WEBVTT

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And heat, rain, and other
harsh weather. Arion Loso of the architecture

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firm HGA says that to determine what's
expected in a location, the industry typically

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relies on historical weather data. They're
generally looking at the past, but global

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warming is changing weather conditions, so
Loxso says if architects and engineers do not

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consider future climate change, their projects
may not perform well over time. For

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example, buildings may not have adequately
sized HVAC systems or enough insulation to keep

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people cool during increasingly extreme heat waves, or a property may lack the capacity

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to divert large amounts of stormwater during
intense downpours. Loso co authored a recent

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report that suggests ways to avoid these
kinds of problems. We really feel like

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there's a need for architects and engineers
to be at least looking at the data

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that's provided by the National Climate Assessment
or by the Intergovernmental Panel and Climate Change.

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To make that happen, She says, building codes and standards should be

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updated, and clients should ask architects
to design with climate change in mind.

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Climate Connections is produced by the Yelle
Center for Environmental Communication to learn more about

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climate change. Visit climatec Connections dot
org. Thank you Inland Empire for listening

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to KCAA Radio. The information economy
has a rid. The world is teeming

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with innovation as new business models reinvent
every industry industry. Inside Analysis is your

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source of information and insights about how
to make the most of this exciting new

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era. Learn more at inside analysis
dot Comsideanalysis dot com. And now here's

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your host, Eric Kavanaugh. Yes, oh yes, folks, welcome to

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the future. Indeed your host here, Eric Kavanaugh, the only coast to

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coast radio show that's all about the
information economy. It's time for Inside Analysis

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and today, folks, we're to
talk about the human touch. Yes,

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indeed AI for HR human Resources,
so artificial intelligence for human resources. All

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the folks in HR who hire fire
try to help us through the whole journey

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of working for a company. We're
to learn from a couple of experts.

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Here. We've got Jeff Webb,
head of solutions Strategy at HR tech company

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I Solved, and Jalen Owen,
head of HR at Hames Corporation calling it

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all the way from Alaska. And
Jeff is is over in Houston, and

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yours truly is in Texas as well, So we've got two Texans and an

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Alaskan on the show to talk about
AI and HR. And HR is changing

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a lot these days, largely due
to AI and also analytics and being able

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to track and understand and of course
it's used in the hiring process where they

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have algorithms now that are just reading
these resumes coming across the transom. It's

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changing how we work day to day. All kinds of stuff is happening.

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So let's dive right in. We
talked to the experts first, Jayleen Owen

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of Hames Corporation, tell us about
yourself and how you are thinking about AI

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for HR, Well, I am
an HR professional. My background is in

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industrial organizational psychology. I have just
completed my seven years here being in Alaska.

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I'm not originally from Alaska, however, though I came to join the

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company in twenty sixteen. For the
family Haines, they are are fourth generation

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family owned and operated organization here based
in Sitka, Alaska. We have a

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multitude of diverse operations including retail stores, a grocery store, liquor stores.

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We have gas stations, residential,
commercial properties, you name it. We've

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got a lot of diversity. So
I joined the team here in twenty sixteen

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to help kind of bring them out
of the dark ages. Being on an

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island, there's a lot of challenges, there's a lot of resource limitations,

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and so having my professionalism to be
able to kind of address some of their

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concerns was why I was brought on. Very cool. And we've also got

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Jeff Web out there. Jeff,
tell us about what you do in the

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HR space for I Solved. Yeah, thanks. So I lead the solution

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strategy team with I Soolved. So
my job is to make sure that we're

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constantly looking ahead, evaluating the needs
of our customers and partners, and making

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sure that we're building that into the
roadmap for the technology and the services that

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we deliver to companies here in the
US. So I get to talk to

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a lot of HR professionals, which
is how I got to know Jayleen,

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and I also get to spend a
lot of time looking at how the technology

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and the sort of human experience in
the workplace intersect which has been has just

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been fascinating and evolving so very very
quickly at the moment. Yeah maybe so,

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Jamiene, I'll start with you the
human touch you know, people want

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to be treated fairly, they want
to be treated honestly, and they don't.

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I mean personally. One of my
pet peeves, I can't stand talking

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to computer voices, Like when you
call these it's like I'm a computer and

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I can speak in complete sentences.
I usually throw some abstract philosophy at it

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and like I'm sorry, I didn't
get that. It goes back to me.

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People want to deal with people.
We understand that there are technologies behind

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conversations and behind businesses. That's very
well understood. But how are you navigating

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this change of infusion of AI and
analytics in what is a very personal job

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of hr AH Very carefully? I
believe that there are a lot of pros,

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but there are also a lot of
cons And when it comes to implementing

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any kind of artificial intelligence, whether
it be in human resources or you're going

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to actively take a role, I
think in your business you need to take

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a look at what both positive and
negative challenges may have as implement implications to

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your business. For us here,
we've been looking at things like could it

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automate some repetitive tasks? Being on
an island, we have a limitation of

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bodies. There's literally about eight thousand
residents that live here year round. We

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are driven by kind of a tourist
industry, but because our industry isn't it's

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a year round industry, there is
a lot of concerns related to whether or

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not that is going to be an
efficient decision for us. Is it going

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to be cost effective, is there
going to be the appropriate support. Even

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when we went through the process of
implementing I Solved. I Solved is so

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on the top of the cutting edge
of technology that many of my employees here

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were not too sure if they were
going to like that development, and so

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it's been a challenge. We don't
really have a lot of remote work here

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on the island, but the idea
of being able to support some of our

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employees. I do have one employee
that is in Idaho, and so being

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able to utilize AI the advancements to
connect us so that we don't have that

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distance between us any longer has been
what I've been focusing on. Okay,

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that's pretty cool. How about you, Jeff, I mean, you're obviously

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in the thick of these things at
I Evolved, trying to help organizations leverage

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all this new technology I'm sure you've
sat around and thought about the ethics of

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all this, but it's like the
train has left the station, right,

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I mean, the I joked earlier
today, the chat is out of the

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bag, if you will chat geep. AI is everywhere and we're going to

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have to learn to deal with How
are you navigating this interesting change in how

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we do business? Yeah? Absolutely, well, I mean for us,

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we focus very much on small to
medium business in the US, So you

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know there's about one hundred and seventy
thousand companies we touch every day, and

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really our focus is going to be
to make it consumable and easy and non

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threatening for them. So the trick
here is to sort of is to stay

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focused on the outcomes. Like I
think it becomes very easy with AI to

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become very wrapped up in the arts
of the possible and the technology, and

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the reality is, you know,
you're running a small business. You've got

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a lot of things to deal with. I'm looking to retain good people,

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i want to recruit new people,
I want to develop them. That you

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must stay focused on what is the
outcomes we're trying to drive here, and

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how do do technologies like AI,
how do they help us get their easier

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and quicker and with less work and
less just less friction in the whole process.

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And so for us, it's a
case of thinking about where does AI

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get deployed appropriately, what are the
right tools that you can infuse AI with

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in other words, to make smarter
and more you know, independent, And

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then how do we communicate that in
a way that is honestly not threatening to

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the folks that we're talking to,
who already have too much to deal with

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in technology coming out in every direction
and compliance changes and you know, expectation

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changes from changing workforce. So I
think half of it's a technology question and

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connecting the technology to outcomes, and
the other half is just communicating that in

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a way that's understandable. Yeah,
that's a that's a great line, and

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you know, jainly not throw it
back over to you because you mentioned I

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think the key facet of this whole
equation here, which is automation and automating

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tedious tasks. That's something that HR
technology has a fair amount of. And

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you know, automation is not new
if you think about it. Just to

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be blunt, every piece of software
ever written automates something. That's what you're

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doing. Whether it's a word processing
application or a photo editing application, whatever

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it is, you're automating something.
So automation isn't new, but this machine

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learning and AI is relatively new,
at least in terms of practice. Right.

