What happens when the lines between what’s real and what’s not blur in the age of AI? From deepfakes to AI-generated resumes, the challenges of fraud are growing at an unprecedented pace. Zain Zaidi, founder and CEO of TransCrypts, a startup in SHRM’s 2025 Workplace Tech Accelerator cohort, dives into the transformative power of self-sovereign verification — an approach that puts control of verified credentials back in the hands of individuals. Learn how blockchain technology and innovative verification tools are helping HR leaders “discern the truth from the fluff and the fluff from the fraud.”
What happens when the lines between what’s real and what’s not blur in the age of AI? From deepfakes to AI-generated resumes, the challenges of fraud are growing at an unprecedented pace. Zain Zaidi, founder and CEO of TransCrypts, a startup in SHRM’s 2025 Workplace Tech Accelerator cohort, dives into the transformative power of self-sovereign verification — an approach that puts control of verified credentials back in the hands of individuals. Learn how blockchain technology and innovative verification tools are helping HR leaders “discern the truth from the fluff and the fluff from the fraud.”
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Alex: Welcome to the A IHI project. I'm Alex Lonzo shr m's. Chief Data and Analytics Officer. This week we're discussing the rising challenges of fraud in employment, credential verification and compliance. Today we'll be highlighting the intersection of AI and self-sovereign verification, and how verified credentials are reshaping trust in the workplace.
Our guest this week is Zain Zaidi, founder of and CEO of transcripts, one of the startup companies in shr M's. Workplace Tech Accelerator 2025 cohort. Welcome to the A IHI Project Zain.
Zain: Hi, Alex. Thank you for having me.
Alex: It's great to have you here, and I'm looking forward to having a deep conversation with you about these types of issues, especially these that are so central to most of us in the HR space who are trying to understand specifically how we can verify [00:01:00] key credentials, uh, for, for our potential workforces.
Uh, one of the things that I, I am grateful for Zain in my role as a member of the SH RM Labs Investment Committee. Is the ability to kind of meet founders like you who have fantastic origin stories, have these kind of really cool kind of ways in which they identified their, their niche and where it is that they want to go ahead and make their mark on the world.
To start us off, can you tell us a little bit about yourself and the story behind transcripts? 'cause it's such a great story and I'd love to hear more about what motivated you to really go down this path.
Zain: Awesome. Yeah. Um, so yeah, my name is Zain. I'm, uh, 25 years old. I've been the. Co-founder and CEO of transcripts for the last five years, believe it or not. So created this, uh, company right outta college. Um, our origin stories actually stemmed from my own personal experience, uh, dealing with my undergraduate transcript.
Um, I was undergraduate student here in the Bay Area. I studied electrical engineering, and [00:02:00] I had got an admissions into grad school. Um, grad school. Wanted to verify. The my grades and to, to verify that the grades that I had stated on my application were authentic, as is a relatively common process. Uh, so they asked me to send over an official transcript from my undergraduate, uh, university.
Um, so I went to the registrar's office. I paid like a $30 fee and had them send over an official transcript. I go into the summer thinking I'm ready to start grad school in the fall. But about halfway through the summer I get this big, bold red email saying, your offer of admissions is in jeopardy. As you can imagine, it's, you know, it was a little bit of a mid-summer panic.
Um, I was like, what happened when I read the email, I found out that they never received my undergraduate trans. So it got me thinking, um, you know, I had already paid this money to send over my official transcript, but they hadn't received it, so I don't really have control over my credentials. Uh, I'm at the whims of my registrar's office to prove that I went to school and after four [00:03:00] years and tens of thousands of dollars and many sleepless nights studying engineering, I figured that was a, it was a little bit silly and that there was probably a better way of solving this problem.
So rather I was, I was able to fix that issue and, and get my admissions back into grad school. But rather than going to grad school, I decided I'd start transcripts.
Alex: I gotcha. You know, one of the things is I've, I've had those night sweats, those terrors myself, right? Uh, and I say that in large part, having graduated from FIU, Florida International University of the State, uh, institution down in Miami, and thinking specific. Specifically about all the steps that I had to take, those little administrative steps to prove that I had a PhD right, to prove that I had earned my, my doctorate.
