AI Guru Strikes Again

[00:00:00] Yo Chad, what if I told you there's a platform that could completely revolutionize your

[00:00:06] hiring strategy in a matter of hours?

[00:00:10] Yeah, I'd call a bullshit.

[00:00:11] Well, it's not bullshit with AI for jobs powered by our friends at this way global.

[00:00:17] Okay, I'm listening.

[00:00:19] Well, everyone else is fishing in the same old talent pools.

[00:00:22] AI for jobs can source over 160 million diverse candidate profiles.

[00:00:29] This way global has established unique partnerships with over 8,500 trusted diversity partners.

[00:00:36] So wait a minute.

[00:00:37] All of the hard on the groundwork is already done.

[00:00:40] That's right cowboy, you can discover 300 qualified candidates per job rack instantly.

[00:00:46] It's like having a candidate sourcing magic wand.

[00:00:49] Dude, if you had a magic wand, you would have Mexican pizzas all day.

[00:00:57] Job distracting me so, Ash.

[00:00:59] AI for jobs advanced matching algorithm analyzes past applicants using trillions of historical

[00:01:05] matching events in over 1600 data points.

[00:01:09] Now that is what AI should be doing, saving recruiters time on sourcing while they provide

[00:01:15] a white glove candidate experience.

[00:01:18] Let's wrap this shit up.

[00:01:19] I'm hungry.

[00:01:20] Listen up kids.

[00:02:22] Yeah, it's Dante's favorite podcast aka the chat in cheese podcast.

[00:02:27] I'm your co-host Joel Cheezman.

[00:02:29] Join us always.

[00:02:31] The J to my silent Bob Chad so wash is in the house and we welcome guru.

[00:02:37] Sethu Pathy co-founder and CEO at Fair Now company who helps companies ensure that their

[00:02:42] AI solutions are compliant and well governed.

[00:02:46] Guru, welcome to the podcast.

[00:02:50] Joel Chad, good to talk to you guys again.

[00:02:54] It's been a few months.

[00:02:55] How are you?

[00:02:56] It is.

[00:02:57] Now make sure that everybody understands.

[00:02:58] Give us a little Twitter bio but how long did it take you to actually get your name legally

[00:03:03] changed to guru and how long is it going to take to be able to get AI as your middle

[00:03:09] name.

[00:03:10] So guru AI.

[00:03:11] I'm working on how long is it going to take?

[00:03:13] How long is it working on his doctorate in love so he could be love guru Sethu Pathy.

[00:03:17] No.

[00:03:18] Do you remember McLeven from back in the day?

[00:03:22] McGuru is coming.

[00:03:24] So give us a little Twitter bio for all the listeners who didn't hear the last episode

[00:03:28] you were on just a little bit a little bit of love around guru.

[00:03:32] Who is guru?

[00:03:33] Thank you guys.

[00:03:34] Yeah, absolutely.

[00:03:35] I am founder and CEO of fair now.

[00:03:39] Basically like Joel said, we help companies especially in the HR space but other spaces as

[00:03:43] well everyone's talking about AI right now.

[00:03:45] Everyone's wanting to build AI use AI.

[00:03:48] But as we all know it's also a little risky right?

[00:03:52] And so helping make sure that what you're putting out there is tested.

[00:03:56] It's compliant.

[00:03:57] It's fair.

[00:03:58] All of that stuff.

[00:03:59] You can share anything on the AI parade danger.

[00:04:01] Oh no, risk.

[00:04:02] No.

[00:04:03] Don't say that.

[00:04:04] Not at all.

[00:04:05] Is work day pounding down your door since they have a discrimination lawsuit right now?

[00:04:10] Yeah, that is a fascinating story.

[00:04:12] I don't know how many of your readers are familiar with this but yeah, a year ago a gentleman's

[00:04:17] sued work day and apparently the situation was he'd been rejected from dozens of job applicants

[00:04:24] that he applied for and said hey, work day was the common kind of filter in all of these

[00:04:28] job applications.

[00:04:29] Now what's interesting is a judge just in January throughout that lawsuit not saying

[00:04:34] that work day was fair or not fair or biased and he was not adjudicating on that but more

[00:04:39] like hey, what's work days role as part of the recruiting ecosystem here?

[00:04:44] They're not actually a hiring company right?

[00:04:48] But they've come back on February 20th.

[00:04:50] He refiled it and mended it and so the lawsuit is back on and so this is going to be one

[00:04:54] of the big test cases Chad in terms of what's going to happen in this space and so we're

[00:04:58] all waiting to see what happens.

[00:05:00] Well California, their first version of the regulations that they started posting out

[00:05:06] actually had vendors on the hook.

[00:05:09] So I mean do you see that actually as just kind of like the first draft and that'll

[00:05:13] follow away or do you think that vendors are actually going to be on the hook?

[00:05:18] The vendors and also the company.

