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welcome everyone to the current edition
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of the Warton AI SiriusXM podcast series
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here on artificial
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intelligence I'm Eric bradow Vice dean
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of analytics here at the Wharton School
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also the KP Chow professor of marketing
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statistics and data science today's
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episode as all of our episodes are is
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sponsored by analytics at Wharton and AI
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at Wharton and today we're going to talk
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about a topic that I think it's hard to
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walk down the street or talk to anyone
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in business and not have them speak
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about which is AI and Human Resources so
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I'm here today by two of my colleagues
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the first is my colleague from the
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management department Matthew Bidwell
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Matthew is the Shing Jang and Y da
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Professor a professor in the management
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department he's also the faculty
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director of a center that's a big part
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of analytics at Wharton Wharton people
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analytics initiative and he's also the
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academic director of Wharton Center for
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human resources program Matthew welcome
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here to our show thank you very much for
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bringing me on Eric a it's great to have
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have you here I'm also joined by my
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colleague Sunny Tay Sunny is an
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associate professor of operations
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information decisions at the Wharton
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School and also teaches many of our
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courses on AI so Sunny welcome to the
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show thanks thanks for having me it's
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great to have you both on such an
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important topic so let me start with the
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beginning Matthew maybe I'll start with
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you um how since you're one of the you
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are the faculty director now of Wharton
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people analytics um how do you think AI
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is going to affect the way that we
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manage people what are both the concerns
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that you have and in equally importantly
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what are the big
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opportunities uh big question I mean
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obviously we need to think a little bit
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about how we Define ai there's kind of
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you know these days when we think about
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AI we kind of leap straight to chat GPT
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and large language models and so on or
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what people a lot of people would call
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the generative AI part where the
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computer the large language model is
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generating a response yeah um yeah if
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you look back historically last five
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years um people have used AI almost to
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describe anything that involves numers
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so it's it's a broad range um yeah I
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think with any of these Technologies as
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ever there's a lot of opportunities to
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kind of improve how we manage people uh
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when we look at how people are managed
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so much of what goes on is kind of gut
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decision making this kind of intuition
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and we have pretty much a century of
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research suggesting that our guts are
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terrible decision makers that actually
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there's a reason why we should be
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thinking with our brains rather than our
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stomachs um and so more broadly when we
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are more systematic when we are more
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thoughtful when we rely on data in
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making decisions who do we hire who do
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we promote how do we manage people all
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those sorts of things we usually make
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much better decisions and so I think the
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extent to which AI helps us be more
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systematic in doing that um it's going
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to be really helpful it's already being
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helpful there are obviously big concerns
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um
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I think kind of three spring to mind um
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so one big concern everybody has is bias
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and
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discrimination um again we know there's
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a lot of bias in the labor market we
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know our guts are discriminating all of
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the time um yeah the good news is
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probably most of the time AI is going to
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be less discriminatory but we think it's
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going to be discriminatory um you know
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particularly when we look at some of
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these more sophisticated large language
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models right they have been trained on
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the Corpus of data that is out on the
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internet even when people aren't being
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deliberately sexist and racist um that
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embodies a whole set of cultural
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assumptions so take sexism for example
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there's been some very nice studies that
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kind of look at word embedding models
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and other things that are trained on
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kind of the Corpus of text you see on
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the internet and they show not
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surprisingly that we think words to do
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with careers are more closely related to
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men's names and words to do with kind of
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home life and family more closely
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related to women's names and so once you
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start using those models to make
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decisions about employment I think the
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risks of bias and discrimination are
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very serious um and I think one of the
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things we worry about particularly more
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generally with kind of using algorithms
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rather than judgment in managing people
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is if you have a manager that
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discriminates that's a problem for the
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people working for that manager if you
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have an algorithm discriminates the fact
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that I can apply a hiring algorithm at
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scale across an entire company across an
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entire industry the sheer volume of
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people that are potentially affected is
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huge um so I think that's one of the
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advantages of scale and the
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disadvantages of scale yeah and so I
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think that is I think that is a very
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live concern um if I can go on a little
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bit just talk about my other two
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concerns um I know we have many other
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questions but I think another couple of
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things that we're thinking about
