Can AI Be Biased? Legal Implications for Recruiting

[00:00:00] Hi, this is Matt. Just before we start the show, I want to tell you about a free white

[00:00:05] paper that I've just published on AI and talent acquisition. We all know that AI is

[00:00:11] going to dramatically change recruiting, but what will that really look like? For example,

[00:00:17] imagine a future where AI can predict your company's future talent needs, build dynamic

[00:00:23] external and internal talent pools, craft personalized candidate experiences, and intelligently

[00:00:31] automate recruitment marketing. The new white paper, 10 ways AI will transform talent

[00:00:37] acquisition doesn't claim to have all the answers, but it does explore the most likely

[00:00:43] scenarios on how AI will impact recruiting. So get a head start on planning and influencing

[00:00:50] the future of your talent acquisition strategy. You can download your copy of the white paper

[00:00:55] at mattolder.me slash transform. That's mattolder.me slash transform.

[00:01:04] Talent acquisition stands on the edge of a revolution, with AI tools promising to make

[00:01:16] recruiting faster and more effective. But will they make hiring fairer? Recruiting

[00:01:24] and HR are already a key focus for governments as they develop legislation for AI, and employers

[00:01:32] are already at risk of breaking existing laws if they use AI tools that discriminate

[00:01:39] against protected groups of people. I'm Matt Alder, and my guest in this episode of Recruiting

[00:01:45] Future is Commissioner Keith Sondaling of the EEOC. In our conversation, we discuss

[00:01:52] the benefits and risks of using AI and hiring. And what employers need to know to ensure

[00:01:58] compliance, both with existing laws, and the new regulations that many countries are

[00:02:04] shortly going to be implementing. Commissioner Sondaling, welcome to the podcast.

[00:02:10] Thanks for having me, Matt. You know, we met a few years ago, and I've subscribed

[00:02:14] to your podcast since, and it's about time I finally have a chance to get on. So I'm

[00:02:20] really excited to talk to you today. Absolutely. And it is an absolute pleasure

[00:02:23] to have you on the show. Could you for mainly the benefit of people outside of the US?

[00:02:30] Could you introduce yourself and explain what you do? Yeah. So I'm a Commissioner at

[00:02:35] the United States Equal Employment Opportunity Commission, the EEOC. So in the United

[00:02:40] States, there is an independent agency that deals with all workplace discrimination issues.

[00:02:47] All issues related to equal employment opportunities. We were actually created out of the Civil

[00:02:54] Rights Movement in the 1960s. Martin Luther King marching in Washington DC led to the creation

[00:03:00] of the Civil Rights Act. The Civil Rights Act then created some of the strongest protections

[00:03:05] worldwide when it comes to entering and being in employee for providing for yourself,

[00:03:14] providing for your family, being in the workplace. We have civil rights laws that protect

[00:03:19] that here in the United States. And it's my agency that administers and enforces it.

[00:03:24] So we have a bunch of roles we play first and foremost. We are a civil law enforcement

[00:03:30] agency. So we're responsible for actually having federal investigators go to workplaces

[00:03:36] and enforce these laws. We also do a lot of compliance. Part of our mission is not only

[00:03:42] to enforce and remedy employment discrimination, but we also do a lot of compliance assistance

[00:03:48] to prevent it from ever occurring in the first place. Another one of our goal is promoting

[00:03:52] equal employment opportunity in the workplace. So I like to say, our mission is very much

[00:03:57] HR's mission is you look at HR professionals. Our identities are fairly similar in that

[00:04:04] regard. So when you think about the big ticket items coming from the United States or even

[00:04:09] globally in HR, the MeToo movement, a pay equity, everything related to COVID and accommodations,

[00:04:17] disability, religion, race, national origin, age. So that's my agency here in the United

[00:04:24] States. And because we were really one of the first globally, a lot of countries really

[00:04:29] look to our agency as we'll talk about to model their employment laws and a lot of multinational

[00:04:35] corporations really use our agencies HR standards as a standards globally.

[00:04:40] Now you're very much out and about on the conference circuit at the moment. I saw you

[00:04:46] transform last week. As you say, we met on leash. I know you're going to HR take Europe

[00:04:52] and in May, what's the, what's your kind of mission? What's your objective with sort of

[00:04:56] getting out there into the into the community via these conferences?