[00:05:19] And employers.

[00:05:20] Yeah, I see both sides absolutely and it's not just California if you look at the EU AI

[00:05:24] act which is the big deal.

[00:05:25] I know we're going to talk about that in a few minutes.

[00:05:27] Let's do it.

[00:05:28] But that one New Jersey, New York State, California, all of them talk about both sides

[00:05:34] of the market.

[00:05:35] So absolutely.

[00:05:36] Do you service mostly the employer side or the vendor side like who's coming to you?

[00:05:39] Is it is it both or just one?

[00:05:42] I obviously both will eventually start knocking on your door but what is it right now?

[00:05:46] That's a good question because it's not what I would have guessed initially right?

[00:05:49] Because initially we went a little bit more after the employer side right because of

[00:05:52] the New York City law and other things.

[00:05:55] So who's actually been knocking on our door more?

[00:05:57] It's actually the vendors and here's why right because they are getting questions from

[00:06:03] their customers.

[00:06:04] And what does a vendor care about?

[00:06:05] They care about selling to customers and so if that's slowing down their sales cycle,

[00:06:10] questions are slowing down their sales cycle.

[00:06:12] They're like okay we got to find a way to answer these questions.

[00:06:15] So that's what's driving the inbound right now.

[00:06:18] Yeah well it makes a hell of a lot of sense.

[00:06:20] So listen, listen vendors.

[00:06:22] If you are fumbling, instumbling in bumbling over the answers around AI, you might want

[00:06:28] to call a guy like guru but question here though is we talked about work day they just

[00:06:34] acquired a hired score who Athena Carp who you might know she was amazing at explaining

[00:06:41] and defending what they were doing right?

[00:06:44] So do you think this is almost like work days way of saying okay we need AI because everybody

[00:06:50] everybody's getting AI.

[00:06:52] So we need AI but we need what everybody's calling here we go kids responsible AI that's

[00:06:58] right I used air quotes.

[00:07:00] Do you think that was pretty much like the bandaid it's like okay, we can get this covered

[00:07:03] if we get Athena her group and people who actually know what the hell they're doing in

[00:07:08] here.

[00:07:09] Do you think that was it?

[00:07:10] I have a hunch.

[00:07:11] I think you're on something there right?

[00:07:12] I think both in terms of the AI journey right?

[00:07:15] I think hired score has been around for a while and it's been quite innovative in the

[00:07:19] space of AI.

[00:07:21] I actually haven't gone deep on their product with Athena but I've talked to her about

[00:07:25] this she and I have had multiple conversations about responsible AI yeah and I found her

[00:07:28] to be one or more thoughtful people around this topic so I would not be surprised if

[00:07:32] both from an AI roadmap.

[00:07:35] Hire score was attractive but also kind of Athena and how she thinks about responsible

[00:07:38] AI I'm sure was very attractive to work.

[00:07:40] When you talk about these things it's normally a big company work day CVS the little guys

[00:07:46] out there have to be freaking out they don't have the resources to build this build responsible

[00:07:51] AI are you getting calls from them and what is that conversation like again it's about

[00:07:57] the vendor versus the employer right so on the vendor side we actually have small vendors

[00:08:03] right who are customers because again they're trying to sell to big companies yeah and

[00:08:08] so big companies are like who are you and how do I know your shit isn't biased right

[00:08:12] so then they cut then they're like all right we got to get through these call a sales

[00:08:16] cycle so on the vendor side it's big small medium are all kind of knocking on the door

[00:08:21] at all.

[00:08:22] On the employer side you're right it's the bigger companies because they're the ones

[00:08:26] were more likely to get sued right if you're a small medium sized company like yeah I don't

[00:08:30] I'm not surprised for the smaller guys versus bigger vendors yeah yeah I mean we're

[00:08:35] pretty you know it's a combination of like how many models do you have how complex are

[00:08:38] your models you know those kinds of things and so if you're a bigger vendor you're probably

[00:08:42] going to have a more complicated AI ecosystem.

[00:08:44] Well yeah I mean we just last last Friday talked about how higher view was an issue for CVS

[00:08:53] so CVS is getting sued because of the use of the higher view system at least that's

[00:09:00] how it's you know that's how drawn up so if you're a company well first and foremost if

[00:09:06] you're higher view you got to get your shit together you got to do it quick right yeah but

[00:09:11] even more so if you're a company especially like a brand like CVS you have to defend that

[00:09:19] brand I mean you have to be you have to do it and you have to be ahead of everybody

[00:09:26] else unfortunately they were not so are you seeing more

[00:09:30] companies being more thoughtful about okay we want to be able to we have to introduce AI

[00:09:37] into the process because if we don't we're going to be left behind so we have to be able

[00:09:40] to do it we've got to figure it out but how do we get people in who actually know what

[00:09:44] the hell they're doing.