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We've had AI for probably forty five
fifty years, but it just wasn't

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very It wasn't readily available because it
was very expensive. We didn't have the

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compute power. Now we have all
that compute power, and so we can

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automate things and we can see things
in a different way. That's what kind

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of gets me excited about this stuff
is that machine learning algorithms can crawl around

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in the background and just look for
patterns of things. And what I've noticed

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anytime I've used a tool that has
that kind of functionality is they will almost

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always come up with some thing you
did not expect. And I think that's

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one of the most positive aspects,
is that you're going to learn things about

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yourself and your team and your organization
that you didn't know before, because this

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all seeing eye is just kind of
watching and never blinking and serving up little

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observations. But what do you think, Oh, I definitely think you're right

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when it comes to the idea of
how we could utilize the AI. I

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think you're absolutely right. We're kind
of behind the ball at this point.

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The cat's already out of the bag, and in the case of where the

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cost of effectiveness can meet the actual
resource requirements, it's going to be the

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most vital piece I think any business
can focus on. You also are going

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to have the issues of that privacy
if the information is collecting and analyzing your

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data. Not only are you concerned
about what data does it have access to,

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but the privacy of that especially from
like a perspective of a small business

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when it comes to proprietary stuff,
where we are competition. Here isn't another

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grocery store, it's a hospital,
it's a bank. When it comes to

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establishing the clear policies on how that
data is collected, how it's stored,

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how it's used, ensuring that our
employees aren't gonna be worried about their privacy,

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let alone the company being worried about
ours. I also think that it's

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important to look at the resistance and
how that trust can impact things like job

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displacement. When we start looking at
automating repetitive tasks, the smaller tasks Yeah,

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it's great, especially when we have
a labor issue right now and I

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don't have the skilled individuals to take
over some of these jobs. AI can

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step in. But I definitely do
agree that there's a lot of ethical consideration

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that we also have to take into
consideration. Yeah, right, well,

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I'll bring them Jeff back in and
we talk about things like payroll for example,

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that's a wonderful use case for automation. You want to be able to

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set up a system and then monitor
it. And again getting back to the

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power of these machines and the power
of algorithms, they will catch errors that

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human beings can make. Humans are
error prone, I mean, we just

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are. We get distracted, we're
taking about something else, so we don't

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do something correctly. And the nice
thing about these machines they will catch those

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mistakes. Nothing happened here, take
a look at that. So payroll is

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a great use case for protonomic right
absolutely. You know, there's a whole

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bunch of places where you can use
that capacity that you referenced of AI to

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sort of to learn and look for
changes to great effect, and payroll obviously

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is one of them where you can
you can have an AI platform essentially learn

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lots of normal payroll run for you
as a business, but also learn actually

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at an individual level, what's normal
for people to be paid. And then

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if you make a mistake and it
happens, even with the best, you

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know, best policies and procedures and
even with good automation technology, mistakes can

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be made. People accidentally get paid
twice for example, I've seen that happen

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and having something like AI just go, you know, this is odd,

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this is unusual. You don't normally
do that. Is that a normal payroll

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run for you? Is there a
reason you're suddenly paying a lot more or

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a lot less. Having that backstop
there starts to again reduce the workload on

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the person doing the task and reduce
the because they don't have to check as

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closely that they know that they've got
something running that can do it for them,

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and also just improve the sort of
the general experience less you know,

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pay wrong mistakes are a reason that
people leave their current company. It's nothing

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more annoying than not getting paid the
right amount of money. So yeah,

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it's a great place to put it. There are a bunch of other places,

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you know, we think of those
really as being the sort of three

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areas we think if AI we're really
working in the role of HR, and

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that would be one of them.
It's sort of in the flow of work,

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where you've got AI watching things happen
and helping to sort of improve and

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smooth out the automation process. There
are obviously other areas too, but just

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at the most basic level, getting
stuff done and reducing the workload and reducing

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errors is a huge win for every
overworked, overstressed HR. Our payroll and

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benefits administrats are out there, that's
right. Well, you know the other

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area, and I'm sure is very
rich with possibilities here is benefits and just

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understanding benefits and different packages and different
programs. Now, I don't know about

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you, but I just I shudder
when I look at some manual that I'm

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supposed to read about. I'm like, oh my god, are you serious?

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Like I particularly this whole thing.
Well, that's something that these large

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language models are very good at,
and AI is getting very good at.

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It's being able to take large amounts
of text, feed them into this machine

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and say, give me a summary
to explain what that means to me.

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I'm thinking that's a huge area because
you know, many people probably don't want

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to admit that they don't really understand
what these benefits packages do because that's kind

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of embarrassing. But it's complicated,
right, So Jeff first to you and

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then over to Jayleen about that.
Yeah, yeah, absolutely, it's funny.

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Actually, that would be probably the
other area where we see AI.

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One of the other areas where we
see AI starting to become really a effective

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in sort of improving and elevating the
experience overall of employees and also as a

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result of the business, and that's
personalization and to your point, exactly understanding

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things and helping shape and personalized recommendations. This is a good benefits package for

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someone like you, or here's how
to find out more about this particular set

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of benefits is one of the areas
that we think it's going to have a

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lot of value. There are plenty
of other areas you think about adding in

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the ability of AI to recommend not
just the benefits package that you might want,

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but but training you might need in
your job. You know, people

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in your job should take these sets
of courses to improve improving just any sort

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of set of you know, things
that the company delivers and personalizing them for

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that set of employees. Is you
know you can scale that with AI,

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which is really hard to do when
you're doing it manually with people. That's

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a good point and that gets us
to really the crux of what we're talking

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about is you want to use these
technologies to tackle the tedium and to tackle

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the complexity where possible. And you
know, we had a show a couple

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of weeks ago where we were talking
about scheduling and if you've got let's say

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a hospital with two hundred nurses,
for example, and one of them wants

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to take time off, well,
right now, it's a very manual job

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for a lot of places, and
some person that has to sit there and

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look at the schedule and say,
all right, well if I put Bob

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over here and Susie goes there,
that's a pain in the rear to do.

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But some of these technologies can bang
that stuff out in their sleep.

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Right. It's just like you just
entered the so and so wants this off

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being used, you know, send
it to Fred, senator John, send

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it to Jim or whatever. The
machines can do that stuff extremely well.

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And then also it's a machine doing
it, and it's not Jim, the

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person who oversees who's going to get
you know, a bunch of people mad

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at him because he said, Okay, you do this, you do that.

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I'll throw that over to Jayleen.
That's a classic example of how these

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machines can do a very tricky job
with no bias because they're just getting the

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job done. What do you think, Oh, that's a that's a loaded

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question in my opinion, because I
think that it's important to also to go

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back a little bit to what Jeff
was saying, especially like when you look

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at things like benefit management, employee
classification. Larger companies that you're going to

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have a lot of different types of
plans, how you manage that ensuring that

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they are getting the kind of assistance
that they need that an HR professional like

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myself would sit down and help them
through maybe an open enrollment process. You're

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looking at data validation and the accuracy, and I think AI's algorithms can really

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help us analyze that payroll data,
allowing for those inconsistencies and those errors to

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be flagged, whether it be tax
compliance or just the automation of data in

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general. When it comes to AI, though, in my opinion, I

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think it's really really important that when
we look at the type of AI you're

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using, because, as you indicated, the natural language processing and the way

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that the chatbots are engaging and learning
to assist our employees are HR professionals aware

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of and still act engaged in that
process to be able to utilize that and

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not allow for possible issues to become
an issue. It could be ambiguity and

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maybe the system doesn't provide the information
you want it as an HR professional,

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or it could just be a bias
built into the language model. That's an

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interesting point, and you know we
are in the early phases of this.