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um algorithmic algorithms like any
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technology have often been applied in a
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fairly punitive way in HR um so I think
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the the classic example of this is
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scheduling software um and so with
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scheduling software it's very tempting
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if you're an engineer sitting in kind of
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your office trying to do the right thing
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you're like how do I increase
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productivity and the way I increase
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productivity is by carefully matching
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people's schedules to shifts in demand
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during the day and so I'm running
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Starbucks I want to give somebody a
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shift that starts at 7 in the morning
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and then runs till 10: well by then
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we're through with the kind of office
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rush and then I want them to go away for
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a while so I don't have to pay them and
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then maybe I want them to come back
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between 4 and 6 and so what you find is
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these schedules end up creating
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schedules that are great for the company
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but have proved terribly damaging for
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the people who actually have to try and
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fit their lives around what the
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algorithm thinks and probably frankly
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end up causing long-term damage in the
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organization as well because you
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maximize that match between supply and
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demand but you end up driving up
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attrition as people won't stay with
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those sorts of schedules and so I think
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there's a broader issue I mean it's
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always ATT tension in managing people
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you know how much do you take into
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account kind of what those people think
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but I think when you have people managed
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by AI you have a bunch of assumptions
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that have being baked in by the
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schedulers by the engineers whoever they
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are increasingly detached from what's
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going on in the ground and I think that
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that often leads to some really bad and
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disrup Ive decisions and so I think done
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well we can incorporate a lot of these
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algorithms um and you know manage people
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better but it does require us to really
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think about how these algorithms are
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being used and have kind of that closed
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loop so we engineer something and then
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we say okay what's actually happening
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and we're very alive to the problems
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it's creating and go back and
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re-engineer it I think when you kind of
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just sit down do an optimization problem
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then kind
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of put it out into the world and let
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everybody suffer the problems um that
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creates a lot of damage too so those are
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some of the things I'm worrying about I
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think one of the things that we always
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talk about is that you know first of all
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what is the objective function you're
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optimizing that's the first thing and I
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think we would all agree and I'll turn
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it over to sunny in just a second would
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be you know these things should be a
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decision support tool the minute you
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automate them you have those dangers of
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you know it up you know if you'd like
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maximizing some objective that may not
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be good for the employees and certainly
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may not be good for the firm so Sunny
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let me turn things over to you since I
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know for a number of years you've been
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one of our Pioneers in teaching AI to
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our students um what's changed like why
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are things so why is everyone so excited
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today you know I'm a statistician I've
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been here at Wharton for 28 years we've
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been doing you know kind of big data
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science for a long time what's unique
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and what's changed about today that's
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made I'm sure everybody want to take
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your class everybody be interested in
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every single thing you're working on so
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I I think you I I think you know you
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touched on it just a second ago uh but
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this this this tension between a
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decision support which is what a lot of
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technology has been doing for a few
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Generations now moving to this world uh
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potentially um either recommending a
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decision or even automating decisions
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and that that's you know what we do at
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work all day what we do is make
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decisions right and that's what
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businesses do organizations are are
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optimized to do and so a technology that
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can uh Ser as that that can make
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decisions or recommend decisions has
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implications uh for all parts of the
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organization people compare it to you
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know electricity has a potential to
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change everything so I think that's one
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part of the uh the reason that people
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are energized about uh this particular
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topic uh the other thing that's that's
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exciting but also somewhat concerning is
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that I think there's more
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unpredictability right now around AI
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than there has been for tools past right
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so we think about as we scale up these
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models people are seeing more emerging
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capab capability that they would not
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have expected so let me press you on
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this just this one topic for a second so
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I can imagine uncertainty in a few
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things one is um I type something into a
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large language model chat GPT Bing AI
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Etc something comes out I type the same
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thing in maybe something doesn't the
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same thing doesn't come out so that we
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could call that in the measurement
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literature test retest reliability
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that's one possibility one is sunny Tay
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changes one word in the prompt prompt
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engineering if you'd like something
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radically different comes out what form
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of uncertainty are you talking about or
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maybe it's the measurement one that I
