AI Guru Strikes Again

[00:00:00] Hi, it's your kids. Lock the doors! You're listening to HR's most dangerous podcast. Chad Soosh and Joel Cheeseman are here to punch the recruiting industry right where hers.

[00:00:11] Complete with breaking news, brush opinion, and loads of snark. Bop-a-lop boys and girls. It's time for the Chabin-Gee's podcast.

[00:00:30] Oh, yeah. It's Dante's favorite podcast, aka the Chad and Cheese podcast. I'm your co-host Joel Cheeseman. Join us always. The J to my silent Bob Chad Soosh is in the house. And we welcome Guru, Sethu Pathie, co-founder and CEO at Fair Now company who helps companies ensure that they're AI solutions.

[00:00:59] Our compliant and well governed Guru. Welcome to the podcast.

[00:01:05] Joel Chad good to talk to you guys again. It's been a few months. How are you?

[00:01:12] It is it is now make sure that everybody understands give us a little Twitter bio, but how long did it take you to actually get your name legally changed to Guru and how long is it going to take to be able to get AI as your middle name.

[00:01:26] So Guru AI that's what I'm working on. How long is working on his doctorate in love so he could be love Guru Sethu Pathie, no?

[00:01:34] Do you remember McLeven from back in the day?

[00:01:36] I was already.

[00:01:37] Yeah.

[00:01:38] McGuru is coming.

[00:01:41] So give us a little Twitter bio for all the listeners who didn't hear the last episode you were on just a little little bit a little bit of love around Guru. Who is Guru?

[00:01:50] Thank you guys. Yeah, absolutely. So I am founder and CEO of Fair Now basically like Joel said we help companies especially in the HR space but other spaces as well everyone's talking about AI right now everyone's wanting to build AI use AI

[00:02:05] But as we all know it's also a little risky right and so helping make sure that what you're putting out there is tested it's compliant it's fair all of that.

[00:02:16] Yeah, I parade danger. Oh no risk. No, don't say that is work day pounding down your door since they have a discrimination lawsuit right now.

[00:02:27] Yeah, that is a fascinating story. I don't know how many of your readers are familiar with this but yeah a year ago a gentleman sued work day and apparently the situation was he'd been you know rejected from dozens of job applicants that he applied for

[00:02:42] and said hey work day was the common kind of filter in all these job applications. Now what's interesting is a judge just in January throughout that lawsuit not saying that you know work day was fair or not fair or biased and was not adjudicating on that but more like hey what's work days role as a

[00:02:59] work as part of the recruiting ecosystem here they're not like actually a hiring company right. Uh-huh but he he he've come back on February 20th he refiled it amended it and so the lawsuits back on and so this is going to be one of the big test cases Chad in terms of

[00:03:13] what's going to happen in this space and so we're waiting to see what happens well California they're there their first version of the regulations that they started pushing out

[00:03:23] actually had vendors on the hook so I mean do you see that actually as just kind of like the first draft and that'll follow away or do you think that vendors are actually going to be on the hook.

[00:03:35] The vendors and also the company and employers yeah I see I see both sides absolutely and it's not just California if you look at the EU AI act which is the big deal I know we're going to talk about that in a few minutes let's do it but that one New Jersey New York state California all of them talk about both sides of the market so absolutely

[00:03:52] do you service mostly the employer side or the vendor side like who's coming to you is it is it both or just one I obviously both will eventually start knocking on your door but what is it right now.

[00:04:02] That's a good question because it's not what I would have guessed initially right because initially we went a little bit more after the employer side right because of the New York City law and other things but who's actually been knocking on our door more it's actually the vendors

[00:04:15] and here's why right because they are getting cut questions from their customers and what does a vendor care about they care about selling to customers and so if that's slowing down their sales cycle questions are slowing down their sales cycle like okay we got to find a way to answer these questions.

[00:04:32] So that's what's that's what's driving the inbound right now yeah well it makes a hell of a lot of sense so listen listen vendors if you were fumbling in stumbling in bumbling over the answers around AI you

[00:04:44] might want to call a guy like guru but the question here though as we talked about work day they just acquired hired score who Athena Carp who you might know she was amazing at explaining and defending what they were doing right so do you think this is almost like work days way of saying okay we need AI because everybody everybody's getting AI so we need AI but we need

[00:05:10] what everybody's calling here we go kids responsible AI that's right I used air quotes it do you think that was pretty much like the bandaid it's like okay we can get this covered if we get Athena her group and people who actually know what the hell they're doing in here do you think

[00:05:26] do you think that was it I have a hunch I think your on something there right I think both in terms of the AI journey right I think higher score what has been around for a while and they've been quite innovative in the space of AI.