And you know, even the signature pages on the, the copies that you have to go through with your, your advisors and. Your, your dissertation committee. It, I still have night sweats about it, and I'm almost 50 now and, and have those and, and those terrors and thinking about an offer being rescinded or my employment being rescinded in some way, shape or form.
So I share your, your concern, [00:04:00] especially around that big red email that your, your offers in jeopardy. One of the things that strikes me is you started off in the higher ed space, right? And then you moved into what it was that was the employment space, and, and I'd love to hear a little bit more in terms of your origin story, what caused you to pivot that to, to that direction?
Mm-hmm.
Zain: Absolutely. As the name implies, our original focus was university transcripts. Um, what we realized very quickly is that selling to universities is nearly impossible as a small startup. Um, universities think of, um, onboarding software and semesters, and as a 20-year-old who had funded the startup primarily through credit card debt, I didn't have semesters to figure out whether or not this was a viable solution.
Um, so. You know, about a few months in, we started talking to great group of advisors. Um, and, and among, amongst that group was people who worked in hr. And they said, you know what? We deal with this pesky issue that we get, these inbound verification reference check [00:05:00] calls. Asking us if our employees work here, is there a way that you can give our employees a document that says this is a valid official proof of employment and proof of income of this employee and allow that employee to share those records themselves.
So me and my co-founder, Ali got to the, got to the drawing board and um, we devised a way to do it for employment and income credentials, and that's been our business ever since.
Alex: You know, I, I, I appreciate that pivot. And I, and as somebody who's been around the HR space for quite a while, I know that that is something that was, uh, like almost vexing is the way to describe it. I just, verification in general is, is something that I, I think most of us don't think about, but when we think about it, it becomes the, immediately a head scratcher.
Uh, one of the things that strikes me is I, I, I'd love to hear a little bit about how. Transcripts approach is unique. What is it that makes it differentiated? Uh, uh, something that is just a, a whole other aspect that we haven't seen in the marketplace to this point.
Zain: Um, so our core tenant is [00:06:00] data self sovereignty, and, and that's a very fancy word for basically a very simple concept. You own your data and in a age where, you know, big tech companies and, and countless others are always asking us for our data and taking that data and, and monetizing it, it's kind of a paradigm shift where it's saying, Hey, look.
We as transcripts, we don't view employee data. We don't, um, you know, we have no access to it. We are simply the platform where you can verify your own employee's data. Um, but aside from that, we don't, we don't do anything else with it. Like, and, and it's N 10 encrypted, so that means that even we as administrators of the.
Have no access to your employee's credentials. Your employee can also, you know, owns that credential forever and can share it with whoever they want, whenever they want, and however they want. Uh, we also use this sometimes loaded technology called blockchain, um, which is a, uh, immutable ledger or database.
This basically ensures that we're able to store this record [00:07:00] in an immutable way, um, so that no one can tamper it, right? Obviously when an employee, there's incentive to commit fraud when whenever you deal with credentials. It's very important to be able to combat that incentive and blockchain is one of those really great ways to combat fraud and increase transparency and auditability of any credential on a platform.
So that's kind of where our focus and our kind of our core technology is. And that's kind of differs us from a lot of, uh, of our competitors.
Alex: What does it look like in terms of the customer journey though? T talk to me about what it looks like in terms of, you know, let's say I'm a, I'm somebody who's applying for a job. How do you guarantee me self sovereignty, so to speak?
Zain: Yeah. So, uh, we have the, the biggest and the most primary way that we, uh, enable consumers to own their employment credentials is that the company that you work for. Reach out to us and we'll onboard them. It takes about 15 minutes to get this whole thing set up. So it's, it's very, it's very low weight. Um, our biggest thing is, you know, we hate change management as much as, uh, everyone else.
And we know in hr, change management is, is one [00:08:00] of the big issues. Um, so, you know, we already have all our security certifications. We have everything ready to go. It's actually directly on our website, so we wanna make it as easy as possible. All we need is, um, we just use, we just integrate directly with your HRS or payroll.
Database. The only reason why we need to do that is because that usually contains the full, authentic list of all the employees in your organization with their title. Uh, using that list, we issue out verified credentials to all the employees in your organization, both current and previous. Those employees can then go to transcripts.com, login with their email and their credential, be waiting there for them.