[00:09:46] So you covered a lot there I want to also wants that hit on the air Canada situation

[00:09:51] to use you guys see that one another good example right where chatbot so you know they had

[00:09:56] a chatbot that's basically a customer facing chatbot and a customer asked them about their

[00:10:01] discount policies and it made something up basically and so the customer then went ahead

[00:10:06] buying a ticket assuming that to be true yeah and then found out it wasn't and there was

[00:10:11] like wait a second I was told explicitly x y z and then air Canada came back and was

[00:10:16] like oh no no that chatbot's a separate company.

[00:10:22] And that's not the judges rule that they were responsible in their live offer that so

[00:10:26] so these are the kinds of the trial and errors that we're going to see a lot of where

[00:10:30] companies try things they're going to mess up and then you know other companies will

[00:10:33] be like whoa we got to we got to be careful here so you're going to see a lot of that

[00:10:36] I think but you're absolutely right I think here you in this one of the things we've

[00:10:40] started building into our platform is actual testing of chatbots of lm's of gen ai testing

[00:10:47] it for bias testing it for hallucinations is it saying crazy stuff is it saying weird

[00:10:51] stuff right like we actually have some some capabilities to test around that so you

[00:10:55] can test that out before you you release it into the wild.

[00:10:57] Google's listening after their recent faux pas with gen i that's not even that isn't

[00:11:05] even a faux pa you're asking for something and you didn't get what you want so now

[00:11:08] you're bitching no there QA messed up they should have tested that better now that's

[00:11:13] that was very surprising guys very surprise company like Google.

[00:11:17] Their images I mean come on this is a little bit different than actually outcomes and

[00:11:22] screwing somebody over because they were a female and they played softball in the AI

[00:11:26] saw that that happened right this is hurting somebody versus not hurting somebody a little

[00:11:31] bit different so let's talk about the actual the actual regulations and the EU go figure

[00:11:38] is leading on this so tell us what the EU is doing yeah not surprising in a way right

[00:11:43] because the EU does operate a more top down fashion there is going to be like a multiple

[00:11:48] hundreds of pages kind of legislation yeah they've been working on it for a while so where

[00:11:53] we've landed since we last had it so back in December there was an informal kind of agreement

[00:11:58] around the contours of the legislation what's happening now dribs and drabs of it have

[00:12:03] been released and in April I believe they're doing a final formal vote on it right and then

[00:12:09] I think 20 days after that it goes into effect quote unquote I put that I put that in quotes

[00:12:14] because companies will have time right so it's kind of a lagged effect you'll have six months

[00:12:19] to get certain things in order you'll have a year to get kind of your bias testing in order

[00:12:23] you'll have a year and a half or two years to get your reporting infrastructure and governance

[00:12:28] layer in order right so there's different components to it and there's different time

[00:12:31] lags to each of these things but it's happening it's happening and the fines are huge right

[00:12:37] up to up to six percent of your annual revenue so this is not like the New York City law on

[00:12:43] any way shape or form the New York City law wrote a blog post on this ended up having no teeth

[00:12:47] behind it and part of it was the fine was five hundred dollars per I mean what why am I exactly

[00:12:58] and it was very narrow it was focused on AEDTs which are automated employment decisioning

[00:13:04] tools for hiring so only and promotion but only if it's automated completely automated so if

[00:13:08] you could easily come in and say oh no my humans in the loop it's not automated and get around

[00:13:12] it right the EUA act is much more broad it even outlines kind of eight high risk areas

[00:13:18] of which guys just so your audience knows HR is one of the eight high risk areas right so HR

[00:13:24] is going to be in the crosshairs of this legislation yeah very consistent and I think California

[00:13:30] New York state others are going to have HR in the crosshairs as well so I think HR financial services

[00:13:35] and health are gonna be three of the domains that are gonna be in the crosshairs of all of the AI

[00:13:41] regulations going on. One thing's the mention is the importance of upscaling so in that time window

[00:13:46] company should be preparing building the skills internally like talk about how company should

[00:13:52] view it upscaling as opposed to just maybe hiring a company like yours or should they do both

[00:13:57] how do I prepare they should do both right because we're more of a platform company or a technology

[00:14:02] company you're still going to need humans in the loop and making kind of really thoughtful decisions

[00:14:08] around risk reward trade-offs right the thing that I say is like look at the end of the day no one

[00:14:13] is trying to reduce risk down to zero right if you were trying to take risk down to zero you never

[00:14:18] leave your bed you'd never leave your house right so that's not how we operate right that's not

[00:14:22] how humans operate that's not how businesses operate but you need to know enough of the calculations

[00:14:27] to understand okay what's the risk of this technology what's the reward and are

[00:14:31] rec comfortable with it so you need both people at the junior levels who understand governance

[00:14:36] but think you need senior stakeholders who really ultimately need to make these calls at a