[00:05:37] I actually haven't gone deep on their product with Athena but I've talked to her about this she and I've had multiple conversations about responsible AI yeah and I found her to be one or more thoughtful people's around this topic so I would not be surprised if both from an AI road map

[00:05:51] higher score was attractive but also kind of Athena and how she thinks about responsible AI I'm sure was very attractive to work when you talk about these things it's normally a big company work day CVS the little guys out there have to be freaking out they don't have the resources to build this build responsible AI are you getting calls from them and what is that conversation like again it's about the vendor versus the employer right so on the vendor side we actually have small vendors right who are

[00:06:20] customers because again they're trying to sell to big companies yeah and so big companies are like who are you and how do I know your shit isn't biased right so then they cut them they're like all right we got to get through these call a sale cycle so on the vendor side it's big small medium are all kind of knocking on

[00:06:38] the door jewel on the employer side you're right it's the bigger companies because they're the ones were more likely to get sued right if you're a small medium size company

[00:06:46] like yeah I don't how are you priced for the smaller guys versus bigger vendors yeah yeah I mean it we're pressing you know it's a it's a combination of like how many models do you have how complex are your models you know those kinds of things and so if you're a bigger vendor you're probably going to have a more complicated AI ecosystem well yeah I mean we just

[00:07:02] last last Friday talked about how higher view was an issue for CVS so CVS is getting sued because of the use of the higher view system at least that's how it's you know that's how drawn up so if you're a company well first and

[00:07:23] foremost if you're higher view you got to get your shit together you got to do it quick right yeah but but even more so if you're a

[00:07:29] company especially like a brand like CVS you have to defend that brand I mean you have to you have to be you have to do it and you have to be a head

[00:07:42] of everybody else unfortunately they were not so are you seeing more companies being more thoughtful about okay we want to be able to we have to introduce AI into the process because if we don't

[00:07:55] we're going to be left behind so we have to be able to do it we've got to figure it out but how do we get people in who actually know what the hell they're doing so you

[00:08:04] covered a lot there I want all of it once that hit on the air Canada situation to use you guys see that one that's another good example right where chatbot so you know they had a chatbot

[00:08:13] that's basically a customer facing chatbot and a customer asked them about their discount policies and it made something up

[00:08:20] basically and so the customer then went ahead buying a ticket assuming that to be true yeah and then found out it wasn't and there was

[00:08:28] a wait a second I was told explicitly x y z and then air Canada came back and was like oh no no that chatbot's a separate company

[00:08:39] and and that's not the judges rule that they were responsible in their liable for that so so these are the kinds of the the trial

[00:08:45] and errors that we're going to see a lot of work companies try things are going to mess up and then you know other companies going to be like whoa we got

[00:08:51] that we got to be careful here so you're going to see a lot of that I think but you're absolutely right I think here you in this is one

[00:08:57] of the things we've started building into our platform is actual testing of chatbots of lm's of gen AI testing it for bias testing

[00:09:05] it for hallucinations is it saying crazy stuff is it saying weird stuff right like we actually have some some capabilities to test around that

[00:09:11] so you can test that out before you you release it into the wild Google's listening after their recent faux pas with gen

[00:09:18] and I that's that's not even doesn't even a faux pa you're asking for something and you didn't get what you want

[00:09:25] so now you're bitching no there QA was messed up they should have tested that better now that's that was very surprising

[00:09:31] guys very surprise a company like Google eight their images I mean come on this is a little bit different than actually

[00:09:37] outcomes and screwing somebody over because they were a female and they played softball in the AI saw that that happened right

[00:09:45] this is hurting somebody versus not hurting somebody a little bit different so let's talk about the actual

[00:09:51] the actual regulations and the EU go figure is leading on this so tell us what the EU is doing yeah not surprising in a way

[00:10:00] right because the EU does operate in a more top down fashion there's going to be like a multiple hundreds of pages

[00:10:06] kind of legislation yeah they've been working on it for a while so where we've landed since we last

