.

• 3:56Speaker 00

  A lot of that's going on with the Olympics.

• 3:57Speaker 04

  As physically, yeah, as physically unbruised as a three-year-old can be.

• 4:02Speaker 01

  Mentally is something entirely different, yes.

• 4:04Speaker 04

  We'll learn that in a few years.

• 4:06Speaker 01

  That's what the hot chocolate's for. So if you can, can you give us a little bit about just kind of like the background of Torch Justice and also how did you stumble upon this, this company, this little company called Eightfold?

• 4:23Speaker 04

  Sure. So like I said, Torch Justice, we take a broad view of worker power and economic justice and sort of started learning about these AI hiring companies. It's something I think that, that everybody who works with workers, who represents workers has heard about and sort of understands is a really big issue for the, the workers that we talk to. They're, they're facing, I think, a lot of difficulties in applying for jobs that they haven't necessarily seen before. So we started looking into these products about a year ago, and we've been working actually very closely with, with Torch Justice has one of our senior fellows is Seth Fratman, who until a year ago was the general counsel at the Consumer Financial Protection Bureau.

• 5:08Speaker 01

  Oh, hello.

• 5:09Speaker 04

  Yeah. And they, they had sort of done some looking into these products and actually issued a bulletin about two years ago, sort of suggesting that there could be FICRA liability, Fair Credit Reporting Act liability for some of these products that sort of track workers and create reports on them. So that, this case came out of that project and I think Eightfold jumped out at us because a lot of huge companies use it. And then we came across a lot of workers who had had experiences with applying to jobs there and with not being able to get jobs with companies that use Eightfold and not understanding why.

• 5:42Speaker 01

  So for all of us dummies out there who don't know what FICRA or F-C-R-A, which is what I've been calling it, we don't know what that is. Can you, can you give us a little, little background on that?

• 5:52Speaker 04

  Sure. Yeah. Lawyers love a, love a nickname for a law. The Fair Credit Reporting Act was, is a, is a law passed in the 1970s that governs what information and, and how companies can use information in creating credit reports that control people's access to credit, insurance and jobs. So FICRA Defines a credit report as information bearing on a consumer's creditworthiness, credit standing, credit capacity, character, general reputation, personal characteristics, or mode of living. The reason why I read through that sort of legal language is because I think that until I got involved in this, I thought that a credit report was just like the thing

• 6:34Speaker 04

  that TransUnion or Equifax or Experian made to get, determine your access to, to a credit card or to a loan or whatever. And it's actually a lot broader than that. The legislative history makes that clear. Legislative history is another sort of lawyer word, which means what the lawmakers who passed the law sort of were talking about and how they explained the need for the law when they passed it. So the Fair Credit Reporting Act was passed in 1970, in 1970, sort of at the very beginning of credit reports as we know them. And at the time that the Fair Credit Reporting Act was passed, credit reports could include stuff like, you know, what your neighbor, what your neighbor said about you, whether,

• 7:16Speaker 04

  what your marital status was, you know, whether you had a reputation as being a drunk among the people that you, that you hung out with. So what, what FICRA was intended to do was to provide some transparency into this process, right? Like people were being denied credit because their neighbors, you know, said they threw loud parties or whatever.

• 7:38Speaker 00

  Not only that, just so I understand, that information was there, but I, I wouldn't have access to it. So it was sort of the, we'll say black box more than a few times in this interview. It's there, but I can't see it, which I think comes to the crux of some of this. So I think you've, you've done a great job of setting the table. Talk about the players in this. Talk about the plaintiffs. You're working with another law firm. Like let's, let's get the players on the field now.

• 8:03Speaker 04

  Yeah, totally. So Torch Justice is one of the lawyers on the case, one of the law firms on the case. The other law firm on the case is a private plaintiff's firm called Outon and Golden. Again, they, they typically do employment cases, but this is sort of their employment and consumer law, I think has a lot of overlap and particularly these days. So then the named plaintiffs in this. Case Erin Kissler and Sridhi Bhamak are both workers who have applied to jobs, a lot of jobs, using Eightfold platforms and have really struggled to find a job. They're both, they both have a lot of experience. They're both sort of relatively older workers and they came to us having applied to just hundreds of jobs

• 8:45Speaker 04

  and been denied for those jobs over and over again and not understanding why.