[00:14:40] company wide level what is our transparency policy what is our what is our red line let me give

[00:14:46] you an example right if you're thinking about something like predicting attrition at the individual

[00:14:51] level right a lot of companies have tried this I've you know my teams have built stuff like this

[00:14:56] it's a little bit minority report us right if I can predict that Joel will leave his company with 70

[00:15:02] percent probability okay what do I do with that information do I throw more money at him

[00:15:07] do I try to keep him what if Chad's about to leave with 67 percent problem what's the difference

[00:15:11] between seven I mean you start to get into situations where like you don't know how to use these insights

[00:15:16] and what to do with it and then people can start gaming it so it's actually there's a really

[00:15:19] interesting ecosystem around some of these insights around AI and so that's where you have to as a

[00:15:25] company say okay we're not going to use it for that purpose it doesn't make sense it's not going

[00:15:29] to drive value and it's going to break trust so these are the really intricate conversations that

[00:15:33] you need to be having but to do that you need to have the talent that understands this at the

[00:15:38] detail level yeah and at the philosophical level right and that's there needs to be upscaled

[00:15:42] let's talk about risk real quick because I think this is the risk that companies are going to care

[00:15:46] about the most if the workday suit was brought in the EU 6 percent of their global revenue equals

[00:15:55] 420 million dollars for a work day that is a risk they care about right absolutely absolutely

[00:16:05] I think there's two risks I think that one that's a pecuniary fine related risk now I don't

[00:16:10] think it'll be that high right it'll be up to that amount and it'll be but it'll be significant

[00:16:15] right the reputational risk matters okay listener how can you help your employees become more

[00:16:24] productive I have answers how about automating manual and repetitive tasks giving meaning to data

[00:16:31] then allowing that data to actually drive decisions and how about matching people to your jobs quicker

[00:16:38] well wait the chat and cheese has a new llm no cheeseman I'm talking about text kernel

[00:16:46] okay that makes more sense what I'm hearing is the groundbreaking concept of wait for it yeah

[00:16:53] simplicity seriously though seriously text kernel cuts through the complexities like a tortilla chip

[00:17:02] through some hot nacho cheese really not your references already anyways text kernel brings

[00:17:08] efficiency and productivity to your operations text kernel seamlessly unifies your tools and data

[00:17:15] to drive efficiencies and success text kernel is creating new opportunities for your recruitment journey

[00:17:24] kind of like adding guac to my barbacoa burrito oh my god how about extracting meaningful insights

[00:17:30] from data I mean that that's something swiftly matching people with jobs automating repetitive tasks

[00:17:38] who knew such advanced concepts were even possible in the land of human resources uh we did Chad

[00:17:47] we did dude wrap it up I'm a little hungry imagine that okay listener get ready to use today's

[00:17:54] tech to drive efficiencies and productivity visit text kernel dot com that's txt keur n el dot com

[00:18:06] nachos right like reputational risk matters if if you're losing customers or candidates because of

[00:18:17] some perception around how you treat um your technology or how little you've tested it and those kinds

[00:18:23] of things that was a little harder to measure in terms of dollars and cents Chad but it can add up to

[00:18:28] be even larger than that amount right yeah especially from a revenue standpoint right I mean it's not

[00:18:33] only the fine but the perspective of lost revenue I mean optics obviously at all factors in there

[00:18:39] now as you were talking about some of the things that you're going to have to do with the e like

[00:18:44] evaluating the impact of AI right and then also ongoing monitoring what is what does it even look

[00:18:52] so that's where kind of you know we come in and help right like what does that look like

[00:18:56] and when you're talking about ongoing monitoring that's each if you're doing this manually

[00:19:01] that ends up being you to hire like a big old data science team right and how many HL

[00:19:06] organizations have the capabilities to hire a big data science team right and then on top of that

[00:19:10] they're going to be like wait a second I thought you told me AI is going to reduce my cost now I

[00:19:14] got to go hire a big data so um this is where it gets tricky but that's the beauty of kind of technology

[00:19:19] like what we're building where we can automate this right like we have expertise in this we've

[00:19:23] done this for many many years we know how to automate it we know the ins and out we know the details

[00:19:27] and I think that can go a long way to helping companies again leverage the technology in a positive

[00:19:32] way which again I'm super bullish on right I think I showed this analogy with you guys last time

[00:19:36] like AI like cars are an incredible technology transform our societies but the same time you've

[00:19:41] got to have breaks you gotta have a dashboard you gotta have rearview mirrors otherwise what do

[00:19:45] you drive in right you're not defmobile right so just put those things in place and then go fast

[00:19:51] right you talk about the EU um and as I listed this I just I realized how complicated and complex

[00:19:57] this is going to be and I think about Europe well yeah apparently the UK is gonna have their own sort

[00:20:03] of set of of AI regulations talk about what they're doing and how it's different or what do