[00:05:00] Yeah. Well, you know, that's something I really made my personal mission when I got confirmed

[00:05:05] to this job in 2020 to be a commissioner. And you know, I'm a labor and employment lawyer

[00:05:11] by training. So I used to defend HR departments in lawsuits. But you know, what I really found

[00:05:18] was there was a significant disconnect between policymaking in Washington, DC and those HR

[00:05:23] professionals around the world who are actually then stuck with figuring out the complexities

[00:05:28] of dealing with labor and employment law dealing with HR laws. So when I first got to the

[00:05:35] you see, I wanted to be more innovative and ask and talk to HR professionals across

[00:05:40] the board from C.H.R.O.'s to T.A. heads saying, you know, what are the big issues that

[00:05:46] you're facing in your practice? And you know, what are your fears and what would you like

[00:05:50] to implement moving forward? And that's when I was surprised to learn that by far and away

[00:05:57] the number one issue was workplace technologies and artificial intelligence and machine learning.

[00:06:03] And like many on the outside, I had to learn what that actually meant because for me, I thought,

[00:06:09] you know, well, that is around workforce displacement of having robots replace actual workers

[00:06:15] because you remember that was all the rage for a long time. And then I realized that really

[00:06:20] only related to certain industries, you know, retail, manufacturing, logistics. And so obviously

[00:06:29] it is more than that. And that's when I dove into HR technology and figured out that there's

[00:06:36] a lot of vendors, there's a lot of programs, there's a lot of companies who are actually

[00:06:39] using machine learning. And this was way before gender to AI to actually make employment decisions,

[00:06:45] to actually help HR departments find the right candidates to advertise in the right places,

[00:06:51] and even to select the candidates. And then have once they become employees of the company,

[00:06:58] algorithms were playing a significant role in their promotion, in their salary, and

[00:07:03] their tenure at the company. So that's how I stumbled into this world. And I was one

[00:07:08] of the first regulators to really start talking about it because I do believe this is the future.

[00:07:14] I do believe in the technology, but there has to be significant, quote unquote, guardrails

[00:07:20] around it to be properly used. So that's why I'm out there all the time. I found out that

[00:07:26] you really have to be in front of people and you have to say here is exactly what your fears

[00:07:30] are from Washington DC. And here is actually how you can live within the structure and framework

[00:07:38] and use these programs both properly, lawfully, and ethically.

[00:07:45] Absolutely. We'll talk more about that in a second and the guardrails are the various bits

[00:07:50] of additional legislation that's sort of popping up around the world. Before we do though,

[00:07:56] let's just focus on the kind of advantages. So what advantages you said you think AI is,

[00:08:02] you said that AI is very much the future. What advantages do you think it brings to

[00:08:08] talent acquisition? Significant, potentially unlimited advantages. Just not only in

[00:08:17] the speed and efficiency, right? For a lot of large companies getting, and with the ease

[00:08:22] of applying on some of these websites now, you can get millions of applications. And

[00:08:28] there's just not enough time anymore for talent acquisition or hiring managers to be able

[00:08:33] to review those at scale. So just from obviously the simplistic efficiency, there's significant

[00:08:40] benefits. But I look at it in a much more complex way than that. And I look at it and

[00:08:45] saying well how has employment decision making occurred for decades? And that is generally

[00:08:52] just through the human brain. And with that, there's a lot of bias that has been baked into

[00:08:58] employment decision making. That's why my agency exists. That's why we get in the United States,

[00:09:06] probably close to 100,000 charges of discrimination every single year and why we have litigation.

[00:09:14] So there's a problem to begin with that companies haven't figured out. And the EEOC hasn't been

[00:09:21] able to stop despite all of our law enforcement and our guidance that we give out.

[00:09:26] So if technology can help us make more transparent, clear, fair employment decisions that are

[00:09:34] actually audible and traceable based upon metrics, not just based upon an individual's bias. And

[00:09:41] that bias may not necessarily be something that's unlawful, right? You know, the unlawful

[00:09:46] categories such as race, sex, ethnicity, religion, etc. But other biases that have no

[00:09:52] bearing on the individual's ability to perform the job, but has really historically prevented a

[00:09:59] lot of people from entering the workforce. So I say that if the AI tools are properly designed

[00:10:06] and carefully used and those are two separate and distinct concepts which we can talk about,

[00:10:11] it can actually help us help TA actually get to that skills based approach, actually have