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think about or maybe it's a different
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one right no for when I when I think
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about uncertainty in this context I'm
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thinking about the uh the question of
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where these uh Technologies can add
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value to the jobs we do right and so if
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I as a person who uh designs jobs or an
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organizational planner thinks about
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where it fits in uh the answer is
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changing in ways that I think are a
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little bit unpredictable and so if we
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think about what it can do now what it
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can do uh tomorrow uh even the people
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who are at the frontier of this
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technology find themselves quite
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surprised these days that we didn't
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think it was going to be able to do that
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and so that kind of uncertainty um
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combined with the fact that it has the
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potential to affect decisions everywhere
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uh have have have a lot of potential for
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you know sort of thinking for for for
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change uh in ways that we don't exactly
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know what's coming but it's is
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energizing in a way so Sunny maybe you
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could just clarify something for me and
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for all of our listeners here on our
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show um the things that generative AI
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models can
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do they don't I mean the algorithm
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itself the statistical engine itself
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doesn't just come up with it right I
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mean somebody has to have programmed it
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to be able to do a certain type of
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problem it's not like it generates
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solutions to problems it just generates
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let's say you wanted to you wanted your
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AI engine to make some decision about
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how to optimally schedule something
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which Matthew said somebody a programmer
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somewhere had to have said this is a
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problem this AI engine should solve it's
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not like the AI engine searched around
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the world and said let's solve time card
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scheduling problems the algorithm
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doesn't decide the problems humans help
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the algorithm decide which problems to
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solve right humans on the input side
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absolutely do tell it do help it
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understand what problems to solve at the
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same time they're incredibly general
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purpose so what capable and flexible
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enough to solve is is quite quite
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impressive yeah I know Matthew you
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wanted to jump in here and talk about
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kind of this this idea of kind of the
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breath of problems and what the future
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might be I mean well I think kind of on
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the uncertainty piece I think it's it's
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very interesting I mean Sunny knows much
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more about this than I do just for
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anybody listening at home so I'm kind of
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mainly curious to hear what he thinks
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but I mean STS me a lot of the
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uncertainty is is how good they're going
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to get how quickly I mean I think you
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know you said and we're all talking
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about it and everyone wants to talk
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about it it's partly because we're all
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so surprised by kind of just the leap in
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capabilities over the last year um of
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these models kind of just blowing
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through the touring test in a way that
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we thought was a long time off but the
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big question is have we now reached
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another kind of plateau where you know
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at which point you kind of say these are
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neat tricks and they can do some things
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quite well but the lack of accuracy the
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hallucinations all of those sorts of
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things are we ready to turn over large
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processes to them wholesale I'm not sure
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or they're really going to improve and
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the thing it makes me think about is
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self-driving cars I mean I remember like
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s or eight years ago when we were
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thinking about buying our next car I
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thought this is the last car I'll ever
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buy because you know by the time I'm
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ready to buy another car all cars will
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be self-driving you know there will be
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no steering wheels why would we have
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them and it turned out
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you could get like 90% of the way but
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that last 10% proved really hard and so
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the question in my mind is great Point
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could we see something similar or do we
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think really they are going to keep
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improving at this rate yeah no
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absolutely I 100% agree I the challenges
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with all these tools right is are that
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and you mentioned this I think a little
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bit when you talked about bias
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discrimination and managers is that all
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these tools are embedded in a context
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where we have I don't know 200 250 years
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of infrastructure about what to do when
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a manager gets it wrong right we
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understand how to deal with human
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decision error anytime you're talking
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about putting one of these tools in
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place which include self-driving cars it
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includes large language models and it
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gets it wrong we don't have that legal
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organizational infrastructure and that
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has been an absolute uh has been a
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constraint and I I suspect it will
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continue to be so we may well have hit a
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plateau it's just that whenever I think
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about what the future is going to bring
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with these tools you know I think this
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is if I think about the history of
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science a little bit this is uh somewhat
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rare in that you have a situation where
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where you have a tool that can do
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certain things and the scientists are
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not trying to figure out how it works
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right that hasn't happened very often in
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history and so this uncertainty is what
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I I tend to tend to caveat some of those
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comments because of that uncertainty
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which is new I think when you compare it