[00:20:08] you expect them to do and how it might be different from the EU and the US I think part of is again

[00:20:14] one of the insights that we have is this is going to be very patchworky each country and region

[00:20:20] and state is gonna have their own in fact they're kind of competing I've talked to folks

[00:20:24] in various state offices we're like oh we got it what's that other state doing oh what's

[00:20:29] that state doing well we got it you know exactly so there's going to be that element of some

[00:20:33] competition amongst the regulators here um that being said there is some common themes and maybe

[00:20:39] that's what you're getting at a little bit the common themes are around hey you got to evaluate and

[00:20:43] monitor your models right like that's just the thing right especially on a couple dimensions one is

[00:20:50] bias that always comes up right so evaluate bias the other dimensions that come up are around

[00:20:55] hate transparency and explainability can you explain what your model is if it's a black box

[00:21:00] that's kind of a problem right especially in the hiring space and you know lending space right

[00:21:05] healthcare right like these spaces you can't just be like oh yeah we just rejected your loan good

[00:21:10] luck we don't know why right you can't you can't do that so things like bias explainability are

[00:21:16] going to be really really common in these legislations and then when it comes to gen AI you're talking

[00:21:21] things like data privacy and security can someone hack into your gen AI right there's all this stuff

[00:21:26] you guys are probably reading out like poison attacks I don't know if your audience has heard that

[00:21:29] but that's where you kind of penetrate the system and basically inject it with kind of your own

[00:21:35] prompts and almost teach it to be a bad kid right and then you can do whatever you know you can

[00:21:41] do nefarious things right so there's a lot of security related things data privacy related things

[00:21:45] and so you see kind of some of these common themes across the the different laws and legislations

[00:21:51] I think it makes it much harder because deep learning is a black box and in almost every case that's

[00:21:57] out there if you take a look at some of these models they're off learning by themselves it wasn't

[00:22:02] something that was programmed it was something that was a part of the process now the data that's

[00:22:08] entirely different what data are you training off of and I think that's where you get companies

[00:22:13] like higher score which was smart buy for workday where they know exactly what their data sets are

[00:22:20] right they know exact and that's the secret sauce right so from the standpoint of a lot of people

[00:22:26] are pointing to the AI piece which I obviously that's going to be the output you know the learning

[00:22:32] and output but isn't the data just as if not more important in this in this whole scheme of

[00:22:40] things it's more important it's not as it's not even the same it's more important in fact I don't

[00:22:46] know how much you follow like the the whole competition between meta and open AI and Google

[00:22:53] and all the right like in terms of the foundation one of the things you might be noticing is they're

[00:22:56] all kind of converging right their performance is at least right and it's because the modeling techniques

[00:23:04] are kind of known now right amongst these like top level data scientists at these organizations

[00:23:09] they know what the best practices are they know it's Google published this famous paper

[00:23:14] call attention is all you need or something like that which was groundbreaking paper in this

[00:23:18] gen AI space and then other companies copied it right so a lot of that is not where you differentiate

[00:23:23] yourself were you differentiate why everyone thinks eventually Google will get back on the right

[00:23:28] track and win this if they have the most and best data out of anyone in the world right and so at

[00:23:34] the end of the day we still expect Google to kind of you know be at the top of this AI race but to your

[00:23:39] point that's what differentiates it right your ability to know and have the best data and understand

[00:23:45] that data really really well and then be able to put it in a way that it's well governed right if

[00:23:50] you cannot make these mistakes if you have amazing data and not make these mistakes you're going

[00:23:54] to be out of the game America is unique in that it has a 50 state setup to where everyone kind

[00:24:01] of makes their own rules talk a little about the current state of what certain states are doing

[00:24:07] we've got California with SB 1047 New York local law 144 anything else that we should be looking

[00:24:15] into the future for different states doing unique things around AI it's chaos it's all chaos

[00:24:21] it's cats and dogs living together states gonna have and imagine how complicated that's going to be

[00:24:27] but then again you know our tax system is a little bit like that right like each you have a

[00:24:31] federal tax system and set up and then each state has their own specific tax policies

[00:24:38] tax rates tax discounts for this thing versus that thing and so you end up then building an

[00:24:44] ecosystem of people who specialize in that stuff right and that's what happens with regulation

[00:24:49] anytime you have a regulation then you have an ecosystem of people lawyers and experts and consultants

[00:24:54] who then start specializing so I expect that to be the case Joel here where you're going to have

[00:24:59] that kind of regulatory ecosystem so you have California New York state is separate from the New

[00:25:06] York City local law Joel so there's going to there is a New York state law as well

[00:25:10] Maryland is working on one right now New Jersey has one and then there's a bevy of other states

[00:25:16] Massachusetts main Colorado has one that's focused on its fascinating insurance companies right

[00:25:21] now so that's a more targeted for the domain the other ones that I mentioned New York state