They can then just click on that credential, click share, type in whoever they wanna share it with. And that credential goes up. Uh, our credentials are accepted at over 99.9% of credit unions, banks, landlords, future employers in the country. So, um, chances are that, you know, once your employee sends out that credential, it will meet the employment verification requirement for that, uh, requesting party.
Alex: Hmm. And, uh, just so I, I I, I, I wanna hit upon something that you [00:09:00] highlighted for me just in, in, in describing that. This notion of controlling and having self sovereignty over your data is, is really critical, right? I mean, we talk about, obviously there's, there's value in that just from a human perspective.
But one of the things that strikes me is. In an era of AI where fraud can be so rampant, in particular, not just because of AI and the ability to create or, or change the ledger, so to speak, the way you've described it, one of the things that strikes me is it's also your data is so powerful and so potent in an AI era because AI is consuming that data repeatedly.
So I'd love to hear what you think really makes it so critical and so crucial in thinking about this concept of, of self-sovereign verification.
Zain: Yeah. You know, one of the big questions that we get asked nowadays is, how do I prevent deep fix? Right? So, you know, as you know, all of us are eventually gonna have deep fix about ourselves. AI is gonna create almost. Realistic 3D impressions, uh, [00:10:00] of who we are. There, there might be, you know, in the next year, you know, you might not know if the person that's hopping on the zoom call with you is actually that person or a deep fake of that person, someone pretending to be that person, right?
AI has gotten so good at emulating voice and video that we have no way of knowing, uh, who this person is. So I think this makes verification even more important, right? If you know this person and, and this Zoom account, for example, has a verified credential attached to it. And, and you get that little verified, you know, check, you'll give you a whole lot more trust, uh, in the whole process.
So we think in an age of AI, where we're kind of headed, where, you know, impersonations are on the rise and it's gonna be almost impossible to discern real from fake information. We think verifications are gonna be in increasingly more important. And that's something that we're actually. As a company focusing more towards how do we ensure not only do we enable your employees to own and access their data, but how do we also ensure that your employees can protect their identity, their digital identity, and verify who they are in a trusted manner.
Yeah.
Alex: Yeah. Uh, Lord knows I myself have been uh, [00:11:00] uh, someone who's actually been taken by this notion of deep. Fakes not personally, not, not taken in terms of, uh, uh, being duped or anything like that, but obviously I we're seeing a, an era of deep fake, really growing to some degree, right? I I, hell if, if you've been anywhere in the last, uh, eight, eight months, you've probably seen deep fakes in the most unsophisticated way kind of pop up and at the same time.
Deep fakes that are as sophisticated as the kind that can lead to massive fraud and massive, uh, kind of theft all over the place. So I'd love to hear some, some examples of what, what you're, you're experiencing in your own world that, that really push this.
Zain: Yeah. I mean, I, I, I heard a story of a, um. You know, there was a controller at a organization and he received a phone call. Uh, I think you've, you've probably heard of this story too, and it, it was the CFO of, of the business. Um, and the CFO asked him to move a very large sum of money, uh, from one account to another external account.
And the CFO made it seem urgent. You know, we all get, you know, phone calls from our coworkers. [00:12:00] And so the, the, the controller did it, um, turns out that wasn't the CFO, it sounded exactly like the CFO Someone had gotten enough. Uh, the CFO was a public person. They went on into the internet and got enough of their, um, you know, their podcast just like this one they were doing and, and, and, and their, and their voice.
And they trained an AI model against that voice. And the AI model was able to emulate that voice to an incredibly realistic level and, you know, uh, convince that controller to send the funds out. So who do you blame in that situation? Right? You can't blame the CFO, you can't blame the control. The controller is doing what he's told, right?
So these are instances that are gonna become more and more prevalent as we, as this technology continues to improve. And it's something that I think it's gonna be extremely important for organizations to go figure out.
Alex: Couldn't agree more. So talk to me a little bit about, uh, we're gonna pivot a little bit and I wanna talk a little bit about ai, but AI in a positive light specifically, I wanna hear. How it is that transcripts is leveraging AI in its platform. Are there any specific AI based [00:13:00] technologies or methods that you're really bringing to bear when thinking about this other than the typical, you know, pat machine learning and, and different things like that?
What are you, what are you using these days that actually le is being leveraged in your, in your core work and platform?