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I know with the large language models
there is this tendency to hallucinate. We've

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got to watch out for that.
But AI is it comes in all sorts

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of shapes and sizes. And the
key to your point, Jane, and

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I see Jeff nodding his head as
well, is that when you implement these

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technologies, you always want to be
able to track and trace and understand what

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they're doing. And you have to
watch out for black boxes, as their

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call, that just make decisions about
people's lives. You've got to be very

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careful about that. So you want
the transparency. You want to be able

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to audit the system and to understand
what it's doing and to map out and

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see and you know, there are
all sorts of ways that that's done these

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days with log files and things that
nature. But you definitely want to be

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very careful before you put any kind
of automation into an organization, especially for

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something as serious and significant as human
resources, because that's dealing with the people

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who are your company. And every
company is a bunch of processes and assets

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and people. But folks, don't
touch adalgy. Right back, you're listening

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to Inside Analysis. Welcome back to
Inside Analysis. Here's your host, Eric

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Tabanaugh. All right, folks,
back here on Inside Analysis talking to a

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couple of experts in h R Human
resources. Of course, we've got Jeff

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Webb and Jayleen Owen, and Jeff, I'm going to give you the hard

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question that I'll let Jayleen think about
it for a second. How do you

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help people who have great, deep
skepticism about AI in their organization? How

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do you help them understand that really
they're positive s to this, that the

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negative sides can be dealt with.
How do you talk to people who are

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very skeptical about this stuff. Yeah, that's really the heart of a lot

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of where we're at, I think
as a lot of organizations. You know,

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probably I spent this year traveling around
the US. I did fifty odd

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different cities. I've probably met a
couple of thousand HR people, and a

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lot of the conversations we talked about
AI came up a lot, and some

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of the times there's a lot of
interest in you know, well, what

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are you doing? What should we
do? Sometimes is simply a case of

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please don't use AI in polite conversation. I think we've all reached a point

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where there's so much AI conversation in
the news it's sometimes difficult to cut through

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to the realistic, actual meat of
the topic. So part of the challenge

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is just being down to earth and
realistic about what we're going to achieve now

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and what we should be achieving in
the future, and then put it in

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context, you know. I also
we look at survey results. We do

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studies ourselves of organizations across the US
and look at their attitudes and employee attitudes,

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and we try to be sensible and
clear about what AI is going to

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be good at and what people are
going to be good at, and I

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think we're going to come I think
we're even in this conversation, we're sort

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of circling around on the areas of, you know what, what a really

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good at? What the people do
so much better than any I can do

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for the foreseeable future. That is
reassuring in many ways. But yeah,

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we've asked. I think we ran
a survey earlier on the Shire, couple

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of thousand employees in the US.
We asked what they thought about AI,

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and actually it was close to seventy
percent. I think we're relatively positive that

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it could improve their work experience.
So I think there's optimism. I just

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think there is skepticism, which is
good, and then a little concern about

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what, you know, how's this
actually going to roll out, what it'll

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look like when it starts to become
much more real in the workplace. Yeah?

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Right, And what about Eugenie,
Leen, you obviously have to counsel

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people who come to you and say
I don't like this, say nice stuff

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at all? What's going on here? Tak my job? How do you

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deal with that? Well? For
me, especially living on an island,

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we have a unique social, cultural, and even economic dynamics here there's a

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lot of specific factors that come into
play when we're looking at how you approach

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people in a small community like this. The sensitivity of taking what would be

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a personal possible conversation or interaction and
expecting the individual to possibly embrace that the

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same way that they would if it
was a person. That's a big concern

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of mine. Ensuring that there's a
community engagement through the AI is going to

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be important because those interpersonal relationships matter, and they matter especially to people in

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this local community when it comes to
decision making and being transparent. The use

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of AI really does concern a lot
of people, especially when it comes to

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the build the building of the trust
of that information and when we get to,

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as I stated before, privacy or
even fairness concerns, but to kind

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of speak to how we would be
able to address that skepticism. In my

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opinion, I think it's really just
going to come down to education and awareness,

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providing and offering clear, accessible information
and really bridging the gap between things

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that might be demystifying of that AI. It'll allow those individuals to really see

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what could be augmented and allowing maybe
some showcases where right now, because we

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have a limitation of bodies. My
business, or at least my experience here

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on an island is almost like a
fish bowl or a case study because we

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are limited on the resources here,
and so we have to kind of think

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outside of the box and look at
things that would possibly allow us to maybe

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create a user friendly process to this
new technology. That's a good point,

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JEFFA, bring you back in.
I'm a big lover of data and being

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able to track things, and I'm
just guessing here that one of the cool

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things you can do, and I
saw this when someone uses your technology to

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help with their HR. Over time
you can see, for example, you

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can score resumes coming in to help
you better understand who to hire. But

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then over time you can also see
how well that works out. And the

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real value to AI is when you
get enough data into a system to be

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able to ascertain what works and what
doesn't work, and then you can do

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you know, what they call regression
analysis for example, and go back and

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look, okay, well what did
happen there and why did this not work?

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And you can really learn a lot
about the people who have come into

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your company, who works, who
doesn't work Because something like a corporate culture

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is very difficult to define. But
if you have enough parameters and you have

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enough data over let's say a year
or two years, for example, all

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of a sudden you should be able
to get better and better at who you

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hire, when you hire, what
kind of jobs and roles you actually need

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in the company. All that stuff
theoretically gets better and better over time if

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you're using the technology to track the
data and be able to understand it.

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What do you think about that,
Jack, Yeah, absolutely, that's you

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know, one of the things that
we talk about really early on as organizations

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to think about adopting technology is a
single platform that kind of captures all of

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the employee interactions becomes essential to ultimately
unlocking that power of that data later.

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If you know everything that's happened to
that employee, if you capture every interaction,

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you know, every performance report,
every time they log in, every

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time they check out. Not again, to your point, not in some

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sort of creepy, big brotherish kind
of way, but simply tracking the flow

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of work and things that are happening, and you know who your good employees

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are and you can evaluate that.
You can very quickly start to identify where

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are the good employees coming from where
are we spending the most money where it

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can we save money on, you
know, not hiring people that quit three

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months ef later. You can also
start to do really interesting analysis things like,

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you know, we had one one
company we work with, they wanted

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to move one of their officers.
And what they're able to say was,

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if we move this office from this
location to this location, which employees are

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likely to be at risk of leaving
because their commute has become unpleasant? And

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we already know that's a factor for
those kinds of employees for example, Or

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you know what kind of money should
we be spending on benefits for this group

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of employees? Or you know what's
the salary changes we should make for those

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So get you get good operational uh
you know, data that you can use

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to sort of improve performance of the
business. You also get bigger sort of

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macro perspectives on your organization. What
does the diversity look like at my organization?

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Is it changing? Is it is
it different in different departments? Are

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certain managers more more likely to lose
employees over a period of time than others?