[00:10:17] hires that are based solely on merit and no other characteristics. Actually allow candidates who

[00:10:24] were never potentially given a shot to get through the door to get through their first screening of a

[00:10:29] resume, and you know all the historical long standing bias about men and women's names,

[00:10:36] those very basic examples. Well this is their ability now if it's designed properly and carefully

[00:10:43] used to actually have employers take a skills based approach to hiring which is where everyone wants

[00:10:49] to go and nothing else. But you know, I always talk about the benefits but in my job I also have

[00:10:56] to talk about the risks too. Of course. If it's not carefully designed or it's improperly used,

[00:11:01] you just flip those then it could potentially scale and cause more discrimination than any one

[00:11:08] individual can do. So so much of it is you know we can't just talk about the benefits,

[00:11:14] we can't just talk about the potential liability, we have to talk about well how do we do this right

[00:11:19] to actually have TA go into a much more technology based, more technology based industry with all

[00:11:29] these very tools that could actually have significant benefits all around. And I suppose to pick up on

[00:11:35] something that you said there which is nice when you talk about a lot. There's almost this kind of

[00:11:41] obsession with you know, with new legislation what are governments going to do to control AI and

[00:11:47] you know I think the point that I've heard you made make a number of times is well actually

[00:11:52] there is already legislation that they are for us falls under. Tell us a little bit about that.

[00:11:57] And that's the biggest misconception out there by far is that there's no laws,

[00:12:02] there's no guardrails, there's no existing framework related to using AI in the workplace.

[00:12:10] And look it's not everyone's fault that they believe that because you know after a generative AI

[00:12:16] and chat GPT and all the moving forward with the EU AI act in the EU and all these governments now

[00:12:24] having summits and then here in Washington you know senators bringing in very famous tech CEOs

[00:12:31] you know that's causing more chaos of saying well look look at the uncertainty related to using AI

[00:12:38] look at the lack of actual loss here. You know either we're not going to use it because of that

[00:12:45] uncertainty which is not good or we're going to use it and not build those governance structures

[00:12:51] around it and let it go and hopefully it makes a great employment decision and hopefully

[00:13:00] it removes bias and we'll buy these AI tools like we buy other software and eliminate our T-8

[00:13:07] apartments and just have computers do it because there's no laws yet all both sides of those are wrong

[00:13:14] because I'm going to simplify it and this is really for those of you in T.A. how you need to look

[00:13:20] at this right? You know there's only a finite amount of employment decisions that an employer can

[00:13:26] make we all know the basics hiring firing promotion you know wages training benefits and at the end

[00:13:34] of the day AI hasn't created a new sort of actual employment decision yet so what it's doing

[00:13:41] is actually assisting you or making that employment decision but you as employers you as hiring

[00:13:47] managers you as our T-8 are the only ones who can actually make that hiring decision and that

[00:13:54] decision that employment action is what's regulated and then what we look at that's what

[00:14:01] HR professionals have had training on that's what you have policies procedures and handbooks on

[00:14:07] you know since you began your career so in a sense we need just to simplify it and say what are

[00:14:13] we asking the AI tools to do? Are we asking it to find the best candidates? Well what guard rails

[00:14:20] do we have in place? What policies procedures do we have in place when we ask a human to go find

[00:14:24] candidates and sort through candidates? So there are laws that apply and it's the same loss that

[00:14:30] you've been subject to your entire career in HR no matter where you are in the world and that's

[00:14:36] what I think we're losing sight of with such an interest now in AI globally from state capitals

[00:14:42] around the world is that there are existing laws then you can use this properly now being compliance

[00:14:50] with the law or be non-compliance in the law if you're just going to ignore it just like every other

[00:14:55] framework you have within your organization whether it's making a hiring decision or you know

[00:15:00] actually for your core business there's regulations around that and we can't lose sight of that

[00:15:04] and a lot of people are because of you know the interests in specific AI legislation which will

[00:15:13] play a role in how you operate especially if you're in Europe or if you're in New York City which

[00:15:19] we'll talk about but just don't lose sight of the fact that at the end of the day the EEOC is going

[00:15:24] to look at the employment decision and we're going to look to see if that employment decision had

[00:15:31] whether it was made by a human or whether it was made by a robot we're going to analyze it the same

[00:15:36] way and liability is going to be the same way and I think there's an interesting point there about