[00:10:11] had it so back in December there was an informal kind of agreement of around the contours of the

[00:10:16] legislation what's happening now dribs and drabs of it have been released and in April I believe they're

[00:10:23] doing a final formal vote on it right and then I think 20 days after that it goes into effect quote unquote

[00:10:29] I put that I put that in quotes because companies will have time right so it's kind of a lagged effect

[00:10:35] you'll have six months to get certain things in order you'll have a year to get kind of your bias

[00:10:39] testing in order you'll have a year and a half or two years to get your reporting infrastructure

[00:10:44] and governance layer in order right so there's different components to it and there's different time

[00:10:47] lags to each of these things but it's happening it's happening and the fines are huge right up to

[00:10:55] up to six percent of your annual revenue so this is not like the New York City law on any way shape

[00:11:00] performed the New York City law wrote a blog post on this ended up having no teeth behind it and part

[00:11:04] of it was the fine was five hundred dollars per I mean what why am I exactly and it was very narrow

[00:11:16] it was focused on AEDTs which are automated employment decision-making tools for hiring so only

[00:11:22] in promotion but only if it's automated completely automated so if you could easily come in and say

[00:11:26] I don't know my humans in the loop it's not automated and get around it right the EUA act is much

[00:11:31] more broad it even outlines kind of eight high risk areas of which guys just so your audience knows

[00:11:37] HR is one of the eight high risk areas right so HR is going to be in the crosshairs of this

[00:11:43] legislation yeah very consistent and I think California New York state others are going to have

[00:11:49] HR in the crosshairs as well so I think HR financial services and health are going to be three of

[00:11:54] the domains that are going to be in the crosshairs of all of the AI regulations going on.

[00:11:58] I think you mentioned is the importance of upscaling so in that time window company should be preparing

[00:12:05] building the skills internally like talk about how company should view it upscaling as opposed to

[00:12:10] just maybe hiring a company like yours or should they do both how do I prepare they should do

[00:12:15] both right because we're more of a platform company or a technology company you're still going

[00:12:20] to need humans in the loop and making kind of really thoughtful decisions around risk reward trade-offs

[00:12:26] right the thing that I say is like look at the end of the day no one is trying to reduce risk

[00:12:31] down to zero right if you're trying to take risk down to zero you'd never leave your bed you'd

[00:12:35] never leave your house right so that's not how we operate right that's not our humans operate

[00:12:40] that's not how businesses operate but you need to know enough of the calculations to understand

[00:12:44] okay what's the risk of this technology what's the reward and are we comfortable with it so you

[00:12:49] need both people at the junior levels who understand governance but they can see in your stakeholders

[00:12:55] who really ultimately need to make these calls at a company wide level what is our transparency policy

[00:13:00] what is our what is our red line let me give you an example right if you're thinking about something

[00:13:06] like predicting attrition at the individual level right a lot of companies have tried this I've

[00:13:11] you know my teams have built stuff like this it's a little bit minority report us right if I can

[00:13:15] predict that Joel will leave his company with 70% probability okay what do I do with that information

[00:13:22] do I throw more money at him do I try to keep him what if Chad's about to leave with 67%

[00:13:27] problem what's the difference between seven I mean you start to get into situations where like you

[00:13:31] don't know how to use these insights and what to do with it and then people can start gaming it so

[00:13:35] it's actually there's a really interesting ecosystem around some of these insights around AI

[00:13:40] and so that's where you have to as a company say okay we're not going to use it for that purpose

[00:13:44] it doesn't make sense it's not going to drive value and it's going to break trust so these are the

[00:13:48] really intricate conversations that you need to be having but to do that you need to have the talent

[00:13:53] that understands this at the detail level yeah and at the philosophical level right and that's

[00:13:58] there needs to be upscaled let's talk about risk grow quick because I think this is the risk that

[00:14:02] companies are going to care about the most if the workday suit was brought in the EU 6% of their global

[00:14:11] revenue equals 420 million dollars for a workday that is a risk they care about right absolutely

[00:14:21] absolutely I think there's two risks I think that one that's a pecuniary fine-related risk now I

[00:14:27] don't think it'll be that high right it'll be up to that amount and it'll be but it'll be significant

[00:14:31] right but reputational risk matters right like reputational risk matters if if you're losing customers