Zain: Yeah. Um, so, you know, we don't use AI as much in, in our solution as we enable others to use AI on, on, on their own data.
Alex: Mm-hmm.
Zain: So one thing that we've started to do is we've enabled users to link their credentials, um, with their, and, and there's some, these new solutions that are coming out with their, uh, chat GPT and things like that so they can be able to a identify and, and, and prove that, you know, these credentials belong to me and then have AI trained on, on that data.
We don't really necessarily use machine learning and LMS within house, mostly because we don't have access to our user's data. Uh, so there isn't much to train on our end. That is by design. Um, and, and according to our mission. Um, but you know, really the thing that we, we think about and what keeps us up at night is.[00:14:00]
You know, we think verifications are the, you know, as a, as much a, as AI has amazing use cases and it's gonna help improve the world many times over. I think it's also important for companies like ours to exist that help prevent some of the bad use cases from AI from ever fostering. Right. Uh, the reality is it only takes a few of these bad cases for regulation to come in and to, to, to slow down innovation.
And, and our goal is to, you know, let's stop that from the, from the. From the get go and prevent regulation from coming in. And, and let's prevent as much fraud as we can, uh, as possible. And, and that's something that we're really focused on.
Alex: So, uh, you, you hit upon a term that I think is actually, uh, quite valuable here. And I think 'cause people confuse verification with fraud prevention, right? And they're actually two very different things, right? They, they, they work hand in hand. But I'd love to hear your, your thoughts, especially around fraud prevention, because it's such a central pillar of, of what you all do.
How does, how is it that ai right, you're, you're helping detect fraudulent activity [00:15:00] in the verification process using ai. How is it that's, that's coming to life?
Zain: Um, so, you know, for example, like let's say you're a, um, you're a hiring manager. Right. And you know, you get a thousand applications, uh, for your role, about 300 to 400 of those applications are gonna be fake. Um, and even more so if you look at that resumes that have, you know, um, to put it, to put, to, to put it nicely exaggerated information.
Right. Maybe a job title that isn't true, maybe a previous role that isn't true. Maybe a, a start and end date that isn't true. Right? Um. Our, our goal is how do we bring all that information out to light and help that hiring manager go through, you know, these thousands or a thousand in this instance, applications and go down to that 200 applicants that are actually, you know, legit, truthful, and, and you know, so that can help.
That's an 80% time saving. So these are things that we're actually like, these are new products that we haven't launched yet, but we're actually [00:16:00] experimenting internally with some of our HR team members. This, you know, how can we help prevent things like resume fraud? How can we improve, you know, timings on, you know, understanding?
Is this application legit? Is it not before the background check process, right? Because background checks cost money to take weeks to fulfill. So how, how can we ensure that so that when you go and actually. Offer these candidates interviews, you know, you have a lot more certainty that this person is legit and, and things like that.
So that, that, that's kind of an area that we're focused on. And, um, you know, you, you, you, you, you brought up a very interesting point. Uh, fraud prevention and verifications are synonymous. They don't mean the same thing. A verified person is a lot less likely to commit fraud. Um, so that's one of the ways that you can kind of.
If you can verify all the users or if you can verify all your employees, or if you can verify all the inbound job applications, uh, you're, you're more likely than not to get less fraud.
Alex: I, I'll tell you what you're, you're, what you're describing is a, a measure of efficiency that I, I know a lot of my friends in talent acquisition are looking [00:17:00] for. I've heard nothing but war stories about. Uh, to both sides of the equation, right? You hear the, the talent acquisition professional, the, the recruiter who, who sits there and says, oh my God, we're being spammed with all these fraudulent, uh, applications and all these different, uh, how do I get a thousand people applying in the course of, of a day for a job that just.
Typically shouldn't, shouldn't, uh, get that many applications. Right? And at the same time, I also hear the people who are the applicants saying, I, how do I prevent myself from being drowned out by all these things? Right? And so you see a lot of applicants doing one of two things. One is, how do I differentiate myself?
In an era where there is all this drowning out happening, and at the same time also, how do I make sure that I'm spamming the world with my own resume as much as possible so that I'm applying for jobs? All of it leads to inefficiency,
right? It leads to inefficiency and, and what a nightmare for, for employers as well as people who are, you know, legitimate verified candidates, as you described, trying to, trying to [00:18:00] achieve the dignity of employment, right?