[00:15:41] who has that liability because it's the employer isn't it's not the vendor who's designed the system

[00:15:46] it's the employer who's made the employment decision and that's a really key concept and a lot of

[00:15:51] people don't understand where that's coming from so let me take a step back sure when our agency

[00:15:59] was created when Title VII of the Civil Rights Act here in the United States was enacted in 1960s

[00:16:06] we were given jurisdiction over three parties and the law says only those three can make an employment

[00:16:13] decision so no one else can make an employment decision nobody else can actually be say you're hired

[00:16:19] you're fired you're terminated right so that's you know employers companies right easy one unions

[00:16:26] and I know the union issue gets a little complex when you get into Europe just you know American

[00:16:32] unions right yeah and then staffing agencies so the big staffing agencies which are all global

[00:16:37] right which are you know on every continent at this point right so those are

[00:16:43] within our jurisdiction and U.S. law says those are the only three parties that can ever make an

[00:16:47] employment decision right within there you don't say an AI vendor right yeah or a software tool

[00:16:53] or an algorithm right so that's that's where it comes from and it's not just picking on the employer

[00:16:58] saying we're going to go after the employer you're going to be the one who's going to be responsible

[00:17:02] even though you didn't design the tool even though you don't necessarily know how to use

[00:17:07] the tool the vendor just help you use it it's rooted in the law now we're gonna we're seeing potential

[00:17:14] changes in that and that's where a lot of this is going and there's you know in the EU AI Act

[00:17:20] you know they're specifically saying that vendors who build the systems you know are going to

[00:17:26] have liability there's proposals in California that also is going to give potential liability

[00:17:33] to the vendors and saying that you know in California if these bills pass that the vendor if

[00:17:38] they're involved in making an employment decision they're essentially going to be an employer too

[00:17:42] but for the meantime you cannot rely on thinking that if the tool does something

[00:17:50] that you may not have access to that you may not understand that you will be absolve of liability

[00:17:55] because it's through an AI and it's through a vendor and that's critical starting point

[00:18:01] and why buying HR technology is so much different than buying other kinds of technology

[00:18:08] and for you as TA professionals for you as an HR who are with the vendors who are deciding which

[00:18:14] products to use and then have to go sell it internally this is where the conversation needs to be

[00:18:21] because that liability rests with your use of it and not the vendors you have to then

[00:18:28] build programs and structures around it within TA within HR to ensure that your own companies use

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[00:19:50] Imagine how fast we could solve the world's biggest problems if more SaaS startups would gain

[00:19:55] traction sooner. Welcome to the Tech Entrepreneur Animation Podcast. This podcast is dedicated

[00:20:01] to sharing experiences from B2B SaaS CEOs who are going above and beyond to deliver chains

[00:20:07] that is noticed. You will hear their secrets and learn what is required to build a SaaS business

[00:20:13] that the world starts talking about, and keeps talking about, and how to overcome the roadblocks to do so.

[00:20:22] To focus a bit on the kind of the new regulations and legislation that's being talked about

[00:20:27] around AI. It's interesting that we're talking about AI in general here, but it always seems to be

[00:20:34] recruiting and TA the people that there's always a big focus on in these kind of discussions.

[00:20:42] What are you from the conversations that you're having? What are you seeing happening in this area?

[00:20:49] Obviously every country is very different. Are there common themes? What's the sort of state of play?

[00:20:57] This is a really great question. It also gets back to the point I was just making about

[00:21:03] the additional things that HR professionals need to do to properly implement these AI HR tools.

[00:21:10] Because you're at all the conferences, man, you see that it's no longer a question

[00:21:16] are you going to use AI in HR? We're way past that. The struggle now is which one are we going to

[00:21:23] use? How are we going to use it? How are we going to comply with the law in our own country?

[00:21:29] In other countries, because a lot of the systems are really designed and a lot of

[00:21:34] talent acquisition heads at larger organizations aren't just dealing with one state, one city.

[00:21:38] They're dealing with the world. How are we going to build this framework within the confines

[00:21:44] of existing law but also to potential future laws that are being passed or being proposed?

[00:21:51] And I've argued now, if you look across the board, starting with the EUAI Act,

[00:21:57] which a lot of your listeners know, places employment decisions in the higher risk category.