[00:14:38] or candidates because of some perception around how you treat your technology or how little

[00:14:46] you've tested it and those kinds of things that was a little harder to measure in terms of

[00:14:50] dollars and cents Chad but it can add up to be even larger than that amount right yeah especially

[00:14:55] from a revenue standpoint right I mean it's not only the fine but the perspective of lost revenue

[00:15:00] I mean optics obviously at all factors in there now as you were talking about some of the things

[00:15:05] that you're going to have to do with the EU like evaluating the impact of AI right and then also

[00:15:11] ongoing monitoring what is what does it even look like so that's where kind of you know we come

[00:15:18] in and help right like what does that look like and when you're talking about ongoing monitoring

[00:15:22] that's each if you're doing this manually that ends up being you to hire like a big old data science

[00:15:28] team right and how many HR organizations have the capabilities to hire a big data science team right

[00:15:33] and then on top of that they're going to be like wait a second I thought you told me AI is going to

[00:15:36] reduce my costs now I got to go hire a big data so this is where it gets tricky but that's the

[00:15:42] beauty of kind of technology like what we're building where we can automate this right like we have

[00:15:46] expertise in this we've done this for many many years we know how to automate it we know the ins

[00:15:50] and out we know the details and I think that can go a long way to helping companies again leverage

[00:15:54] the technology in a positive way which again I'm super bullish on right I think I sure this analogy

[00:15:59] with you guys last time like AI like cars are an incredible technology transform our societies

[00:16:04] but the same time you got to have breaks you got to have a dashboard you got to have a review mirror

[00:16:08] is otherwise what are you driving right you're not defmobile right so just put those things in place

[00:16:14] and then go fast right you talk about the EU and as I listen to this I just I realized how complicated

[00:16:21] and complex this is going to be and I think about Europe well yeah apparently the UK is going to

[00:16:26] have their own sort of set of of AI regulations talk about what they're doing and how it's different

[00:16:32] or what do you expect them to do and how it might be different from the EU and the US I think part of

[00:16:37] is again what one of the insights that we have is this is going to be very patchworky each country

[00:16:43] and region and state is going to have their own in fact they're kind of competing I've talked to folks

[00:16:48] in various state offices we're like oh we got it what what's that other state doing oh what's

[00:16:53] that state doing we got it you know exactly so there's going to be that element of some competition

[00:16:58] amongst the regulators here that being said there is some common themes and maybe that's what

[00:17:03] you're getting at too a little bit the common themes are around hey you gotta evaluate and monitor

[00:17:08] your models right like that's just the thing right and especially on a couple dimensions one is

[00:17:14] bias that always comes up right so evaluate bias the other dimensions that come up are around

[00:17:19] hate transparency and explainability can you explain what your model is if it's a black box that's

[00:17:24] kind of a problem right especially in the hiring space and you know lending space right health care

[00:17:30] right like these spaces you can't just be like oh yeah we just rejected your loan good luck we don't

[00:17:34] know why right you can't you can't do that so things like bias explainability are going to be really

[00:17:41] really common in these legislations and then when it comes to gen AI you're talking things like

[00:17:45] data privacy and security can someone hack into your gen AI right there's all this stuff you guys

[00:17:50] are probably reading out like poison attacks I don't know if your audience has heard that but that's

[00:17:54] where you kind of penetrate the system and basically inject it with kind of your own prompts

[00:18:00] and almost teach it to be a bad kid right and then you can do whatever you know you can do

[00:18:05] nefarious things right so there's a lot of security related things data privacy related things and

[00:18:09] so you see kind of some of these common themes across the the different laws and legislations

[00:18:15] and I think it makes it much harder because deep learning is a black box

[00:18:19] and in almost every case that's out there if you take a look at some of these models

[00:18:24] they're off learning by themselves it wasn't something that was programmed it was something that

[00:18:28] was a part of the process now the data that's entirely different what data are you training off

[00:18:35] of and I think that's where you get companies like hired score which was smart by for workday

[00:18:42] where they know exactly what their data sets are right they know exact and that's the secret sauce

[00:18:47] right so from the standpoint of a lot of people are pointing to the AI piece which I obviously

[00:18:54] that's going to be the output you know the learning and output but isn't the data just as if not

[00:19:01] more important in this in this whole scheme of things it's more important it's not it's not