So to speak.
Zain: Exactly. I, I think, you know, um, the whole hiring process is something that's gonna get revamped and it's probably already going through that revamping. It's just the old thing of here's a online portal, submit your resume. It doesn't work in the age of ai. And as these AI tools become more sophisticated, um.
You know, it's, it's just gonna become very difficult to be able to discern legitimate applicants from non-legitimate, uh, all the way down to fraudulent applicants as well.
Alex: Yeah, it's ripe for disruption to your point. I, I, I think it's absolutely critical there. So, you know, today we're in a world of, of AI generated resumes. Uh, what would you say are the biggest challenges that HR professionals and hiring managers. Uh, are, are, are sort of experiencing in these AI generated kind of resume reviews and then more importantly, what is it that you think that they can do to kinda leverage tech to, to overcome that?
Obviously transcripts, but what about other kind of forms of tech?
Zain: [00:19:00] Absolutely. So, you know, as someone who also leads HR at our very small company, um, you know, I, I can attest to that. Whenever we have a job application and, and we're hardly a, you know, a large company that gets probably large companies with brand names probably get many more times applications that we do.
But even in the smallest of postings, we get hundreds if not thousands of applicants. And it is extremely time consuming to go through it. And, you know, you there, there are ways. That exists to automated like keyword detection, things like that. Um, those are imperfect ways. You can ask any hiring manager.
You let go of a lot of good candidates, um, and you still sift in a lot of bad ones. So someone at the end of the day still has to go through these, at these applications. Some of the interesting things that I'm seeing now is actually using AI to counter ai. Um, so people are using, um, you know, I, I won't plug any specific names, but, uh, there are tools that exist that, uh, can go through these resumes for you and actually give you a much more comprehensive overview of what the candidates.
Um, you know, [00:20:00] job history, and it can even even click on external links. So if a candidate has their LinkedIn or their GitHub attached, they'll click onto those links and review those GitHubs and LinkedIns for you on your behalf. So you get a basically a, instead of a multi-page resume or even a one page resume, you get.
A blurb about this candidate that you can quickly review and approve or decline. So I think that's, those are some things that are pretty cool, right? Like how do you turn a, you know, long one page relatively dense resume into a couple sentences, uh, of just the key information that you're interested in. I think that's extremely, uh, potent and it can help save a lot of time, um, and, and get through a lot of that, you know, fluff that exists on resume.
Alex: You know, it's interesting. I, I see a lot of that fluff on resumes and I always wonder how it is that the algorithms are really picking up and teasing apart that, that fluff. Right. I, I, I say that 'cause I've, I've myself have run a mini experiment from time to time to say, okay, what do you see in this resume versus what do I see?
Meaning me, the human. And it's always fascinating how much I the [00:21:00] AI can pick up that I don't pick up. Right? Uh, and, and it, it's sort of funny. I, I remember I had a boss, I'll never forget this, uh, a former supervisor who used to weigh a resume and say, this is how I know this person has been prolific.
Right? That, and the university that they went to, those were the two criteria that they used. And I said, how do you know any of that is true? Right? And I, I, I can put together my, my vita. Right. Being a, the, uh, academic PhD in the room, right? My vita could be 83 pages long if I wanted to. Right? But how do you know what I actually contributed to that?
The, the, the 83 pages of fluff, right?
Zain: Exactly right. Uh, I, I think one of the big issues that I see is that, a lot of times with candidates, they'll work for some big company and they'll be like, help contribute. Going from zero to a hundred million in sales. You're like, wow, that sounds extremely impressive. And then you realize there were 35 other people on their team.
So it's like, yes, you were. But 1 of 35 people who've helped contribute to that. Right. So, it's a lot of getting through, to the, [00:22:00] core of what, their contribution is. and it's very interesting. It's very interesting to see how AI can say, Hey, you know what?
This is an interesting claim, but let's, flag it and let's keep it for, let's, scrutinize it a little bit more.
Alex: One other area that I think it's actually helped me significantly is actually in looking at this notion of the, the person who claims they're a strategist versus not a strategist, and led that strategy, to your point A, a team of 35 growing to, to 100 million is, is very different than somebody who's actually put together the entire strategy and then had somebody else execute and implement it.