• 8:50Speaker 01

  Well, this sounds, I mean, much more layered than the Mobley versus Workday case where it's literally, that to me just seems like age discrimination. This seems much deeper from the standpoint of, as Joel had said, collecting all that data and it being in a black box and not being validated and/or verified by the actual candidate and/or user or consumer or whatever the hell you want to call them, right? But there's literally no validation or verification. And I'm sure you've seen the Eightfold patent. There's a lot of inference that happens.

• 9:22Speaker 04

  Yeah.

• 9:22Speaker 01

  The patent, you take a look at some of their marketing materials that talks about enrichment data and actually taking from LinkedIn and GitHub and all these different areas and they're enriching a person's profile. And it's like, well, how do you know that that's valid data? So if you could dig into that a little bit, because this is so much more layered than the Mobley case.

• 9:43Speaker 04

  Yeah. And I think the Mobley case is just addressing a different issue. It's also a serious issue. I think there are, there are reasons to think that this AI hiring software facilitates and sort of recreates human biases. And so there are potentially, I think, discrimination cases that could come from this. This is different. This case sort of applies to all users and deals with the right to, both to privacy and to transparency, to understanding sort of how your data is being used. And so just based, again, based on Eightfold's public information, eventually I think we will hopefully get insight into sort of, you know, private information, be able to test that in discovery. But right now we just have access to its public information.

• 10:24Speaker 04

  But there's a lot of public information they've put out there about that, about their product. And based on that public information, I think there's two potential issues, both of Which would violate the Fair Credit Reporting Act. One is that they do sort of in their patents in various marketing materials, talk about these enriched talent profiles and the enriched talent profiles sort of suggest that they find data from elsewhere on the internet about a specific candidate and add that data to a candidate's profile. I think they've denied that. Again, the discovery will sort of let us explore more into that and our allegations are based on the public statements that they've made. The other issue though, even setting aside whether for any individual candidate, they like go to your LinkedIn sort of, you know,

• 11:09Speaker 04

  blogs that you've written, whatever their marketing material suggests, even if they don't do that, for every applicant, they sort of take the the information that the applicant has given them, they take the information that the employer has given them, and then they break down that information and sort of analyze it and use that that by that analysis, they make inferences about, you know, what jobs are like each other, what job titles are like each other, and what sort of skills an employee is likely to be able to develop, what their career trajectory is, what jobs they're likely to have in the future. And all of those inferences are provided to the employer and, and, you know, the employer sort of used to decide whether or

• 11:51Speaker 04

  not someone is eligible for a job. And, you know, those inferences are are unverifiable, right? Some of them are predictions of the future. And I think that that in itself is a credit report under the definition of the credit report, and it feels, I think, very invasive to people to have this machine deciding what you want to do with your career. I mean, you know, maybe it's wrong.

• 12:11Speaker 00

  And in case it ever comes out, Chad, that OnlyFans account is not me. It is not me. Okay?

• 12:18Speaker 01

  Check the credit card on that one.

• 12:20Speaker 00

  Eightfold is a company with a $2 billion plus dollar valuation. They've raised about $400 million. I assume that they have something to say about this. What has been their response other than just it's not true?

• 12:31Speaker 04

  So their response has been, you know, we are an ethical AI company and what we use is just candidate data and employer data. Now, I am not the judge of what's who's ethical or not, right? That's not my job. I'm a lawyer. My job is to decide whether or not it looks like they're complying with the law. So setting aside whether they're ethical, which is not something that I'm opining on. Again, the allegations in the complaint come from their marketing material. They're, I think it's, they're all cited. You can follow the citations and see the patents. They're all in the complaint. And regardless of whether for sort of any individual candidate, the, the individualized information is only from, you know, the, the resume and the employer.