[00:19:07] even same it's more important in fact I don't know how much you follow the the the whole competition

[00:19:13] between meta and open AI and Google and all the right like in terms of the foundation one of

[00:19:19] the things you might be noticing is they're all kind of converging right their performance is at least

[00:19:24] right and it's because the modeling techniques are kind of known now right amongst these like

[00:19:31] top level data scientists at these organizations they know what the best practices are they know

[00:19:35] it's Google published this famous paper call attention is all you need or something like that which

[00:19:40] was groundbreaking paper in this gen AI space and then other companies copied it right so a lot of

[00:19:46] that is not where you differentiate yourself where you differentiate why everyone thinks eventually

[00:19:50] Google will get back on the right track and win this if they have the most and best data out of

[00:19:55] anyone in the world right and so at the end of the day we still expect Google to kind of you know

[00:20:01] be at the top of this AI race but to your point that's what differentiates it right your ability

[00:20:06] to know and have the best data and understand that data really really well and and then be able

[00:20:11] to put it in a way that it's well governed right if you cannot make these mistakes if you have

[00:20:15] amazing data and not make these mistakes you're going to be ahead of the game America is unique in

[00:20:20] that it has a 50 state setup to where everyone kind of makes their own rules talk a little about

[00:20:27] the current state of what certain states are doing we've got California with SB 1047

[00:20:35] New York local law 144 anything else that we should be looking into the future for different

[00:20:41] states doing unique things around AI it's chaos it's all chaos cats and dogs living together states

[00:20:48] going to have and imagine how complicated that's going to be but then again you know our

[00:20:53] tax system is a little bit like that right like each you have a federal tax system and set up

[00:20:58] and then each state has their own specific tax policies tax rates tax discounts for this thing versus

[00:21:06] that thing and so you end up then building an ecosystem of people who specialize in that stuff

[00:21:12] right and that's what happens with regulation anytime you have a regulation then you have an ecosystem

[00:21:16] of people lawyers and experts and consultants who then start specializing so I expect that to be

[00:21:21] the case Joel here where you're going to have that kind of regulatory ecosystem so you have

[00:21:27] California New York state is separate from the New York City local law jules so there's going

[00:21:31] to there is a New York state law as well Maryland is working on one right now New Jersey has one

[00:21:39] and then there's a bevy of other states Massachusetts main Colorado has one that's focused on its

[00:21:44] fascinating insurance companies right now so that's a more targeted for the domain the other ones

[00:21:49] that I mentioned New York state and California in particular are much broader right so that's

[00:21:55] going to be the interesting pieces like some of these are going to be quite broad and some of them

[00:21:59] are going to be kind of domain specific and so we're going to see how that plays similar taxes we

[00:22:02] have local taxes we have state taxes we have federal taxes and the federal kind of trumps all that

[00:22:07] and we talked a little bit about politics in the green room I was surprised to see the FCC come

[00:22:12] out with laws so quickly around robo calls you know Joe Biden famously recently called people it

[00:22:19] wasn't him but he was saying don't vote it so I was Dean Phillips wasn't it I mean that was the

[00:22:25] other fucking Democrat no it wasn't him it was someone on its campaign it was someone who used to

[00:22:29] work for Dean yeah yeah I'm sure there's stuff we don't even know what's going on but I was I was

[00:22:34] I was impressed to see the FCC move so fast to make this illegal which tells me and I commented

[00:22:39] recently that politicians are freaking out thinking that their voice could be out there saying well who

[00:22:44] knows what so what's your take on the feds coming down with okay everyone here are the rules all

[00:22:51] the states are going to follow it are we gonna are we're gonna stay in this sort of disparate state

[00:22:56] rules can the Fed come in and make sense of all this in your opinion I think it can on certain areas

[00:23:02] right and they have if you saw the White House EO I think that came out right around Halloween of 2023

[00:23:07] there are a couple of areas where the federal government does have domain right and we want to come

[00:23:11] to things like national security or things like kind of our democracy right like that ends up being

[00:23:19] kind of the federal governments domain and it's actually kind of concerning and I think they put

[00:23:23] they went moved fast because we are we talked about this Joel we're an election cycle right now

[00:23:27] right so it's not something they can wait six months to do and you guys have seen it this can be

[00:23:33] crazy right like it's it's the voice stuff it's also the image stuff it's the videos like you