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So there's a lot of power that
you get looking at your own company

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from the data and that I think
is part of the job really is to

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unlock that and make it very consumable
for hr folks and business leaders. I

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actually think there's another area too that
we forget about. You know, we

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just talking about our own you know, I sold as a company. We

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touched the lives of what about seven
million US employees every day. Can you

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think about you think about what that
means when a company, you know,

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you're running a bank and des Moin
for example, and you're not sure why

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your bank tellers are leaving, Well, you have the ability, using these

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kind of technologies to say, well
about are we losing bank tellers faster or

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slower than other bank tellers in Des
Moines or in Iowa or in the US,

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And are we paying more or less? Are we spending more or less

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on benefits? So that capacity to
not only unlock the sort of insight of

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what's happening operationally in your own business
and therefore impact the experience of your employees,

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but to then go benchmark yourself against
other organizations and say, well,

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how are we doing? Are we
better, are we worse? You know,

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what are the areas we could improve
in? That becomes incredibly powerful too,

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So Yeah, you're right. That
sort of capacity to hold and analyze,

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to consume data and sort of spit
out results and insights really starts to

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redefine what's possible for HR people,
business leaders, payroll folks and so on

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to just you know, drive better
business outcomes. Yeah, that's a great

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that's a great point that you just
made. And it kind of gets us

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back to being data driven and doing
these comparisons, because again you might think,

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oh, we're doing a terrible job, are you. If you have

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the capacity to see what your peers
are doing and what those rates are for

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those other companies, now you understand, oh no, this is actually normal.

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And Jamie, I'll throw this one
over to you. The other really

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interesting space I have to believe is
in training and research and learning and being

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able for compliance purposes but also just
for improving the workplace, having people take

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classes on various aspects of the business. Being able to track Hey, we

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rolled out this program to help people
be more sensitive around diversity. For example,

404
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did the complaints go down a month
later or did they stay the same?

405
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Did they go up? You can
kind of better understand what's working and

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what's not working. From an educational
perspective, right, Oh, definitely,

407
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as long as the information that you're
receiving is accurate. Where I get concerned

408
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about is the unintended consequences that the
AI system may produce, especially if that

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information or the data isn't being set
up properly, or the interpretation of the

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00:29:33.359 --> 00:29:37.920
data is being determined by maybe another
AI because you get that bias involved.

411
00:29:37.319 --> 00:29:41.759
But yeah, definitely, I think
that when it comes to the dynamics,

412
00:29:41.839 --> 00:29:48.519
especially within a small workforce like myself, education and training are vital. It's

413
00:29:48.559 --> 00:29:52.039
what determines our success, and especially
me being on an island where I have

414
00:29:52.119 --> 00:29:56.200
a devoid of skill level and education
or even knowledge, and I'm bringing people

415
00:29:56.240 --> 00:30:02.319
in because the labor is sure.
I need to be able to to benefit

416
00:30:02.440 --> 00:30:07.240
from whatever technology can give me and
speed those processes up to give me a

417
00:30:07.279 --> 00:30:11.799
balance between the innovation with the traditional. Yeah, that's a good point.

418
00:30:11.799 --> 00:30:15.440
And you know, speaking of bias, I remember learning about this story is

419
00:30:15.480 --> 00:30:18.960
back in twenty eighteen, Amazon,
which of course is a very big company,

420
00:30:19.079 --> 00:30:23.799
very analytics and AI friendly, very
focused on that stuff. They rolled

421
00:30:23.799 --> 00:30:30.480
out this AI recruiting tool that basically
said don't hire women, and so they're

422
00:30:30.519 --> 00:30:33.880
like, what, oh, no, hold on, turn that thing off.

423
00:30:33.279 --> 00:30:37.519
You do have to watch out.
That's the sort of common sense moment.

424
00:30:37.599 --> 00:30:41.599
I'll throw it over to Jeff.
AI gets stuff wrong. I mean

425
00:30:41.680 --> 00:30:45.920
it is artificial intelligence, and real
intelligence can get stuff wrong. People get

426
00:30:45.920 --> 00:30:48.640
stuff wrong all the time. So
AI doesn't mean it's perfect. It's just

427
00:30:48.759 --> 00:30:53.160
making some recommendation based upon a model, based upon data that's fed and you

428
00:30:53.240 --> 00:30:56.440
got to watch for these things and
watch out for it. And that was

429
00:30:56.680 --> 00:31:02.200
just a classic example that yes,
bias does exist, then you have to

430
00:31:02.240 --> 00:31:03.920
watch out for it. So you
always have to, you know, keep

431
00:31:03.960 --> 00:31:07.279
your human hat on as you're using
things like AI. Right, Jeff,

432
00:31:08.240 --> 00:31:12.880
Yeah, absolutely, And you know, I think Jalen kind of nailed it

433
00:31:12.880 --> 00:31:15.720
a couple of times there, you
know her commentary about transparency earlier, on

434
00:31:15.880 --> 00:31:21.400
her commentary about you know about bias
right now, I one hundred percent agree,

435
00:31:21.799 --> 00:31:23.480
And I think that's the challenge.
We know that you can build it.

436
00:31:23.599 --> 00:31:26.799
You can build a model that inherently
is fine, but you train it

437
00:31:26.839 --> 00:31:32.400
on bad data, it will produce
bad results. And so part of the

438
00:31:32.519 --> 00:31:36.880
you know, the challenge that the
organizations like our as every organization that's using

439
00:31:36.920 --> 00:31:41.519
AI, but especially when AI is
becoming infused in HR processes. The challenge

440
00:31:41.519 --> 00:31:45.400
is to make sure that you don't
allow that to happen, that you're you're

441
00:31:45.480 --> 00:31:48.400
using it in a way that is
ethical and transparent and there's unbiased as physically

442
00:31:48.400 --> 00:31:53.279
possible, you know, for things
like recommending a course that someone should take,

443
00:31:53.359 --> 00:31:56.359
or checking a payroll, or you
know, simply being a chatbot.

444
00:31:56.400 --> 00:32:01.359
We do a lot of work with
chatbots and basic HR questions like is next

445
00:32:01.359 --> 00:32:07.039
Thursday at a company holiday or not? Those kind of things you're relatively unlikely

446
00:32:07.079 --> 00:32:09.160
to see as a significant bias,
but you have to be very careful,

447
00:32:09.440 --> 00:32:15.039
and even for us when we start
to do things like stack rank resumes to

448
00:32:15.119 --> 00:32:20.240
compare them against job openings, you've
got to be really careful to not exclude

449
00:32:20.240 --> 00:32:22.839
people from that process. You must, you know, provide to your point.

450
00:32:23.319 --> 00:32:28.319
You can provide insight, you can
provide recommendations, but ultimately the AI

451
00:32:28.400 --> 00:32:34.799
should be working alongside a professional like
Jalene who understands the broader context and that

452
00:32:34.799 --> 00:32:40.079
that really starts to define I think
how we'll see AI becoming realized in HR

453
00:32:40.200 --> 00:32:43.920
processes. It's a lot of the
time it's going to be working as a

454
00:32:44.000 --> 00:32:49.240
counterpart to a professional who can then
provide context and the other thing that humans

455
00:32:49.319 --> 00:32:52.319
do that AI does not do,
which is empathy. Context and empathy.

456
00:32:52.599 --> 00:32:55.319
It's what we are, you know, we're magnificent context and empathy machines.

457
00:32:55.599 --> 00:33:02.319
AI is a great analytics You should
combine those things you're thinking about affecting the

458
00:33:02.440 --> 00:33:07.759
lives of people in the workplace.
You shouldn't have one override the other.