• 13:11Speaker 04

  Again, these inferences, these sort of, these calculations, this match score is being compared to, I think their, their big selling point is, you know, we compare people to 1 billion data points, right? We, our decisions are informed by 1 billion data points. I mean, that is a lot of external data that they're using to draw these inferences, um, to create these reports that say things, you know, like this person has these skills today, but they're likely to be able to develop these skills in the future. And based on the types of companies they've been at in the past, they're likely to go to this type of company in the future. So, you know, I think that regardless of what data they're sort of ingesting about any individual, what goes through their system and then comes out is, I think, a, a much more expansive report than that.

• 13:54Speaker 04

  This is not just them sort of reciting what's on someone's resume.

• 13:58Speaker 01

  Meet Dave. Dave's resume says he's customer centric, a point of sale systems expert and a people leader. Reality check, Dave's resume was written by AI and AI applied to thousands of jobs while Dave was on his couch binging episodes of Jackass all day.

• 14:18Speaker 01

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• 15:00Speaker 01

  There's literally no hard edges around what it actually means, but yet everybody's using it. And I want to dig into this patent real quick because I have it up my computer. So as you were talking about talent insights, quality of education. I don't know what that means. Does that mean that I get a better quality of education by the different school that I go to? I mean, what does that mean? Career growth progression, skills, depth, industry expertise, and then you get into the personality insights. This is kind of scary. Okay. Talking about whether I'm a team player, I'm an introvert extrovert. Whether I play chess or, you know, a high endurance athlete. I mean, it's just like, and then there's also the predictions piece that that's associated to this.

• 15:44Speaker 01

  This is all set in the patent that's out there that is the framework of which Eightfold was built on. And yet they're they're denying all of this.

• 15:53Speaker 04

  I think that's exactly right. I mean, there's a lot of information that they have said and we're just repeating that information. Right? I mean, again, everything in the complaint is cited. We're just repeating it. And I do, I think it feels very invasive to people and it's, I think, pretty clearly covered by these existing laws. I mean, again, these laws are from the 1970s, but even at the time that they were passed, their drafters were concerned with sort of computerized data collection, very specifically. The sort of line that I love is one of the, one of the drafters said, "The individual is in great danger of having his life and character reduced to impersonal blips and key punch holes in a stolid and unthinking machine."

• 16:35Speaker 04

  Now, obviously, we've moved past key punch holes, but I think the, the, the overall sort of concern is very much coming to bear in these products.

• 16:43Speaker 00

  Rachel, this is a, this is a class action lawsuit. For those, again, that don't know, sort of describe what that means, how that could unfold. And then maybe what, what are some historical examples of how these cases resolve?

• 16:56Speaker 04

  Sure. So a class action is a structure for a case where a single issue or a single sort of legal concern affects a large group of people all at the same time. So the idea is that, you know, every single person who's submitted an application who's been affected by Eightfold could file their own lawsuit. That would probably be like millions of lawsuits, right? That that just doesn't make sense from an efficiency perspective. It would overwhelm the courts. None of it would work. You can't afford to hire your own lawyer for that. So the a class action sort of aggregates all of those claims together using sort of the examples of a couple of named plaintiffs, a couple of people that have been affected in the same way as everybody else by the lawsuit.

• 17:36Speaker 04

  So the way that those cases proceed is generally sort of go through what's called a class certification procedure, where the court decides if like the lawyers are correct, that it's more efficient for the case to move forward all at once. That's often one of the sort of big early fights is whether you get class certification or not. Once a class has been certified, sort of everybody's claims are in it together. People in the class get notice of their rights, have the opportunity to stay in the class or to leave the class. So all of that moves forward together. Generally, people don't have to affirmatively opt into the class. If a class is certified, they're a member of the class. So people who are affected right now don't have to do anything in order to sort of stay part of part of the lawsuit.

• 18:18Speaker 04

  And then, you know, eventually either it all goes goes to trial sort of together or there's a settlement. And then obviously, the alternative is that, you know, class certification is denied. The cases go forward on their individual claims. We're optimistic, I think, that this is an appropriate case for for class treatment.

• 18:33Speaker 01

  Yep. It seems like, or at least I think, I believe I read that there are over a billion profiles within the actual Eightfold system. And this is obviously, we're just going to break it down into, you know, the US. So let's say that there's a 100 million or, eh, let's say 50, 50 million people who've been impacted on this. How are they notified? Are they notified? How are they notified? And how do you get into the discovery process at this point?