[00:23:38] can make anything now and you saw Sora have you guys played around with Sora which is um I mean

[00:23:44] isn't it you movies right you can yeah right yeah that's the one right uh and you can put people

[00:23:51] in there you can put I can put Chad in one of those movies and have him doing stuff he's never

[00:23:55] done right in an actuality right and so no ideas Cheezman I won't take the podcast on that we got

[00:24:01] Goor on the show I'm not gonna I'm not gonna muddy it up don't worry don't worry goor is too high class

[00:24:07] for that um so that stuff is now and and and so I completely understand why they came in to try to like

[00:24:13] curtail that right now that being said other things like hey how do you want to govern

[00:24:19] software in the hiring space or the lending space you know now the thing is there's already

[00:24:23] one other thing I do want to say and keep talks about this a lot there's already federal legislation

[00:24:29] involved in lending and in hiring and in health right like we already have like there's rules

[00:24:35] already right so in in hiring the EOC like we have lot there's this um chapter um title seven

[00:24:40] of the Civil Rights Act where you cannot be biased and it doesn't say humans can't be biased or AI

[00:24:46] can be but it doesn't say anything so you are so already companies are absorbing that risk

[00:24:51] without really thinking about it and so they should be proactively instead of waiting for additional

[00:24:56] AI legislation they should already be on top of this saying hey we need to know what's going on

[00:25:00] you've talked about this for for shit years now with AI right it scales decision making right

[00:25:08] and it scales bad decision making it's not that uh humans haven't been making bad decisions we have

[00:25:15] it's not that humans haven't been biased oh we're so biased right but now the AI is taking that

[00:25:21] bias behavioral data some of those decisions some of the quote-unquote predictors that some of

[00:25:26] these companies use and it's scaling it so now if you're a company who got away with bias and all

[00:25:34] those things before this is going to make all of that scale and it's going to make it much easier

[00:25:40] to identify who's fucking around 100 percent I think you read on both of those accounts it's much

[00:25:46] easier to scale bad biases and bad kind of decision making the same time it's also going to be

[00:25:55] easier to sue and I think that's where you were going with your second point if I looked at

[00:25:59] that correctly in that you know exactly where the problem is it's in that I'm gonna be easier to

[00:26:04] see though because it's gonna be the the problems not gonna be small because the scale was small

[00:26:09] that scales gonna be much larger so therefore the problem's gonna grow with the scale and

[00:26:13] you're it's gonna be much easier to identify so therefore you're gonna have more people pointing

[00:26:18] and saying oh look there's a lawsuit but I think all of that is true I think you're more likely

[00:26:23] more it's more gonna be more easy to sue more easy to have a lawsuit but at the same time though

[00:26:26] let me say it's gonna be easier to monitor as well yeah right because that's what I'm gonna my point

[00:26:30] being right when you have 50 different recruiters right and three of them aren't entering the data

[00:26:36] in the system and like six others like are what you know what I mean it's just hard to know what's

[00:26:42] going on but with the model in place it's actually easy to monitor and you can automate the monitor

[00:26:48] and the thing is this has been going on for a while like I talked to you about capital we come from

[00:26:51] capital one capital and monitors its models like all constantly it's not hard and so HR just needs

[00:26:56] to build these practices in they just need to kind of have that mindset adjustment and say hey look

[00:27:00] that we're we're starting to become a technology domain a data driven domain we need to then along

[00:27:06] with that build these other practices in place it's easier to monitor a model than it is 50 humans

[00:27:11] remote work obviously in the last few years has become incredibly popular and we've seen sites

[00:27:17] that are platforms for managing a diverse global workforce the remotes the oysters the deals etc it

[00:27:25] seems to me like recruiting on a global basis would open your company up to a lot of risk in terms

[00:27:31] of AI hiring going back to these different laws in different countries and states and all over the

[00:27:36] all over the map how do you how confident are you that these platforms are covering companies

[00:27:43] asses do you feel like companies are reluctant to go out on a global scale because of the risk

[00:27:49] like I'm just curious your thoughts on the global state of things and AI recruiting

[00:27:54] you know when you were talking about that maybe just think of this thing that got past in India

[00:27:58] I don't know if you guys saw this recently but any company that uses AI in India for anything

[00:28:04] has to get like a federal approval which is kind of crazy to think about I mean imagine something