459
00:33:08.240 --> 00:33:12.960
Yeah, that's a really, really
good point. Empathy is so important in

460
00:33:13.039 --> 00:33:16.400
business, and I think we're moving
through generations right. We were talking in

461
00:33:16.480 --> 00:33:21.559
the break about how we have these
multi generational companies now where you've got baby

462
00:33:21.599 --> 00:33:25.200
boomers and Gen xers and Gen Y
and millennials and Gen Z like. These

463
00:33:25.200 --> 00:33:30.160
are very different groups of people in
how they react, how they respond,

464
00:33:30.319 --> 00:33:34.599
and probably how they respond to data. I mean, kids just grew up

465
00:33:34.599 --> 00:33:37.279
with iPhones these days. They know
how to use this stuff. Baby boomers

466
00:33:37.279 --> 00:33:40.440
did not, and so you always
have to keep that in mind. And

467
00:33:40.440 --> 00:33:45.000
that's a very personal thing. I'll
throw it over to Janean real quick about

468
00:33:45.039 --> 00:33:49.319
ninety seconds. It's very important to
have that empathy and to always be human

469
00:33:49.400 --> 00:33:52.359
to the human people in your organization. Right, Well, yes, definitely

470
00:33:52.640 --> 00:33:55.200
There's one word I would have to
say sums this all up. It's the

471
00:33:55.279 --> 00:34:01.759
authenticity of it. AI comes from
what you tell it and what we program

472
00:34:01.799 --> 00:34:07.920
into it. But as Jeff indicated, the empathy doesn't come natural. It's

473
00:34:07.920 --> 00:34:13.960
something that I'm concerned as we move
forward with this is eventually everything that we

474
00:34:14.039 --> 00:34:16.039
can give it as a personality is
going to be built in there. But

475
00:34:16.119 --> 00:34:21.960
are we going to lose that personal
touch that's the authenticity of the matter.

476
00:34:23.119 --> 00:34:27.679
Yeah, Well, they I've joked
before. I did a show a few

477
00:34:27.760 --> 00:34:30.800
years ago and someone made a joke
about something and I was like, they

478
00:34:30.800 --> 00:34:32.639
told me to be authentic. I
mean, come on, going to be

479
00:34:32.679 --> 00:34:37.079
myself here. I think you care
about what you wish for out there.

480
00:34:37.719 --> 00:34:39.800
Well, these are all excellent points. Well, let's see, we're coming

481
00:34:39.880 --> 00:34:44.679
up on a break here. We're
talking to Jeff Web and Jalene Owen HR

482
00:34:44.760 --> 00:34:47.960
professionals about the impact of AI,
and I think in the next segment we'll

483
00:34:49.000 --> 00:34:52.239
dive into the hiring process and what
these things look for. You know,

484
00:34:52.280 --> 00:34:58.119
you think about Google and search engine
optimization and what happened there. Basically,

485
00:34:58.119 --> 00:35:01.159
Google has algorithms for how they track
and index the web, and then people

486
00:35:01.199 --> 00:35:05.239
are always trying to gain the system, try to get the Google room to

487
00:35:05.320 --> 00:35:07.039
point at them, and so you
know that's got to be happening in the

488
00:35:07.039 --> 00:35:12.159
talent acquisition space where we're trying to
use algorithms to sort through the data and

489
00:35:12.239 --> 00:35:15.440
someone's trying to trick that algorithm to
use the proper words to get them to

490
00:35:15.480 --> 00:35:17.079
look at your resume. We'll pick
that up after the break. Folks,

491
00:35:17.159 --> 00:35:27.960
don't touch up doll. You're listening
to Inside Analysis. Welcome back to Inside

492
00:35:28.000 --> 00:35:35.760
Analysis. Here's your host, Eric
Tabanat all right, folks back here on

493
00:35:35.800 --> 00:35:39.239
Inside Analysis, talking all about HR
and AI as they come together and what

494
00:35:39.280 --> 00:35:44.559
that means for businesses. We've got
Jeff Web of Iesol and Jamie Owen of

495
00:35:44.880 --> 00:35:47.719
Haymes, and Jeff, I'll throw
it over to you. The talent acquisition

496
00:35:47.960 --> 00:35:53.480
side is a very interesting space to
tackle because now you can have algorithms to

497
00:35:53.920 --> 00:35:59.880
capture the text of the resumes and
sort them based upon certain priorities and skid

498
00:36:00.119 --> 00:36:01.920
some that's rise to the top.
So you don't have to go through all

499
00:36:01.920 --> 00:36:04.960
one hundred of them. You can
just use the machine to say, all

500
00:36:05.000 --> 00:36:07.519
right, look at the top ten. But you do want to understand how

501
00:36:07.559 --> 00:36:10.000
it's coming to those conclusions, right, tell us how you folks are able

502
00:36:10.000 --> 00:36:15.440
to use AI for the talent acquisition
side. Yeah, absolutely, you know

503
00:36:15.440 --> 00:36:19.079
it's funny. Actually, let me
even take a step back from the sort

504
00:36:19.079 --> 00:36:22.039
of the premisse of your question,
because certainly, what we've built is,

505
00:36:22.039 --> 00:36:25.239
you know, we've built this into
technology ourselves, is the ability to sort

506
00:36:25.239 --> 00:36:30.320
of start to match the things that
the resume is talking about with the things

507
00:36:30.360 --> 00:36:32.480
that you're looking for, just at
least start to say, well, this

508
00:36:32.599 --> 00:36:37.000
looks like it's a closer match than
this set of resumes here and sort of

509
00:36:37.039 --> 00:36:42.119
stack rank can provide some guidance and
path through looking at the resumes. But

510
00:36:42.239 --> 00:36:45.840
you know that that actually isn't the
problem. The problem most organizations face isn't

511
00:36:46.079 --> 00:36:50.960
I've got resumes to deal with.
It's finding good people in the first place.

512
00:36:51.000 --> 00:36:53.880
It's attracting them in the first place. And you know, one of

513
00:36:53.920 --> 00:37:00.119
the things that most organizations doing terrible
job at is writing job ads. Like,

514
00:37:00.119 --> 00:37:02.360
most of the time I look at
job descriptions and job ads, they

515
00:37:02.440 --> 00:37:06.239
sound like a hundred reasons not to
apply for this company. You know,

516
00:37:06.320 --> 00:37:07.440
you have to be you know,
you have to have a master's degree in

517
00:37:07.480 --> 00:37:09.480
this, and you have to have
served overseas, and you have to be

518
00:37:09.519 --> 00:37:13.119
able to tap down and play the
banjo, and you know, this is

519
00:37:13.199 --> 00:37:15.599
sort of long list of things that
you must be able to do before we

520
00:37:15.639 --> 00:37:17.440
want to talk to you. And
that's I think how a lot of a

521
00:37:17.480 --> 00:37:21.559
lot of people have been taught how
to write job description when they put the

522
00:37:21.559 --> 00:37:24.119
ad out there. And actually,
what we're seeing is a couple of things

523
00:37:24.119 --> 00:37:29.039
going on. One is that terrifyingly
enough, marketing folks are being asked more

524
00:37:29.079 --> 00:37:32.639
and more to help market the company
through the job ads and descriptions. But

525
00:37:32.719 --> 00:37:37.599
AI is also now starting to be
used in there to help people that don't

526
00:37:37.679 --> 00:37:43.039
professionally write job descriptions and job ads
to write things that are actually inviting and

527
00:37:43.239 --> 00:37:46.880
interesting and reflect the culture of the
company rather than simply a list of prerequisites

528
00:37:46.920 --> 00:37:52.599
like I'm sort of assembling a piece
of IA furniture. And so so it's

529
00:37:52.599 --> 00:37:55.679
interesting what we're seeing is AI being
used to start the process earlier. Let's

530
00:37:55.840 --> 00:37:59.880
write really good job ads. So
let's assist somebody in writing a job ad

531
00:38:00.199 --> 00:38:02.599
to get people to come in and
then we can start to use Also,

532
00:38:02.679 --> 00:38:06.519
then you know, a separate set
of AI process to start to rank and

533
00:38:06.880 --> 00:38:10.440
filter through and analyze the contents of
the people that are coming to us.