• 19:01Speaker 00

  The good news is our listenership is about 50 million, so that'll cover most of most of the people that know about this now.

• 19:07Speaker 04

  So generally, when you

• 19:08Speaker 00

  You're welcome.

• 19:10Speaker 04

  When you get a class certified, notice goes out to everybody. I think that we've all gotten sort of like emails in our inbox that say like, this is a class notice, whatever. Often they go to spam.

• 19:20Speaker 00

  Postcards.

• 19:21Speaker 04

  Yeah, postcards, whatever. I actually check my spam pretty regularly as a lawyer who does class actions to be like, am I missing a class action here? Is there like, is there something I need to opt into? Or, you know, like submit a claim form to get money, right? I'm not going to leave free money on the table.

• 19:36Speaker 00

  All those sales leads in the spam inbox for lawyers. I had no idea. Never thought of that. Yeah.

• 19:42Speaker 04

  But anyway, so if a class gets certified, I think generally class notice goes out. And then if there's a settlement, if there's like money, then class notice definitely goes out. Again, it's usually some combination of email and, and, and mail. And then in a case that's this big, there may also just sort of be like press coverage where you learn about, learn about, learn about it. They're sort of like posting ways to, for a class case to get notified. Generally though, I mean, one of the advantages is that of a, of a class action is that you don't individually usually have to do anything to participate, potentially unless there's money on the table.

• 20:14Speaker 00

  Is there a website? That's usually something, isn't it?

• 20:17Speaker 04

  There will be. Yeah, there will be. There's not right now. Yeah. But there's generally a, a class website.

• 20:22Speaker 00

  I think Chad owns Eightfold Sucks. If, if you need that one in the future, just a heads up.

• 20:27Speaker 04

  The defendant usually has to agree, and I do not think they're going to agree to that one.

• 20:31Speaker 00

  They probably won't agree on that one. Sorry. Sorry, Chad.

• 20:34Speaker 01

  That's no fun. That's no fun at all. So going back to the, the class action side of the house. So back in 2017, I think Talent Ben was in a class action, but they went ahead and settled. So therefore they didn't have to go to discovery. And to be quite frank, our industry and the world needs discovery because we have no clue at this point because of all the black boxes that are out there, not just Eightfold. We've got so many black boxes that are out there. There has to be a precedent set and there's not really, or at least I don't know of one, especially in the hiring space where there's been a precedent set. How do you, this is no pressure, this is on you, Rachel. How do we make sure this gets to discovery? So we can actually see the data and see what's happening so that companies that are currently doing it can stop, number one. And number two, ones that

• 21:19Speaker 01

  Want to get into this space, start to understand, oh shit, you know, maybe we need to do some redesign.

• 21:24Speaker 04

  So as lawyers and as lawyers representing a class, it's our ethical obligation to sort of achieve whatever result we think is in the best interest of the class. Sometimes that does mean early settlement. If, for example, I'm not saying that this is what happened in that case. I'm not saying that that's this is what happened in any specific case. But if sort of the defendant comes and says, you know, we messed up, we are, we're going to change our practices. Let's sort of get this resolved before there's a lot of expensive litigation, then maybe you'll reach an early settlement. I mean, the other reason why you might reach an early settlement is that you realize your case isn't as strong as you thought. So there are situations in which you reach a, a pre-discovery settlement and that is the best, sort of the best outcome for your class.

• 22:03Speaker 04

  As lawyers, I think that we are, you know, towards justice, we, we do litigation, we are in the public interest. We are sort of trying to impact, you know, affect policy and bring attention to issues that we think need more light. And I think this is one of them, but we ultimately have the responsibility sort of to the class to get the best possible result for them. And litigation is expensive, right? So, and discovery is expensive. So there are, I think, incentives on all sides to, to settle early before all of the money that could potentially go to the class ends up going towards, you know, actually the legal battle. So, you know, I, I can't know how this case is going to, to shake out. And, you know, again, we have obligations that aren't just sort of purely get to discovery.