[00:28:10] like that passing here it wouldn't right but that's a pretty ownerless thing so to your point Joel now

[00:28:16] like hey what if you are using what if you're a multinational company and you use AI in your

[00:28:21] hiring practices just generally right and India's one of your places and wait a second now for India

[00:28:27] I got to go get special approval and then from right like so you're so this is the thing that's

[00:28:31] going to be hard to manage right is like and this is what I meant by that you're going to have to

[00:28:35] hire some policy folks some lobbyists you're going to probably hire legal folks compliance

[00:28:40] brightly you're going to start to have to have this kind of ecosystem of people to think about

[00:28:45] how these things affect us right and so that's going to be a bit of a challenge I totally agree

[00:28:51] 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

[00:28:56] workday I'm seeing CVS I'm seeing all these big brands that are getting thrown all over the place

[00:29:01] is it feasible that I can do my business without even using AI especially when we're talking about

[00:29:08] hiring in a very competitive market for a short time right for today for tomorrow for this next

[00:29:13] month for this next year and so at the end of the day then it comes to like how forward thinking

[00:29:17] our CHROs is what it comes down to right and if you are kind of a short term operational CHRO

[00:29:25] you're okay that's not my problem right we're going to be fine for this year I'm focused on

[00:29:28] this year I'm focused on this quarter I got to cost this quarter like you know so there's

[00:29:32] different types of CHROs that have come across Chad the ones that are strategic visionary forward

[00:29:38] thinking absolutely not absolutely not right they know AI in fact there's so many opportunities

[00:29:45] in HR like HR is like the perfect domain actually there's so many opportunities to use gen AI

[00:29:50] capabilities natural language processing capabilities predictive analytics capabilities right

[00:29:54] I was talking to a company the other day where they still have each recruiter manually at least check

[00:30:02] the resumes that come in and apparently a hundred thousand resume applicants last year and I

[00:30:07] was just shocked I'm like really in 2024 you know what I mean that's just super inefficient

[00:30:14] right like you can't compete doing that again you can do it for next week you can do it for next

[00:30:20] quarter but like in 2025 2026 how are you holding your you know your cost down how are you

[00:30:25] innovating things like your employees every single employee has a question about I remember I

[00:30:29] had so many questions around vacation days policies all of that stuff you can automate all of that

[00:30:36] there's so many opportunities and this is I think it was some of the fun part of the conversation

[00:30:40] like hey how can you use AI to create value in HR and there's really a lot of opportunities so for

[00:30:45] the ones the companies and the CHROs that are forward thinking they're absolutely not going to

[00:30:49] shy away from it keep coming back to your different distates different laws and how disparate this gets

[00:30:56] just curious about your industry typically there's a there's a coconut Pepsi and then a bunch of

[00:31:01] phantasy doctor peppers like where does where does this go do we have like local accountants where

[00:31:08] people are AI sort of regulation experts in your local market and it's a few people like your state

[00:31:14] farm agent is it a big are there a couple big players and it's all like software and we know

[00:31:19] all the answers and all the states in real time is it both of those things like how do you think this

[00:31:25] this industry evolves from one of the pioneers I think there's some parallels to if you think

[00:31:31] about there's already compliance in HR related to hiring put a put aside AI and bias right let

[00:31:36] what let's look I'm not talking about that but when you hire if I go higher in Canada if I go

[00:31:42] higher in Europe if I go higher in Asia there are local laws that I have to follow to do that hiring

[00:31:49] right compliance all the stuff from from how you interact with them to how you onboard them the

[00:31:56] contracts this pay the salary the level all that stuff and so you have these companies payroll

[00:32:01] companies that's how they got started right was like hey you're going to go higher over there we'll

[00:32:06] help you we'll deal with all the compliance we'll take care of all that for you right and so in a

[00:32:09] sense it's a little bit of like what we're trying to do and others are going to try to do as well

[00:32:13] which is like hey we are going to become experts like one of the we're partnering with law firms

[00:32:17] for instance right and so we're going to be experts on that stuff and kind of take that off your

[00:32:20] plate and that's what you know like a deal does right or that's what like a a day force right

[00:32:26] they kind of they take that off of your plate and I think you're going to see a little bit of

[00:32:31] that kind of model when it comes to kind of AI regulations too so in talking to our friend friend