Right? And so I, I myself have found that the, the AI is helpful at picking that apart.
Uh, in many cases by actually picking up on the keywords that somebody is, is using, or more importantly, also teasing apart what it is that they actually did from a performance perspective with a variety of different assets.
Right. Uh, yeah. Go ahead, sorry.
Zain: absolutely. And, and, and there's some, even some, there was one thing that my co-founder experimented with where, uh, we [00:23:00] went to some of, uh, these sales resources and created an actual org chart of the organization so you could actually even place. Where this, this employee was in the org charts, you can actually like really weigh, oh, you know, they were like, they were, you know, they were relatively junior employees, so they probably didn't contribute a whole lot to this particular initiative that was companywide, for example.
Right. So you can start understanding, and AI can do this relatively easily now with, with, with the agents in AgTech workflows.
Alex: So I'm gonna ask you a question here, and I'm gonna, I'm gonna ask you to put your futurist hat on, right? 'cause now we're, now we're, I want Zain, the futurist to be in the room with me here. How do you see verification of credentials and resume information or data in. Evolving as AI becomes more advanced and accessible.
Right. So like you just referenced AG agentic AI as an example, right? And I think about ag agentic AI at the data layer. I think about it at the orchestration layer. How is it that you see some of that evolving, uh, especially in, in the context of verification of credentials and resume data?
Zain: I almost don't see [00:24:00] any more digital interactions that occur with non verified users. So, you know, imagine your Slack channel. Imagine your Gmail. You wouldn't receive any more in inbound emails from anyone else, aside from people who have verified credentials. And the reason for that is that most, unless you sign up for a newsletter or whatnot, most people don't want to receive emails from, uh, chat GPT or, or, or some, um, you know, some spam that's using chat GPT to, to generate that email on their behalf.
I think it becomes increasingly important in organizations, right? We have organizations that are still very much remote or still have, even if they're in person, still have a good chunk of. Um, the day that's in the digital environment issue with any of those interactions in that digital environment is that you don't know if your counterparty is actually that party or not That party.
And like I mentioned with some of the deep fake examples, it can get really good with even emulating voice, how they talk, how they write messages, things like that. So I imagine in the future where, you know, misinformation and [00:25:00] disinformation and. And, uh, fake information all on the rise, you're gonna need to have verified profiles and verified credentials, um, to be able to be certain that the counterpart that you're dealing with in a digital transaction, whether that digital transaction be as simple as receiving or sending an email all the way out to obviously financial transactions, legal transactions, whatnot, are legitimate.
Um, so that's kind of where I see the future. I see. Um, and that's, you know, that that's one of the great conversations philosophically about ai. If AI makes everything much, if it makes it much harder for you to discern reality online, which it already is, right? There's a lot of videos about, you know, prominent people and they're, they sound very legit.
They look very legit, but then you find out that they're, they're not, they're fake. Um, it, it will create a, eventually create a world where you don't trust anything online and therefore people will only want to trust things that come from. People have to trust in reality. And the only way to curate and bridge that, that real trusted profile with the digital profile is [00:26:00] through verified credentials and digitally verified identities.
So that's kind of where I see the future heading. And um, I think it's gonna become something that's increasingly important, um, as the technology improves. And actually, like, not to sound too, um, doomsday, but I think the entire digital web. We'll actually fall apart if you cannot, um, if, if we can't solve this problem correctly, because the reality is no one will trust anything online.
It'll come to a point where anything that you read online, no one will trust it. And that's obviously not in a, not a good place to be.
Alex: So, you know, it's funny you mentioned that 'cause I, I like to practice and I like to talk in movies, right? Uh, the team knows this. My, the audience knows this. I like to talk in movies. So as far as I'm concerned, Skynet is coming. If it hasn't already come, I think it has. And, and I know that we're gonna be batteries in the matrix at some point, right?
Joking, of course. But one of the things that stands out is, I, I, I would love to hear your perspective, 'cause you just alluded to it, right? How do we kind of see and maintain a future? What changes do we need to see to really maintain that future where authenticity and trust. Remain at the [00:27:00] forefront. Right.
You, you, you highlighted that obviously in social world we're, we're seeing the, the notion of trust be redefined. It's, it's become a very different animal in terms of what we trust and what is fact, so to speak. Talk to me a little bit about what you see as far as technological changes and innovation that needs to happen to ensure that authenticity and trust are, are still paramount to, to employers and to others.