• 22:48Speaker 04

  That said, I do think discovery is a really important way to shine light on a lot of these issues and

• 22:53Speaker 01

  Oh, yeah.

• 22:54Speaker 04

  is one way that litigation can help advance the public interest.

• 22:58Speaker 00

  It's commercial time.

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• 24:13Speaker 00

  Rachel, this was filed in California. Explain to me again, as a layman, it'll be tried in California if it goes to court? And then can other states use it as precedent? Well, how does the federal, because it's a federal law? Like, explain that to me. Help me, help me make sense of that.

• 24:29Speaker 04

  Yeah, so the case was filed in California and it's under both federal and California law. I think that the class is defined as sort of everyone who was affected by eightfold nationwide. The California claims only apply to people in California because that's sort of how, how state law works. FICRA applies to, to people nationwide. I honestly, I can't remember off the top of my head how we sort of defined the class. I think it is a nationwide class, so it would affect people nationwide. But it is also common, I think, there are other state laws that, that provide similar protections to, to workers in other states. You know, I think that, that there's a potential for follow-on cases in other states as well.

• 25:10Speaker 00

  And historically, I mean, this is a, this is a company that serves global clients. Could this be a springboard for, say, the EU or other international governments to look at this and say, like, we're going to do the same here because that's an issue here? I mean, that's, that's a real existential threat to any, any AI company, I would think.

• 25:30Speaker 01

  Oh, yeah.

• 25:30Speaker 04

  Yeah, so I, I'm not a US lawyer, right? I am a US esquire.

• 25:34Speaker 00

  We won't hold your feet to it. I asked historically, like, this happens.

• 25:37Speaker 04

  Yeah, I mean, absolutely. And I do think that the EU does have some stronger privacy protections than the US in a lot of ways.

• 25:43Speaker 00

  They do.

• 25:44Speaker 04

  It's my understanding they are looking into these AI companies and these AI hiring platforms. And so I do think that is an issue that all of these companies need to be aware of.

• 25:51Speaker 01

  And I'm sure they are paying attention to the news and seeing the headlines all over the place.

• 25:57Speaker 00

  Are you saying they might have added motivation to stick it to our AI companies, Chad?

• 26:01Speaker 01

  Could be, could be.

• 26:03Speaker 00

  Rachel, we have a, we have a lot of employers listen to this show and many of them are using, if it's not Eightfold, it's some other AI black box. We can magically give you the perfect candidate for every job. What kind of risk? You mentioned Microsoft, I think, as an Eightfold user and, and part of this case. Correct me if I'm wrong, but like what employers are freaking out about this stuff? What would you tell them in light of this case?

• 26:27Speaker 04

  The case that we have brought is just against Eightfold. It's not against any of the employers. That said, I, I think that there is potentially liability on the part of the employers. One of the, one of the things about the Fair Credit Reporting Act is that it imposes potential liability, both on the people that use the credit reports and the people that create the credit reports. So, uh, you know, I do think it's important to understand what these rights are. They're fundamentally, you know, they're disclosure correction rights. So I think employers should be aware of what those rights are and, and just make sure that they're complying with the law and providing users, providing applicants with all of the rights that they're entitled to.

• 27:04Speaker 01

  Well, it's interesting that you say that and it's a great question, Joel, because on the Mobley versus Workday side of the house, they had asked for client lists of Workday clients that were actually using aspects, AI, quote unquote, AI aspects of the product. So, I mean, Joel and I have talked about this for years. Many laws actually hold the employer responsible solely. We're starting to move to a shared responsibility where these black boxes are being, you know, created. Although that doesn't leave the employer off the hook because they should be doing the due diligence and the constant auditing and looking at analytics and also their, their hiring cohorts to really understand what's happening in.

• 27:49Speaker 01

  The black box. I find this really, really interesting. There are so many other things that are happening out there and I'm sure you already have a full plate as it is. But you know, there are companies that are asking, or actually forcing in some cases, employers to provide hiring signals back to train LLMs, right? So large language models. And for me, that's way too far, right? To be able to actually take these hiring signals. I mean, first and foremost, the employer shouldn't be giving to them. But as a candidate, and that attached to me and all of these different systems. If these hiring systems are training large language models, I have no clue that this is happening.