[00:25:28] and California in particular are much broader right so that's going to be the interesting pieces like

[00:25:33] some of these are going to be quite broad and some of them are going to be kind of domain specific

[00:25:36] and so we're going to see how that plays out similar taxes we have local taxes we have state taxes

[00:25:40] we have federal taxes and the federal kind of trumps all that and we talked a little bit about politics

[00:25:45] in the green room I was surprised to see the FCC come out with laws so quickly around robo calls

[00:25:52] you know Joe Biden famously recently called people it wasn't him but he was saying don't vote it

[00:25:58] so I was deemed Phillips wasn't it I mean that was the other fucking Democrat no

[00:26:02] no it wasn't him it was someone on its campaign it was someone who used to work for

[00:26:06] digging yeah yeah yeah I'm sure there's stuff we don't even know what's going on but I was I was

[00:26:10] I was impressed to see the FCC move so fast to make this illegal which tells me and I commented

[00:26:15] recently that politicians are freaking out thinking that their voice could be out there saying

[00:26:20] well who knows what so what you're taking on the feds coming down with okay everyone here are the

[00:26:26] rules all the states are going to follow it are we gonna are we're gonna stay in this sort of

[00:26:31] disparate state rules can the Fed come in and make sense of all this in your opinion I think it

[00:26:36] can on certain areas right and they have if you saw the White House EO I think that came out right

[00:26:41] around Halloween of 2023 there are a couple of areas where the federal government does have domain

[00:26:47] right and when it comes to things like national security or things like kind of our democracy right

[00:26:53] like that ends up being kind of the federal government's domain and it's actually kind of concerning

[00:26:58] and I think they put they they went moved fast because we are we talked about this Joel we're in

[00:27:02] an election cycle right now right so it's not something they can wait six months to do

[00:27:07] and you guys have seen it this can be crazy right like it's it's the voice stuff it's also the

[00:27:11] image stuff it's the videos like you can make anything now and you saw Sora have you guys played around

[00:27:17] the Sora which is I mean isn't it movies right you can yeah right yeah that's the one right

[00:27:26] and you can put people in there you can put I can put Chad in one of those movies and have him

[00:27:30] doing stuff he's never done right in an actuality right and so no ideas Cheezman I won't take

[00:27:35] the podcast on that we got Guru on the show I'm not gonna I'm not gonna muddy it up don't worry

[00:27:41] don't worry Guru's too high class for that um so that stuff is now and and so I completely

[00:27:48] understand why they came in to try to like curtail that right now that being said other things like

[00:27:53] hey how do you want to govern software in the hiring space or the lending space you know now the

[00:27:58] thing is there's already one other thing I do want to say and keep talks about this a lot

[00:28:03] there's already federal legislation involved in lending and in hiring and in health right like we

[00:28:09] already have like there's rules already right so in hiring the EOC like we have like there's this

[00:28:15] um chapter title seven of the civil rights act where you cannot be biased and it doesn't say

[00:28:20] humans can't be biased or AI can be but it doesn't say anything so you are so already companies

[00:28:25] are absorbing that risk without really thinking about it and so they should be proactively instead

[00:28:31] of waiting for additional AI legislation they should already be on top of this saying hey we need

[00:28:35] to know what's going on we've talked about this for for shit years now with AI right it scales decision

[00:28:43] making right and it scales bad decision making it's not that humans haven't been making bad decisions

[00:28:50] we have it's not that humans haven't been biased oh we're so biased right but now the AI is taking

[00:28:57] that bias behavioral data some of those decisions some of the quote unquote predictors that some

[00:29:02] of these companies use and it's scaling it so now if you're a company who got away with bias

[00:29:10] and all those things before this is going to make all of that scale and it's gonna make it much easier

[00:29:16] to identify who's fucking around 100 percent I think you read on both of those accounts it's much

[00:29:22] easier to scale bad biases and bad kind of decision making the same time it's also going to be easier

[00:29:31] to sue and I think that's where you are going with your second point if I looked at that correctly

[00:29:36] in that it you know exactly where the problems it's in that all easier to see though because it's

[00:29:41] gonna be the the problems not gonna be small because the scale was small scales gonna be much larger

[00:29:47] so therefore the problem is gonna grow with the scale and you're it's gonna be much easier to

[00:29:51] identify so therefore you're gonna have more people pointing and saying oh look there's a lawsuit

[00:29:56] are you struggling to attract the talent you need today do you lack visibility into where your

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[00:30:44] at acquire roi.com and start transforming your talent acquisition today but I think all of that is true

[00:30:49] I think you're more likely it's more gonna be more easy to sue more easy to have a lawsuit at the same

[00:30:53] time though let me say it's gonna be easier to monitor as well yeah right because that's what I'm

[00:30:57] gonna my point being right when you have 50 different recruiters right and three of them aren't