Zain: I think there has to be a disclaimer on whenever AI is being used or whenever an AI agent is, um, pretending to be a human being. I think you can have an internet where you have a bunch of these bots roaming around pretending to be humans.
That's an internet that doesn't work, right. Um, it's almost whenever we read that Instagram comment, whenever read a Reddit, you always think that, that there's a human being behind this. Um, it's increasingly becoming where there's a bot behind it. And it's easy to, you know, uh, spread misinformation or spread, um, you know, missed narratives or, or false narratives, uh, around certain things by creating those bots, uh, and bot [00:28:00] armies.
And we know that both state actors, corporations, they've all, they're all all been guilty of using these things. So I think there needs to be, these platforms need to come together and say. Like we'll flag whenever a, um, you know, a a a bot is pretending to be a user. It'll say bot next to it. Um, I think also with images and videos, I think there should be some sort of way, um, with within these, you know, video generation tools and audio generation tools for you to know that this is a AI generated audio to, for you to know that this is an AI generated video.
I think it's very important. Um, you know, it's. You need to flag these things so that people don't believe them. 'cause once people start believing fake information, then you start getting all kinds of very, very bad outcomes for society, um, that are actually very, like in, in a lot of political scientists and and whatnot, economics have talked about.
Situations where society starts believing in false narratives, it can lead to a, a lot of bad things happening. So I think this is something that's, it's extremely crucial. Um, also a [00:29:00] shameless plug, I think, you know, with, with the HR audience, you know, I think the first thing that you can do is incorporate transcripts in your flow so that your employees can actually own a verified credentials for they.
And we're gonna continue to work towards, uh, building out use cases where those employees can not only share those credentials for real life use cases like mortgages, future jobs. Um, responding to background checks, et cetera, but also for digital interactions as well.
Alex: Yeah. Well, I appreciate you taking the time to kind of share that perspective, that future. I, it, it almost gives me chills, right? Uh, just because I, I, I do think there's a, a kind of a concept that we all need to kind of. Wrap our heads around in terms of the notion of authenticity and, and really trust being a, a continual thing that we've gotta build within our tech stack.
Uh, so I'm gonna round us out here a little bit with one question. It's my curve ball that I ask everyone, uh, when, when I, when they, uh, are part of this, uh, this, this show and this audience, this podcast. One of the things that stands out to me is, uh, if you were in a room with the [00:30:00] top 50 CHROs. Of, uh, major corporations, small corporations, but the big 50 C CHROs, what kind of advice would you give them as far as what their focus should be, but more importantly, what they should be dealing with when thinking about applicant fraud and compliance issues?
Right? What, what is it that they're missing that they don't really address today or they haven't really thought about today?
Zain: I think the advice that I would give is that, you know, organizations, especially of that size tend to be slow moving. They're still operating under the previous paradigm, the pre-chat GPT paradigm that. A lot of what I read online is real. A lot of these resumes have actually been written by human beings, things like that.
Um, I think the thing that I'll say now is that we're, we're seeing, especially people in my age, and in my generation, is that, people in Gen Z and, younger, they're not writing their resumes anymore. They're putting five, six bullet points into ChatGPT and ChatGPT's writing that, stuff.
And they're not even. [00:31:00] reviewing the information that ChatGPT is spitting out. So even unintentionally they might be committing fraud because ChatGPT is gonna fill in the blanks with whatever sounds good, and then they're gonna submit that to, your job. I, think you have to really build, and understand that, in, this day and age, we have to be a little bit more, proactive with, countering that.
and, there's a, new generation of people who are AI first. And while that's good and all, you have to counter that with tools that can help really discern the truth from the fluff and the fluff from the fraud. And I think that's something that's gonna become increasingly important as as time goes on.
Alex: You know, when I talk to CHROs, the number one thing I talk to 'em about is data and understanding the signal from the noise. I like how you put it, the fluff from the fraud. I really appreciate that in trying to figure out what's actual, you know, truly verifiable and, and the kind of thing that keeps authenticity at the forefront.
To your point, uh, that's gonna do it for this episode, and I'm grateful. [00:32:00] Thank you so much, Zain, for sharing your expertise and deep insights with us this week.
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