[00:22:04] New York City's local law 144, which was the first law related to actually using AI

[00:22:12] for hiring and promotions. Of course, limited to New York City,

[00:22:16] it's important to stay abreast of all these changes. And not fear that there's going to be a new law

[00:22:24] that comes out that's going to completely essentially in a way diminish all the work you've been doing

[00:22:33] and trying to deal with the vendors, vet these systems, implement these systems to take a more

[00:22:37] technology based approach. Unfortunately, I think that's how a lot of people are looking at that.

[00:22:41] I wanted to spell that right now because if you look across the board, let's start the first

[00:22:48] AI HR law was actually in 2020 in the state of Illinois. And that was related to video interviewing.

[00:22:55] If you remember... I do remember, yeah. Yeah, so facial recognition and video interviewing

[00:23:01] because there were some software out there that during the interview process on Zoom would

[00:23:07] analyze and employs face. And I don't need to get into the legal issues right now about

[00:23:13] how that may impact certain groups over the other or how that may impact disabled workers

[00:23:19] or religious workers, I can go off on that. But look, that's a state that Illinois basically said

[00:23:24] if you want to use facial recognition in a video interview, you're going to have to disclose it.

[00:23:30] You're going to have consent. You're going to have opt out. Basically made it so difficult

[00:23:35] to use in Illinois that in an in essence it basically banned facial recognition interviews in Illinois.

[00:23:42] And then New York comes along and says, you know, for New York City employers,

[00:23:47] you have to do pre-deployment audit testing. You have to disclose those audits.

[00:23:52] But then they said that's only for hiring and promotions only on race sex and ethnicity, not

[00:23:59] you know, terminations, not anything related to compensation or total rewards.

[00:24:05] And not on the other categories like age and disability, right? So you're seeing

[00:24:11] little differences between all of those. And I think that's just the decision you need to make

[00:24:17] and you're starting to see common themes and there's dozens of state proposals related to AI

[00:24:24] in HR. But a lot of them are starting to get to employee consent. A lot of them are starting to get

[00:24:31] to pre-deployment audits, yearly audits. Or as you saw in Illinois, just essentially almost

[00:24:39] restricting it in certain areas and even the EU. But what I've been arguing is these are all

[00:24:45] things, you know, essentially business decisions, you have to make yourself on knowing that these

[00:24:51] are coming. And knowing that you're starting to see common threads such as consent, such as

[00:24:55] pre-deployment audit. At some point nationally or globally, if you start doing those, you're going

[00:25:01] to then be in compliance, you know, essentially across the board. And for instance on the pre-deployment

[00:25:08] audit site, that is not required under federal law. But we, the EU, see us put out guidance if

[00:25:13] you do, we encouraging you to do that. Why? Because if you do it pre-deployment audit and you see

[00:25:18] that it has, it's discriminating against certain groups. And the reason it's discriminating against

[00:25:24] certain groups, whether it's the algorithm, whether it's the, you know, your applicant pool or whether

[00:25:28] it was just a skill requirement that you put in there. What that wasn't actually necessary for

[00:25:32] the job that winds up having a disparate impact against certain workers, you could test that in

[00:25:37] advance of ever making an employment decision. You could test that in advance to see if there's bias

[00:25:43] and then correct it before you ever cause any harm to any individual worker. So if you do that,

[00:25:49] saying because I want to be in compliance with New York, because I have to be in compliance with

[00:25:52] the EU because I primarily operate out of Europe, you're going to be even further in compliance with

[00:25:59] federal law because you tested it, you found potential bias, you fixed it ever before you made a

[00:26:05] decision on someone's livelihood. And I think that is so important. Instead of fearing all these

[00:26:10] additional requirements, the more you sort of selectively choose maybe because you have to because

[00:26:16] you operate in those jurisdictions or you don't just knowing that this is good governance. This is

[00:26:20] as far as it's going to go. You're going to be just in further compliance and you can have more

[00:26:25] ease in using these tools. So I don't look at it as, you know, this is, let's stop our programs. Let's

[00:26:31] stop doing this because there's going to be so many new laws. Well, these are all things HR

[00:26:36] professionals know how to comply with. Yeah, already it's just instituting them now when it comes

[00:26:40] AI. Yeah, 100%. That just makes perfect sense. What would your advice be? So obviously, you know,

[00:26:47] there's a bit of a rush at the moment to develop tools with generative AI, you know, some employers

[00:26:55] are actually developing their own tools, which is interesting, but obviously there's also a big