[00:31:03] entering the data in the system and like six others like or what you know what I mean it's just hard

[00:31:09] to know what's going on but with the model in place it's actually easy to monitor and you can

[00:31:15] automate the monitor and the thing is this has been going on for a while like I talked to you about

[00:31:18] capital we come from capital one capital and monitors its models like all constantly it's not hard

[00:31:23] and so HR just needs to build these practices in they just need to kind of have that mindset

[00:31:27] adjustment and say hey look we're starting to become a technology domain a data driven domain

[00:31:33] we need to then along with that build these other practices in place it's easier to monitor a model

[00:31:38] than it is 50 humans remote work obviously in the last few years has become incredibly popular

[00:31:43] and we've seen sites that are platforms for managing a diverse global workforce the remote

[00:31:50] the oysters the deals etc it seems to me like recruiting on a global basis would open your company

[00:31:57] up to a lot of risk in terms of AI hiring going back to these different laws in different countries

[00:32:02] and states and all over the all over the map how do you how confident are you that these platforms

[00:32:09] are covering companies' asses do you feel like companies are reluctant to go out on a global scale

[00:32:16] because of the risk like I'm just curious your thoughts on the global state of things and AI

[00:32:21] recruiting you know when you were talking about that maybe just think of this thing that got past in

[00:32:26] India I don't know if you guys saw this recently but any company that uses AI in India for anything

[00:32:32] has to get like a federal approval which is kind of crazy to think about I mean imagine something

[00:32:38] like that passing here it wouldn't right but that's a pretty ownerist thing so to your point Joel

[00:32:43] now like hey what if you are using what if your multinational company and you use AI in your

[00:32:49] hiring practices just generally right and India's one of your places and wait a second now for India

[00:32:55] I got to go get special approval and then from right like so you're so this is the thing that's

[00:32:59] going to be hard to manage right it's like and this is what I meant by that you're going to have to

[00:33:03] hire some policy folks some lobbyists you're gonna probably hire legal folks compliance right like

[00:33:08] you're gonna start to have to have this kind of ecosystem of people to think about how these

[00:33:13] things affect you right and so that's going to be a bit of a challenge I totally agree

[00:33:19] so if I'm I'm a company I'm out there this is carrying the shit out of me go figure I'm seeing

[00:33:24] workday I'm seeing CVS I'm seeing all these big brands that are getting thrown all over the place

[00:33:29] is it feasible that I can do my business without even using AI especially when we're talking about

[00:33:35] hiring in a very competitive market for a short time right for today for tomorrow for this next

[00:33:41] month for this next year and so at the end of the day then it comes to like how forward thinking

[00:33:45] our CHROs is what it comes down to right and if you are kind of a short term operational CHRO

[00:33:52] you're like hey that's not my problem right we're gonna be fine for this year I'm focused on

[00:33:56] this year I'm focused on this quarter I got to cost this quarter like you know so there's different

[00:34:00] types of CHROs that have come across Chad the ones that are strategic visionary forward thinking

[00:34:06] absolutely not absolutely not right they know AI in fact there's so many opportunities in HR like

[00:34:13] HR is like the perfect domain actually there's so many opportunities to use Gen AI capabilities natural

[00:34:19] language processing capabilities predictive analytics capabilities right I was talking to a company

[00:34:25] the other day where they still have each recruiter manually at least check the resumes that come in

[00:34:32] and apparently a hundred thousand resume applicants last year and I was just shocked I'm like really in 2024

[00:34:38] you know what I mean that that's just super inefficient right like you can't compete doing that

[00:34:45] again you can do it for next week you can do it for next quarter but like in 2025 2026 how are you

[00:34:50] holding your you know your cost down how are you innovating things like your employees every single

[00:34:56] employee has a question about I remember I had so many questions around vacation days policies all

[00:35:01] of that stuff you can automate all of that there's so many opportunities and this is I think it's

[00:35:06] some of the fun part of the conversation like hey how can you use AI to create value in HR

[00:35:11] and there's really a lot of opportunities so for the ones the companies and the CHROs that are

[00:35:15] forward thinking they're absolutely not going to shy away from it I keep coming back to your

[00:35:20] different distates different laws and how disparate this gets just curious about your industry typically

[00:35:27] there's a there's a coconut Pepsi and then a bunch of fans as a doctor peppers like where does

[00:35:32] where does this go do we have like local accountants where people are AI sort of regulation experts

[00:35:38] in your local market and it's a few people like your state farm agent is it a big are there a couple

[00:35:44] big players and it's all like software and we know all the answers and all the states in real time

[00:35:50] is it both of those things like how do you think this this industry evolves from one of the pioneers

[00:35:56] I think there's some parallels to if you think about it there's already compliance in HR related

[00:36:02] to hiring put a put aside AI and bias right look let's look I'm not talking about that but when you