534
00:38:10.440 --> 00:38:13.760
But I, you know, really
you've got to tackle both ends of that,

535
00:38:14.320 --> 00:38:20.239
or else you're just you're just filtering
bad sets of resumes coming in without

536
00:38:20.239 --> 00:38:22.599
actually getting the good people in the
first place. And that's really the challenge

537
00:38:22.599 --> 00:38:25.760
most organizations face. Let's get the
good people in here. I know this

538
00:38:25.840 --> 00:38:30.119
is probably something jayleens, you know, good wax lyrical about for quite some

539
00:38:30.199 --> 00:38:32.719
time to given her world. Yeah, there you go, Deleen over to

540
00:38:32.760 --> 00:38:39.400
you. Well, I definitely can
see where utilizing it to do all of

541
00:38:39.440 --> 00:38:45.199
the things that I can't do myself
when I'm trying to recruit. But I

542
00:38:45.199 --> 00:38:50.719
would be concerned about AI putting a
priority on specific skills or even on the

543
00:38:50.760 --> 00:38:55.159
initial screening process, and how people
could possibly crack that, and and being

544
00:38:55.199 --> 00:39:00.599
that there may be this black box, as you had indicated before, with

545
00:39:00.800 --> 00:39:07.039
kind of the reasoning behind the decisions, as long as we don't have any

546
00:39:07.079 --> 00:39:10.320
type of an over reliance on that
automation, that we're actually looking at the

547
00:39:10.360 --> 00:39:16.320
results making sure that we're taking into
consideration those unique talents or potential contributions,

548
00:39:16.440 --> 00:39:22.079
not just the status of maybe the
black and white data. Yeah, they

549
00:39:22.079 --> 00:39:24.679
can do the job, but are
they going to be a cultural fit to

550
00:39:24.719 --> 00:39:28.960
my people and are they going to
be the kind of skill level that I

551
00:39:29.039 --> 00:39:32.039
need. Yeah, that's probably the
hard part, right, and I'll throw

552
00:39:32.360 --> 00:39:37.480
over to Jeff to one for a
second. But back culture, it's a

553
00:39:37.599 --> 00:39:43.239
very difficult thing to define, but
it is very palpable. So you know,

554
00:39:43.519 --> 00:39:46.320
are you able now to some extent
to be able to sort of absorb

555
00:39:46.360 --> 00:39:51.360
that, ascertain that and then apply
that kind of that that lends, if

556
00:39:51.400 --> 00:39:53.719
you will, to the hiring process. Yeah. I think this is where

557
00:39:53.760 --> 00:39:58.719
the key and again that it gets
to really the heart of this whole conversation

558
00:39:59.239 --> 00:40:01.079
is around the rest things that we
should expect AI to do for us,

559
00:40:01.079 --> 00:40:07.239
And there are things that we should
continue to have HR professionals and you know,

560
00:40:07.519 --> 00:40:12.280
recruitment professionals bring to the table that
their skills and again back to context

561
00:40:12.280 --> 00:40:15.880
and empathy, those are the things
that we do better than anybody else.

562
00:40:15.920 --> 00:40:19.599
So I think the role here for
AI is let's reduce the workload, let's

563
00:40:19.599 --> 00:40:24.320
do initial analysis, Let's provide some
sort of guided path through the process,

564
00:40:25.159 --> 00:40:30.880
and also to your point to start
to look for things like bias creeping into

565
00:40:30.880 --> 00:40:34.559
the hiring process. So don't forget
we should also be using AI, you

566
00:40:34.599 --> 00:40:38.480
know, one does to evaluate the
diversity of your organization. A certain part

567
00:40:38.559 --> 00:40:42.960
of your organization suddenly becoming a whole
lot less diverse. Are they're starting to

568
00:40:43.000 --> 00:40:46.679
recruit just certain types of folks.
So's there's an opportunity to have some checks

569
00:40:46.679 --> 00:40:50.880
and balances with the AI too,
to make sure that you're not starting to

570
00:40:50.880 --> 00:40:54.920
become some you know, monolithic organization
where everyone looks the same. But yeah,

571
00:40:55.000 --> 00:41:00.639
no, you should. You should
be recruiting for fit with the organization.

572
00:41:00.679 --> 00:41:04.679
I think there's a lot of a
lot of studies are showing that actually

573
00:41:04.719 --> 00:41:09.960
recruiting for the sort of their attitudes
and the enthusiasms and the personality are actually

574
00:41:10.119 --> 00:41:15.039
far moreive for organizations than just recruiting
on skills and experience. And so you

575
00:41:15.079 --> 00:41:19.039
want again a I could do some
of this, but you've got to have

576
00:41:19.119 --> 00:41:22.679
it paired with the people whose job
it is, the professionals to actually evaluate

577
00:41:22.719 --> 00:41:25.599
more effectively. But that's the whole
point. That's the point of AI.

578
00:41:25.719 --> 00:41:30.000
The point of AI is to free
those people up to do those things,

579
00:41:30.079 --> 00:41:34.880
to take the workload off their plate
that you know, we did a study

580
00:41:34.960 --> 00:41:40.039
and we found about forty percent of
HR leaders spent most of their day answering

581
00:41:40.079 --> 00:41:45.360
the same set of questions, which
is a horrific waste of talented people's time,

582
00:41:45.440 --> 00:41:49.239
not to mention horribly probably demotivating.
So you wanted to use tools like

583
00:41:49.280 --> 00:41:52.840
AI and automation to take that stuff
away so that they then have the time

584
00:41:52.880 --> 00:41:58.760
to bring their skills and training and
background and empathy and context into the things

585
00:41:58.800 --> 00:42:02.960
that those things really make difference.
I'm sure absolutely Recruiting should be the first

586
00:42:02.960 --> 00:42:06.679
thing that you're looking at there.
How do we recruit the right people?

587
00:42:07.079 --> 00:42:10.639
Yeah, you know, I had
Steve Lucas, who is now the CEO

588
00:42:10.719 --> 00:42:15.039
of Boomy, but before you with
the CEO of a company called IIMs ICIMS.

589
00:42:15.079 --> 00:42:19.159
You may have heard about the recruiting
company, and he showed me something

590
00:42:19.159 --> 00:42:22.880
that I think they did for Target
which was just brilliant, which is video

591
00:42:22.880 --> 00:42:27.920
interviews. So instead of just entering
your CV, which is so cold and

592
00:42:27.960 --> 00:42:32.400
black and white and hard to describe
and hard to even really understand or enrich

593
00:42:32.480 --> 00:42:36.519
if you're on the writing side,
No, he just had people video and

594
00:42:36.599 --> 00:42:37.800
Hi, my name is Bob I
really like doing X, Y and Z

595
00:42:37.840 --> 00:42:42.199
blah blahlah blah. So I was
like, that is freaking brilliant because now

596
00:42:42.199 --> 00:42:45.480
you can see the person instead of
having to rely on text. I mean,

597
00:42:45.519 --> 00:42:50.039
this is an example of how we
can leverage all these new technologies to

598
00:42:50.719 --> 00:42:54.000
kind of jumpstart the process get and
get through some of the more painful stuff,

599
00:42:54.159 --> 00:42:58.239
because when you can look and talk
to someone, that's a whole lot

600
00:42:58.239 --> 00:43:01.400
different than looking at a resume,
right, j oh, definitely, especially

601
00:43:01.440 --> 00:43:06.880
if you can see the person.
I've heard about the automation of like interview

602
00:43:07.119 --> 00:43:12.519
scheduling and how that's been utilized.
My biggest concern, especially being where I'm

603
00:43:12.519 --> 00:43:17.079
at on an island, is that
the lack of explanated ability being able to

604
00:43:17.199 --> 00:43:22.400
understand what they're going to be selling
me as an applicant, I need to

605
00:43:22.480 --> 00:43:25.480
know and be able to see them. And having that opportunity, Oh,

606
00:43:25.480 --> 00:43:30.719
that would definitely change the game.
Yeah, that's good stuff. And then

607
00:43:30.920 --> 00:43:34.880
maybe one last little point on this. The other nice thing getting back to

608
00:43:35.000 --> 00:43:38.639
tracking things, is that Jeffa throws
to you if you have your AI suggesting

609
00:43:39.440 --> 00:43:44.599
people to hire, well and you
track every time that we went with the

610
00:43:44.639 --> 00:43:47.880
recommendation and how often did that work? Yep? Right, Like you always

611
00:43:47.880 --> 00:43:51.199
want to be tracking this stuff to
see it's like, well, for some

612
00:43:51.239 --> 00:43:52.960
reason, it keeps recommending people who
don't work out. So we got to

613
00:43:52.960 --> 00:43:57.679
go back to susy algorithm do something
to sort of rejigger that whole thing.