[00:26:59] ecosystem of new vendors out there building interesting, building interesting tools, which is always

[00:27:05] great to see. Would your advice be to those vendors in terms of how they, how they sort of, you know,

[00:27:12] how they how they build their offerings? Well, on the generative AI front, there's a few different

[00:27:17] ways to look at it for HR professionals. One, there's actually using generative AI to make job

[00:27:23] descriptions, using generative AI to do a performance reviews, using generative AI to tell you where

[00:27:29] to do your employment advertisements, actually doing some of the HR functions. And the issues with

[00:27:35] that are those differences that some of the issues we saw early on with machine learning and

[00:27:41] algorithms discriminating against discriminating against certain groups because, you know, if that data

[00:27:46] is just coming from the internet, if that is not your own data, if that is not what you're looking at

[00:27:50] from your own vetted, you know, company policies or your own workforce, you have no idea what bias

[00:27:57] you're injecting into that job description you're asking generative AI to do. So if you say generative

[00:28:03] AI, go make me the best job description or the best job advertisement for this level of employee

[00:28:10] and it does it and it looks better than anything you can drafted and you put it on the internet

[00:28:14] and for some reason that has injected, you know, another company's bias that was out there where

[00:28:19] the generative AI found it from or worse put in job requirements that may be necessary for the job

[00:28:27] but not necessary for your company's actual job, that individual's job in that part of, you know,

[00:28:33] in that location. That's how specific we look at it. Then you're injecting bias in there

[00:28:39] that you have no control over and it's already out there and it's already potentially causing

[00:28:44] harm and discriminating. So there's so much when it comes to generative AI and using it for

[00:28:48] core AI HR functions to ensure that you're not injecting somebody else's bias or somebody else's

[00:28:55] requirements or skills or talent within your organization that is not critical, not necessary

[00:29:03] that you won't hold up and you're going to discriminate. The second part of that is which I think

[00:29:08] is really going to be coming for HR leaders is how it's going to impact the dynamics of the workforce.

[00:29:15] So let's put aside about using generative AI in HR to do the functions but as you see all the reports

[00:29:21] about how generative AI is going to essentially eliminate so many positions within your company.

[00:29:26] Jet, you're not going to need a recruit for these positions anymore because the generative AI can do

[00:29:32] it faster and more efficient and you've seen all those stats out there about all these companies

[00:29:36] predicting doom and gloom and about all these jobs going away and that's a really serious concern

[00:29:42] for HR professionals because like everything else they're going to have to manage the change in

[00:29:47] their organization's workforce about eliminating some of those positions and having generative AI

[00:29:53] do it. We're not going out and recruiting on some of those positions that they normally would have

[00:29:58] and there's significant issues with that if you're going to do layoffs related to generative AI

[00:30:03] you know how is that going to impact your current workforce? You know what we're starting to see is

[00:30:08] well who's normally gets laid off when we have a big reduction in workforce, higher paid workers

[00:30:16] which tend to skew to older workers right? And especially if generative AI can do those

[00:30:20] work faster and more efficient so that's going to have an impact on older workers or you know what

[00:30:26] I like to say are the newer workers. I'm not saying younger workers, I'm saying newer workers

[00:30:30] and this directly impacts all the work TA has been doing to diversify their workforce. All

[00:30:36] you know the newer workers are much different historically than they've ever been before. They're

[00:30:44] more diverse. They've come from a lot of different places all that recruiting efforts whether it's

[00:30:50] on diversity, a diversity of skill, diversity of background to get them in the applicant pool. If you

[00:30:56] want to just simply say well let's just lay off the newest crop of workers because they are you know

[00:31:02] the newest in the company having ingrained themselves is cheaper to do that. Well you're going to

[00:31:06] that's going to have a disparate impact on certain groups and we're already seeing the jobs that

[00:31:10] are going to be displaced by generative AI are going to have a disproportionate impact on women,

[00:31:15] on African-Americans, on Hispanic Americans and it gets very complicated with I'm not a labor

[00:31:19] economist of why that is right about the the jobs that are going to be displaced but that's all

[00:31:24] going to fall on HR both on the front end to make sure that there is no discrimination and then when

[00:31:29] those individuals are laid off. The second part of it too you know when it comes to the generative AI

[00:31:34] equation which again is in HR's wheelhouse is related to now skilling your current workforce to be

[00:31:41] able to use these tools and our disabled workers going to be able to have the same opportunities