[00:32:38] of the show EOC commissioner Saundirling he had mentioned over a year ago that there's more than

[00:32:46] likely going to be an ISO around this the international organization for standardizations

[00:32:51] more than likely that's going to be a standardization that everybody can kind of flow into so it's not

[00:32:56] so chaotic and it seems like we're on our way there can you talk about that a little bit I can

[00:33:03] I agree and I disagree and so where I agree is yeah there's going to be a standard and it's here

[00:33:08] ISO 42,000 one was released late last year now it's still I think in the process of like there could

[00:33:13] be some amendments some revisions and that kind of thing and there's still there's still a process

[00:33:18] to be worked through there but realize there's a difference between market standards and regulations

[00:33:23] right a market standard is great right it tells you that like your product has a certain level

[00:33:28] of quality certain level of like certification that you can take for granted right a regulation

[00:33:34] still look I still got even if I have the standard I still got to go satisfy that regulation if I

[00:33:39] don't want to get fined if I don't want to get on it's make it easier for regulators though to be

[00:33:43] able to take a look at a standard and say okay that's a standard we're going to get behind that

[00:33:48] you have to do that so it makes it much easier for a bunch of individuals a bunch of 80 year old

[00:33:54] individuals who they don't know a smart phone they've got a jitterbug phone right they don't

[00:34:00] have a smartphone so at the end of the day is this not like a smart way to just say oh wait a minute

[00:34:05] those guys seem to have their shit pulled together let's just go ahead and jump on that tree there's

[00:34:10] absolutely truth to that and in fact a lot of the private sector companies are pushing for that

[00:34:14] they're saying hey guys white house you know whatever let's develop market standards let's have

[00:34:19] public private partnership let's work together and let's develop these standards and let those

[00:34:23] standards be the things that provide the kind of common ground around this stuff right and there's

[00:34:29] some truth to that like if you look at the white house eo again the White House what the eo says is

[00:34:34] they put the nist national initiative standards and technology standards and technology right in

[00:34:39] charge of like developing some of those standards so if you look at it in the US that's kind of our

[00:34:44] approach now look the states at the end of the day are going to do their own thing right so the White

[00:34:47] House can't govern that but from a White House perspective I do think other than things related

[00:34:51] to national security which are right and they own the domain around that but otherwise they

[00:34:58] that's kind of what they're saying Chad they're saying hey let's this let's build some standards

[00:35:01] nist you kind of you know we give you power to run with this work with private sector work with others

[00:35:07] and develop standards and and let's let's let's go with that so I think there's absolutely some truth

[00:35:12] that is guru sethopathy everybody here are listeners that want to know more about you maybe they

[00:35:19] can keep up with some of this stuff where would you send them to learn more couple sources follow me

[00:35:24] on LinkedIn follow fair now on LinkedIn we're both myself my account and fair now's account

[00:35:29] we're publishing a lot on these topics whenever there is whether it's a lawsuit when there's a

[00:35:34] company that's put out something that we think we disagree with or agree with or laws and

[00:35:40] legislations as we've been talking about we'll be posting on there so please follow us as well

[00:35:44] as our website so we have a whole area where we talk specifically about legislations about market

[00:35:49] standards and we talk about what's coming down the pipe and what's in these things the EU AI Act

[00:35:54] we have a whole page on who's affected what's going to happen and all that stuff so between those

[00:35:59] two spaces you should be able to learn story short gurus a busy guy the Chad and cheese podcast has

[00:36:05] a bat phone to guru okay so you can always listen to us but yes definitely go to fair now yeah thanks

[00:36:13] thanks for keeping us up to speed guru this is a complicated issue Chad that's another one in the

[00:36:18] can we out we out thank you for listening to what's it called podcast the Chad the cheese

[00:36:28] friend they talk about recruiting they talk about technology but most of all they talk about nothing

[00:36:35] just a lot of shout outs of people you don't even know and yet you're listening it's incredible

[00:36:41] and not one word about cheese not one cheddar blue not show pepper jack swiss so many cheeses

[00:36:51] and not one word so weird any who we should have subscribed today on iTunes Spotify Google Play

[00:37:01] or wherever you listen to your podcasts that way you won't miss an episode and while you're at it

[00:37:08] visit www.jad cheese dot com just don't expect to find any recipes for grand cheese so weird we out