614
00:43:57.719 --> 00:44:00.280
But what do you think, jee, Oh yeah, absolutely, And that's

615
00:44:00.280 --> 00:44:01.679
really the definition of AI anyway,
is you know, you sort of you

616
00:44:01.719 --> 00:44:05.920
build a model using machine learning of
the model of the world, the model

617
00:44:05.920 --> 00:44:08.840
of the process. But really when
it starts, what defines it as really

618
00:44:08.920 --> 00:44:14.559
artificial intelligence is the capacity for the
system to alter that model as it learns,

619
00:44:14.639 --> 00:44:15.400
right, to be able to say, you know what, I keep

620
00:44:15.440 --> 00:44:20.159
recommending this and it's not working out. I should start to edge around the

621
00:44:20.159 --> 00:44:23.400
parameters, move around and change the
kind of recommendations of making. And again,

622
00:44:23.440 --> 00:44:28.199
if you have the I mean just
the plug for the single platform approach

623
00:44:28.239 --> 00:44:30.639
here is because you're able to see
everything from sort of pre higher all the

624
00:44:30.679 --> 00:44:34.559
way through, you actually see what
happens to people, and so as a

625
00:44:34.639 --> 00:44:36.519
result, you can see, you
know, when we hired this kind of

626
00:44:36.519 --> 00:44:38.760
person, they were wildly successful.
This kind of person not so much from

627
00:44:38.760 --> 00:44:42.639
this place we hired them, they
stayed from this place we hired them,

628
00:44:42.679 --> 00:44:46.000
they did not. That becomes really
valuable. Yeah, and you can understand

629
00:44:46.000 --> 00:44:50.800
the contact too, you know,
it gets into the concept of a good

630
00:44:50.920 --> 00:44:54.159
manager. And I personally, I
think that management is one of the most

631
00:44:54.280 --> 00:45:00.320
underappreciated jobs in the business world.
I'm old enough to remember. And thennineteen

632
00:45:00.360 --> 00:45:02.079
eighties when we had that big flattening
out. I don't know if anyone remembers

633
00:45:02.119 --> 00:45:05.880
that, but like they got rid
of all this middle management because it was

634
00:45:05.920 --> 00:45:07.400
just an expense. It was like
a cost they didn't want to deal with.

635
00:45:07.800 --> 00:45:12.519
And that's a problem because when you
have a good manager. It took

636
00:45:12.559 --> 00:45:15.199
me till I was I think thirty
four or something, and I had a

637
00:45:15.199 --> 00:45:17.599
good manager. I've moved up to
Seattle for him, and I'm in my

638
00:45:17.679 --> 00:45:21.559
first meeting with him one on one. We get to the last like you

639
00:45:21.559 --> 00:45:22.760
know, five minutes of the meeting, he goes, is there anything I

640
00:45:22.760 --> 00:45:25.639
could do for you? And I
remember like looking over my shoulder, like

641
00:45:25.719 --> 00:45:31.400
did some VP walk in? And
that goes to show you what my thought

642
00:45:31.480 --> 00:45:36.159
process wasn't around management. I was
like, oh my god, I apparently

643
00:45:36.159 --> 00:45:38.840
have never had a good manager before, because exactly what a good manager should

644
00:45:38.880 --> 00:45:42.840
say to you. Good managers don't
just tell you what to do. Good

645
00:45:42.920 --> 00:45:46.000
managers are there to help you do
your job well. It's supposed to be

646
00:45:46.000 --> 00:45:50.800
an encouragement role. Yes, there's
a disciplinary side to it, but that

647
00:45:50.840 --> 00:45:53.719
should be a very rare component to
the process, not the not the norm.

648
00:45:53.800 --> 00:45:58.119
Basically, so all this technology,
what we're saying here, should be

649
00:45:58.280 --> 00:46:02.639
used to encourage posit management to a
courage good experiences at companies. We want

650
00:46:02.719 --> 00:46:07.440
happy employees. We want the employee
experience to be good because then the customer

651
00:46:07.480 --> 00:46:09.599
experience will be good and all kind
of works outs. But you folks are

652
00:46:09.639 --> 00:46:16.519
listening to Inside Analysis, all right, folks, time with the podcast bonus

653
00:46:16.559 --> 00:46:22.599
segment here on Inside Analysis talking to
Jeff Web of I Saul and Jaine Owen

654
00:46:22.639 --> 00:46:27.000
of Hames all about AI and HR
are coming together. And we were just

655
00:46:27.079 --> 00:46:30.559
chatting about bias and you know,
what is bias and how do you detect

656
00:46:30.599 --> 00:46:34.199
bias? And then once you've detected
bias, what do you do about that

657
00:46:34.599 --> 00:46:37.239
detection of bias? Right? I
mean, these are the hard questions to

658
00:46:37.320 --> 00:46:42.519
answer. And I'm not sure if
we're in an employee friendly or a hiring

659
00:46:42.559 --> 00:46:44.840
friendly, you know, like buias
market, sellers market. I'm not sure

660
00:46:44.840 --> 00:46:46.559
where we are right now. I
think it's a very strange world. But

661
00:46:46.639 --> 00:46:51.199
in any case, we want to
know where there's bias and and help people

662
00:46:51.280 --> 00:46:54.239
understand the biases that they have.
Because what if Mark Twain said, biases

663
00:46:54.320 --> 00:46:59.880
you're is the set of experiences you've
accumulated by the age of eighteen or something

664
00:47:00.119 --> 00:47:02.000
like that, I think that's that's
your bias, because that's what you grew

665
00:47:02.079 --> 00:47:05.719
up with. But jeff A throw
it over to you first, how do

666
00:47:05.719 --> 00:47:07.880
you identify bias? And then what
do you do about it when you find

667
00:47:07.920 --> 00:47:10.800
it? Yeah? Yeah, this
is the thing that I think is most

668
00:47:10.800 --> 00:47:15.519
critical as we think about AI in
HR. You know, I say looking

669
00:47:15.519 --> 00:47:20.679
for bias and transparency. Transparency is
huge, Like it's really important to be

670
00:47:20.840 --> 00:47:23.239
clear why you're using AI and what
you're using it for. And we were

671
00:47:23.360 --> 00:47:27.400
talking about you know, this sort
of the model that I think is the

672
00:47:27.440 --> 00:47:30.599
way we're evolving anyway, which is
that there will be AI systems that are

673
00:47:30.599 --> 00:47:35.079
focused on tasks. They're very task
centric, and they will go do something.