• 28:34Speaker 01

  Right? This is again, the whole fair credit report.

• 28:36Speaker 04

  Absolutely.

• 28:37Speaker 01

  I have no clue that this is happening. It sounds like there could be perspective impact in, in many other areas of large language models, training and such.

• 28:48Speaker 04

  I think that's absolutely right. I think that using this data to train large language models. And again, that's one of eightfold's big selling points, right? Is that they train their large language models on, you know, 1 billion skills, 1 billion people, et cetera. I think that there is potentially significant liability there. And that employers, as well as the sort of reporting companies themselves, really need to be aware of that and, and, and think through that very seriously.

• 29:12Speaker 00

  Rachel, we have a lot of people who use LinkedIn. And I want your opinion on LinkedIn as a model where someone voluntarily gives their profile, creates a, you know, an account. And then ideal, I guess LinkedIn could do some sort of a ranking of people based in their system.

• 29:28Speaker 04

  Yeah.

• 29:28Speaker 00

  Do you view a LinkedIn as susceptible to cases like this? Or are they sort of immune because you're, you're voluntarily going on to their system? You're agreeing to their terms of service, which I assume they say, we, we can do whatever the hell we want in terms of algorithms and whatnot. Like, do you think LinkedIn is immune to these kinds of cases? Could LinkedIn be legally bound to showing people like a credit report? What they are showing employers or what employers can access in their algorithm? Like where does LinkedIn or maybe? Even indeed, which has a really big database. Where do they play into into these cases, if at all?

• 30:05Speaker 04

  This is a very lawyerly answer. I don't want to make any comments on LinkedIn's specific potential liability. But generally speaking, I mean, we all agree to so many terms of service. We are all bound to infinite, infinite word count of these sort of fine print terms for every single thing we do online. That said, one of the things that's really powerful about the Fair Credit Reporting Act is that it requires not only consent, like the click through consent that companies get from you all the time and you have no idea what's in there. It requires standalone and clear and conspicuous disclosures. So the click wrap type of disclosure, type of agreement that we're all agreeing to all the time via those little pop up terms of service boxes. That is not good enough.

• 30:48Speaker 04

  FICRA requires the FICRA disclosures to be made separately from any other disclosures. They can't be buried in a long contract and they need to be clear and conspicuous. They need to be in a contract, in a format that people will read and understand as disclosures about this specific thing. So I think that's one of the things that's powerful and helpful about FICRA is that you can't just bury the disclosures in the fine print contracts or behind the little hyperlink that says terms and conditions, or all of the various ways that companies get you to agree to these contract terms.

• 31:22Speaker 00

  Those bastards.

• 31:23Speaker 01

  Rachel, you've given us so much to think about, so much to think about. This is Rachel Dempsey, Esquire, from the Torch Justice, the Associate Director. So now, Rachel, if there are, I don't know, prospective class action suit clients who want to reach out to you, want to find out more on what they should do because they feel like they've been impacted, how would they actually reach out to you and start that process?

• 31:49Speaker 04

  Yeah, so you can find my contact information on the Torch Justice website. That's Torch with an S, which is something that I have to explain like at least once a week. But yeah, my contact information is available there. Our intake form is available there and we love to hear from people.

• 32:05Speaker 00

  Chad, I have a sneaking suspicion that we will hear from Rachel again. Again in the future. That's another one in the can, everybody. We out. We out.

• 32:14Speaker 05

  Thank you for listening to, what's it called? A podcast. The chat, the cheese. Brilliant. They talk about recruiting. They talk about technology, but most of all, they talk about nothing. Just a lot of shout outs of people you don't even know, and yet you're listening. It's incredible. And not one word about cheese. Not one. Cheddar, blue, nacho, pepper jack, Swiss. So many cheeses and not one word. So weird. Anywho, be sure to subscribe today on iTunes, Spotify, Google Play, or wherever you listen to your podcasts. That way you won't miss an episode.

• 32:59Speaker 05

  And while you're at it, visit www.chadcheese.com. Just don't expect to find any recipes for grilled cheese. So weird. We out.