[00:36:07] hire if I go higher in Canada if I go higher in Europe if I go higher in Asia there are local

[00:36:14] laws that I have to follow to do that hiring right compliance all the stuff from from how you

[00:36:20] interact with them to how you onboard them the contracts the pay the salary the level all that stop

[00:36:27] and so you have these companies payroll companies that's how they got started right was like hey

[00:36:32] you're going to go higher over there we'll help you we'll deal with all the compliance we'll take

[00:36:35] care of all that for you right and so in a sense it's a little bit of like what we're trying to do

[00:36:39] and others are going to try to do as well which is like hey we are going to become experts like

[00:36:43] one of the we're partnering with law firms for instance right and so we're going to be experts

[00:36:47] on that stuff and kind of take that off your plate and that's what you know like a deal does right

[00:36:51] or that's what like a day force right like they kind of they take that off of your plate

[00:36:57] and I think you're going to see a little bit of that kind of model when it comes to kind of AI

[00:37:01] regulations too so in talking to our friend friend of the show EEOC commissioner

[00:37:08] Saundirling he had mentioned over a year ago that there's more than likely going to be an ISO

[00:37:16] around this the international organization for standardizations more than likely that's going

[00:37:20] to be a standardization that everybody can kind of flow into so it's not so chaotic

[00:37:26] and it seems like we're on our way there can you talk about that a little bit I can I agree

[00:37:32] and I disagree and so where I agree is yeah there's going to be a standard and it's here ISO 42,000

[00:37:37] one was released late last year now it's still I think in the process of like there could be some

[00:37:42] amendments some revisions and that kind of thing and they're still there's still a process to be

[00:37:46] worked through there but realize there's a difference between market standards and regulations

[00:37:51] right a market standard is great right it tells you that like your product has a certain level of

[00:37:56] quality certain level of like certification that you can take for granted right a regulation still

[00:38:02] like I still got even if I have the standard I still got to go satisfy that regulation if I don't

[00:38:06] want to get fined if I don't want to get on it's make it easier for regulators though to be able

[00:38:11] to take a look at a standard and say okay that's a standard we're going to get behind that you have

[00:38:16] to do that so it makes it much easier for a bunch of individuals a bunch of 80-year-old

[00:38:22] individuals who they don't know a smart phone they've got a jitterbug phone right they don't have

[00:38:28] a smartphone so at the end of the day is this not like a smart way to just say oh wait a minute

[00:38:32] those guys seem to have their shit pulled together let's just go ahead and jump on that train

[00:38:37] there's absolutely truth to that and in fact a lot of the private sector companies are pushing

[00:38:41] for that they're saying hey guys white house you know whatever let's develop market standards

[00:38:46] let's have public private partnership let's work together and let's develop these standards and

[00:38:51] let those standards be the things that provide the kind of common ground around this stuff right

[00:38:57] and there's some truth to that like if you look at the White House EO again the White House

[00:39:01] what the EO says is they put the NIST national Institute of Standards and Technology

[00:39:05] standards and technology right in charge of like developing some of those standards so

[00:39:10] if you look at it in the US that's kind of our approach now look the states at the end of the day

[00:39:13] are going to do their own thing right so the White House can't govern that but from a White House

[00:39:17] perspective I do think other than things related to national security which are very important

[00:39:22] right and they they own the domain around that but otherwise they that's kind of what they're saying

[00:39:27] Chad they're saying hey let's this let's build some standards NIST you kind of you know we give

[00:39:31] you power to run with this work with private sector work with others and develop standards and

[00:39:36] let's let's let's go with that so I think there's absolutely some truth to that

[00:39:40] that is guru Seth Upathy everybody hear your first listeners that want to know more about you

[00:39:46] maybe they can keep up with some of this stuff where would you send them to learn more

[00:39:50] couple sources follow me on LinkedIn follow fair now on LinkedIn we're both myself my account and

[00:39:56] fair now's account we're publishing a lot on these topics whenever there is whether it's a lawsuit

[00:40:01] whether there's a company that's put out something that we think we disagree with or agree with

[00:40:07] or laws and legislations as we've been talking about we'll be posting on there so please follow

[00:40:11] us as well as our website so we have a whole area where we talk specifically about legislations

[00:40:16] about market standards and we talk about what's coming down the pipe and what's in these things

[00:40:20] the EU AI Act we have a whole page on who's affected what's going to happen and all that stuff so

[00:40:27] between those two spaces you should be able to learn story short guru's a busy guy

[00:40:32] the Chad and cheese podcast has a bat phone to guru okay so you can always listen to us but yes

[00:40:38] definitely go to fair now yeah thanks thanks for keeping us up to speed guru this is a complicated

[00:40:44] issue Chad that's another one in the can we out we out thank you for listening to what's

[00:40:52] a called a podcast the chat the cheese friend they talk about recruiting they talk about technology

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