674
00:47:35.119 --> 00:47:37.400
They'll go, you know, look
for an area, they'll go look

675
00:47:37.440 --> 00:47:40.199
for change they'll make recommendations, and
ultimately I think they will feed into other

676
00:47:40.280 --> 00:47:45.760
AI systems that can more broadly analyze
what's actually happening in your organization. And

677
00:47:45.760 --> 00:47:49.119
I think at that layer, you
know, if you start to have you've

678
00:47:49.119 --> 00:47:52.239
got task centric AIS, you've got
more general evaluation, you can start to

679
00:47:52.360 --> 00:47:57.440
look for biases. You can have
AIS that are trained to look for biases,

680
00:47:57.679 --> 00:48:01.360
especially if you think about the capacity
to compare yourself to other similar organizations

681
00:48:01.400 --> 00:48:04.960
across the world to say, well, wait a minute, are we suddenly

682
00:48:05.000 --> 00:48:08.119
skewing in some weird direction that is
not normal for our business? And then

683
00:48:08.159 --> 00:48:13.239
I think the third thing is the
human aspects. Right again, we come

684
00:48:13.280 --> 00:48:19.000
back to this that the vision here
should be for AI to be unshackling the

685
00:48:19.239 --> 00:48:23.079
hr professional, the people who are
trained to deal with people, unshackling their

686
00:48:23.119 --> 00:48:28.880
time and energies by dealing with the
day to day and helping them have more

687
00:48:28.920 --> 00:48:31.840
time, energy and focus to bring
to the table their empathy and their context

688
00:48:31.840 --> 00:48:37.519
to their understanding and their training of
what's normal and what's appropriate for that organization.

689
00:48:37.760 --> 00:48:42.880
So you've got to you know,
got to got to see this as

690
00:48:42.920 --> 00:48:47.079
AI working as a part of the
team with an HR professional not replacing the

691
00:48:47.119 --> 00:48:52.599
functions of HR professionals because they're too
critical. That's a really, really,

692
00:48:52.679 --> 00:48:55.480
really good point. I'll throw it
over to Jail into comment. Done that,

693
00:48:55.519 --> 00:48:59.800
but I think it's a great closing
argument here, because you're right,

694
00:49:00.320 --> 00:49:02.920
human resources must be run by humans. At the end of the day,

695
00:49:02.920 --> 00:49:08.039
it's a very human process hiring and
firing and managing people. It's very human.

696
00:49:08.079 --> 00:49:12.599
Empathy is crucial or no one's going
to want to work at your company.

697
00:49:13.159 --> 00:49:15.519
I can tell you that for sure, and that reputation will get out.

698
00:49:15.599 --> 00:49:17.199
Jalene, what do you think,
Well, I definitely think it needs

699
00:49:17.239 --> 00:49:22.920
to be representative of the population that
you're serving. Not all businesses cut the

700
00:49:22.960 --> 00:49:25.960
same. We all, as HR
professionals, know that we have different demographics.

701
00:49:27.639 --> 00:49:31.159
Sometimes bias is built into that demographics. Maybe as for an example,

702
00:49:31.280 --> 00:49:37.679
you're in law enforcement and you don't
have the ability to be a little lax

703
00:49:37.760 --> 00:49:42.440
on certain types of requirements. But
I think it's going to be very important

704
00:49:42.480 --> 00:49:46.880
for the human resource professional who implements
the AI into their businesses to ensure that

705
00:49:46.960 --> 00:49:53.000
they're representing their population. They're focused
on reducing any biases, looking at how

706
00:49:53.039 --> 00:49:58.760
they can ensure that that data is
transparent and that when you do find those

707
00:49:58.800 --> 00:50:02.440
issues and you're reviewing that data,
you're cleaning it up, you're looking at

708
00:50:02.719 --> 00:50:06.719
what it is and how it is
impacting your job, whether it be that

709
00:50:06.760 --> 00:50:10.480
you're reevaluating the model. Is this
something that we want to continue that rationale,

710
00:50:12.199 --> 00:50:16.119
or maybe there's some advancements that algorithmic
adjustment has occurred and the technology is

711
00:50:16.159 --> 00:50:21.119
now catching up with the needs that
as an HR professional we may not have

712
00:50:21.159 --> 00:50:24.000
had in the very beginning. That's
a really, really good point too,

713
00:50:24.039 --> 00:50:27.679
And this is we're on a learning
curve. We're all on a learning curve

714
00:50:27.679 --> 00:50:30.119
here, and we're going to be
on that curve for a while. That's

715
00:50:30.159 --> 00:50:34.280
one of the most interesting comments I've
heard from someone in this business who said

716
00:50:34.320 --> 00:50:37.480
about machine learning, it's about learning, and he meant human learning. So

717
00:50:37.599 --> 00:50:42.280
we need to learn how these things
work, what they do, what they

718
00:50:42.360 --> 00:50:45.519
don't do, and watch out for
the watch out for the holes, the

719
00:50:45.519 --> 00:50:49.320
gaps, the biases, and learn
about ourselves in the process too. And

720
00:50:49.800 --> 00:50:52.800
that's what life's all about, right, is learning and having fun and working

721
00:50:52.800 --> 00:50:57.000
and doing cool things. But folks, look these two up online. Jeff

722
00:50:57.039 --> 00:50:59.920
Web that's Jeff with the g G
E O F F Web with two b's

723
00:51:00.360 --> 00:51:04.599
of I saw Jayleen Owen. That's
j A y l E n l e

724
00:51:04.760 --> 00:51:07.960
n E Owen from Haytes. Thanks
so much for your time and thanks for

725
00:51:07.480 --> 00:51:10.920
putting the human touch on AI.
Well talking next time, folks, you've

726
00:51:10.960 --> 00:51:17.639
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Empire talks back. The attitude that, well, the little guy cannot

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win seems to prevail despite the fact
that over time we've seen that the little

809
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guy, if he is persistent,
he becomes the big guy. Empire talks

810
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back, No, it's because maybe
people figure out little knowledge is like smoke.

811
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It leads to the fire. Empire
talks back. I think this drive

812
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for equality, this drive for justiceab
is gathering steam as opposed to fading out.

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I think more and more people realize
the importance of the freedoms that America

814
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represents. Empire Talks Back with Wallace, Allen and Friends Sunday mornings at ten

815
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am on AM ten fifty CASEAA.
But what seems like things are fine coming

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Time Network Communications. How's your Internet slower

817
00:59:01.559 --> 00:59:05.719
than what you were paying for?
Feeling boxed in with the high cost of

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the Internet, it can be frustrating
and expensive, and with net neutrality coming,

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it's not getting any easier. Ready
for a better high speed Internet service?

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And you're ready for Cybertime And they're
local and right in your own backyard.

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Cybertime provides connectivity for all our transmissions
you're listening to at CACAA. Cybertime

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00:59:22.159 --> 00:59:25.880
is locally owned and will respond to
your needs with the best service. It's

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00:59:25.960 --> 00:59:31.079
chris cool, fast and sleek.
Cybertime uses the latest leading edge microwave technology

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00:59:31.159 --> 00:59:35.960
to be able to offer clients a
safe, reliable public or private network that

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00:59:36.000 --> 00:59:40.400
fits almost any budget size. Numerous
local city agencies rely on Cybertime's microwave private

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00:59:40.480 --> 00:59:45.599
network for their most critical mission applications. You should too. Get connected,

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00:59:45.719 --> 00:59:49.360
stay connected, get smart, get
cybertime. You can Google, text or

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00:59:49.400 --> 00:59:53.480
call Cybertime Network Communications at nine O
nine seven ninety five nine five five nine

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00:59:53.519 --> 01:00:00.320
Cybertime Network Communications in Kalamesas DA CACAA, lowl into N one oh six point

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01:00:00.360 --> 01:00:06.719
five F M K two ninety three
c F Brino Valley. I'm doctor Anthony

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01:00:06.800 --> 01:00:08.320
Lyzerwitz, and this is climate connections