[00:31:47] to be able to learn how to become a prompt engineer if they need more time or if they need different

[00:31:52] kind of adaptive devices and same with you know older workers and I'm not trying to be ages

[00:31:56] here but obviously Gen Z who grew up in their phones maybe to able to learn these tools faster

[00:32:03] where you want to make sure your older workers don't say well I've been doing my job the same way

[00:32:07] for 20 30 years I'm just going to quit now yeah right yeah or and you're forcing me out in that

[00:32:12] sense and then it's also producing a lot of anxiety and fear about well no matter how old I am

[00:32:19] if I'm playing training generative AI to do my job at what point am I going to be terminated

[00:32:25] and generative AI is going to take my job and then that's leading to mental health issue so

[00:32:30] I like to say that because you could see that you may just see like well generative AI

[00:32:35] you know it's just going to come in we want to do it but then it goes back exactly to HR professionals

[00:32:40] to kind of again take the lead in this to make sure that these broader generative AI programs

[00:32:45] outside of you know using generative AI to make a job description it's that's still within

[00:32:51] you know HR's job description to make sure these programs are being implemented properly

[00:32:56] so it's a fun question and I'm not sure you're allowed to make predictions but I'm going to ask

[00:33:01] you anyway and I think this this kind of comes into what you were saying earlier

[00:33:06] where do you hope we're going to get to with all of this if we were having this conversation in

[00:33:10] three years time what would we be talking about I would like to see mass integration

[00:33:15] of AI and HR and generative AI and HR and I would like to you know ensure that of course

[00:33:23] HR professionals are still leading the charge in the integration and using of

[00:33:29] the software because you know unlike other areas in the business when you're dealing with AI

[00:33:34] and HR you're dealing with self-rights you're dealing with you know people's ability to to provide

[00:33:40] for their families and that's just you know a different level of care that HR professionals are

[00:33:45] used to doing than other areas of the business so as we all know at this point you know AI is

[00:33:50] taking all over all aspects of businesses and I think that's why HR professionals really need

[00:33:55] to be able to be a very significant part of that equation and ensure that all the long standing

[00:34:02] policies procedures on how to hire an employee how to promote an employee how to pay employees

[00:34:07] that HR professionals use that and don't lose sight of the fact that all that still applies when

[00:34:14] using technology when you know having AI make that decision or assist HR professionals with decision

[00:34:22] so my prediction is that for this to work it's sort of a prediction and sort of a request for

[00:34:28] this to work you know HR professionals really need to not be intimidated by the changes of the law

[00:34:33] or the technology itself go back to the basics and you know when you're dealing with the vendors

[00:34:39] you know you need to make sure that you're asking the right questions of saying how is this

[00:34:44] going to work on my workforce how is this going to work on our specific job descriptions how is

[00:34:49] this going to work with our specific employees or applicant pools and then you know what are you

[00:34:54] going to do to ensure that our own use of it is proper and that's half working with the vendor

[00:35:00] and also internally and this is where you know HR professionals need to take the lead is saying okay

[00:35:06] we need not only to buy these products but we need to then build the governance structure around

[00:35:12] it. Identically what we already have in our employee handbooks and our procedures if an employee you

[00:35:18] know if somebody in HR makes a bias hiring decision and we find out that they unlawfully hired

[00:35:24] you know a man for this position and that was their only qualification generally they would be

[00:35:29] terminated for that because you have equal employment opportunity policies and procedures and

[00:35:34] that same thing needs to happen with AI is saying that the same requirements that we're using

[00:35:39] the hire whether it's a person or whether it's AI are going to be in place and that's where it needs

[00:35:45] to go to work. Keith thank you very much for talking to me. Thank you for having me.

[00:35:51] My thanks to Commissioner Sondaling both Keith and myself will be speaking at HR Technology Europe

[00:35:57] which is taking place in Amsterdam in May. You can get a 50% discount on your ticket by

[00:36:03] going to matolder.me slash europe and using the code mat50 that's maw50 so matolder.me slash europe

[00:36:16] and the discount code is mat50 you can follow this podcast on Apple podcasts on Spotify or via

[00:36:24] your podcasting app of choice please also subscribe to our YouTube channel you can find it by going

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[00:36:44] everything that's coming up on the show thanks very much for listening I'll be back next time

[00:36:49] and I hope you'll join me

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