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to other technological innovation so let
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me ask you both next about what I'll
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call application areas that you think
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are let's be positive people are about
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extremely positive so for example one
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area that Matthew you already mentioned
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was about hiring let me let me ask you
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the following if the following would be
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a good example or a bad example so I'm
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even going back to my days so prior to
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coming to Wharton you two may not know
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this I was at the educational testing
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service in Princeton and we were working
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on automated scoring algorithms for
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essays long time ago people may not have
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called it AI but we were ingesting the
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words and trying to construct scores but
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not for the purpose that when Matthew
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Bidwell takes the SAT that's the score
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he's going to get but how do I use
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humans in a most efficient way when I
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have millions of essays to score how can
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I use an engine to basically do a first
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pass algorithm and then humans will come
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in on the really tough ones so let's
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take hiring as an example but you use
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any example you want why can't I use an
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AI engine to do a first pass algorithm
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to look at a thousand résumés that I get
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for a job 950 get pruned off by the
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algorithm and then the 50 that seem to
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have the credentials that match what I
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want I then use humans and intervention
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to go in so do you have any concerns
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about that two-step process now bias and
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discrimination Can Happen by the
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algorithm so maybe there's some in that
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950 but what do you think about that and
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also if you have a better example than
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mine I'm sure our listeners would love
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to hear no that's great I would totally
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do that um so and I actually I mean
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there' have been some experiments with
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this um generally they've worked out
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reasonably well I always say it's not
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some I'm it's not so much that I'm a
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huge AI Optimist I'm just human skeptic
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um so I mean think the challeng is when
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you look at people how people actually
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make hariring decisions it's so
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haphazard that hariring actually I think
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is one of the places where this tends to
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work really well now I mean we can get
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back to that question about kind of is
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it decision support is it actually
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making the decision I mean one thing
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just to be aware of is I think in
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practice that is a very fine line I
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think most of the evidence
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is people tend to do what they're told
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um and so frankly if the algorithm says
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you should hire this person most of the
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time that's what they're going to do and
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so you can't you know kind of when we
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say well there's a human in the loop so
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it's okay yeah but are they really I
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mean once they've been told this is the
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right way to do it they're going to
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largely follow the advice but yes I'm
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actually I I think that is one of the
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better places I think there are concerns
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about bias um my guess is in the vast
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majority of these cases the bias of the
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AI is still orders of magnitude less
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than the biases of the human rers so I'm
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I'm reasonably bullish on that as a case
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so Sunny what's your thought on both the
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example I gave Matthew just talked about
00:17:06
and maybe what do you think is the most
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promising area where AI can have a
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transformative effect on human resources
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today I want to underscore and pick up
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on something you said which is Let's Be
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You Know positive people I think the uh
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framing of the conversation uh in the in
00:17:20
the broader uh the Press uh maybe has
00:17:22
been too much uh Zero Sum with employers
00:17:26
and and employees or or manag
00:17:28
and and workers and and and there may
00:17:30
certainly be some of that but there uh
00:17:32
it seems that there's a lot of
00:17:34
opportunity to use these tools in a way
00:17:36
that um enhances employee experience
00:17:39
enhances employee well-being I was
00:17:41
talking to an executive last week um
00:17:44
that's using generative AI to write
00:17:46
performance reviews and first of all it
00:17:48
saves them a ton of time but the second
00:17:50
thing is that they're able to use that
00:17:51
excess time to do one-on-one mentoring
00:17:53
and coaching uh so there are a lot of
00:17:55
opportunities to maybe improve and they
00:17:57
also by the way uh provide more frequent
00:17:59
performance reviews so it's almost on a
00:18:01
monthly basis instead of by anual so
00:18:04
lots of opportunities um where where we
00:18:06
can think about um the employee you know
00:18:08
a lot of these tools what they're doing
00:18:09
is they're taking parts of the work that
00:18:11
we may not enjoy so much there so
00:18:13
definitely there are definitely uh
00:18:14
places where we can think about using AI
00:18:17
um at least as a first order uh first
00:18:19
order ad first order application uh to
00:18:22
think about how how can work be better
00:18:24
how can we do a higher touch better job
00:18:26
and making sure our employees stick
00:18:28
around are happy and are being
00:18:29
productive so we're here on the Wharton
00:18:31
SiriusXM AI series uh on talking about
00:18:35
artificial intelligence this is
00:18:36
sponsored by analytics at Wharton and AI
00:18:38
at Wharton and again I'm joined today by
00:18:40
my colleagues Matthew Bidwell of the
00:18:41
management department and sunny Tay of
00:18:44
our operations information and decisions
00:18:45
department so Matthew let me just ask
00:18:47
you um one of the things I love doing
00:18:49
with my MBA students actually I've been
00:18:51
doing this for years now is I always
00:18:53
start out one of the lectures and I say
00:18:55
you know AI in that case machine
00:18:58
learning learning is coming for your job
00:19:00
which kind of Industries or which areas
00:19:04
of the workforce do you see you know in
00:19:06
some sense if you were advising our mbas
00:19:09
or undergrads like I don't know this
00:19:11
seems like a pretty risky area to invest
00:19:14
in today as a career any particular jump
00:19:17
out in you and thinking like wow I don't
00:19:19
know like for example I'll pick my home
00:19:21
Department if I was in the Creative
00:19:23
Marketing business today coming up with
00:19:25
advertisements I'd be thinking I don't
00:19:28
know seems like AI engines could do a
00:19:31
pretty good job of you know coming up
00:19:33
with a massive combination of features
00:19:35
of ads that seem to be effective I'll
00:19:38
just pick one from my home Department of
00:19:39
marketing any anything come to your mind
00:19:42
I'm nervous about this I mean i' I've
00:19:44
chatted with sunny about this before
00:19:45
we've had kind of two decades of
00:19:49
people making predictions about what
00:19:51
work is going to go away based on AI and
00:19:55
in retrospect they've mainly been
00:19:56
hilariously wrong um so I kind of feel
00:19:59
like this is this an area that's very
00:20:02
hard to protect we've recently seen
00:20:03
these things saying you know when we
00:20:05
look at which jobs are going to be most
00:20:07
affected by these kind of new AI
00:20:10
Technologies like things like English
00:20:11
teachers are at the top of the list and
00:20:12
you're just like no no I mean if I think
00:20:15
about which jobs are likely to be safest
00:20:17
from AI I cannot see chat GPT
00:20:19
maintaining control in a class of 14y
00:20:22
olds it's just not going to happen so I
00:20:24
think it's very hard I mean we are
00:20:26
seeing I mean you mentioned kind of
00:20:27
creative I think uh freelance graphic
00:20:29
designers have already really taken a
00:20:32
big hit um so we're seeing some jobs um
00:20:36
essay Mills so if you made your money by
00:20:38
ghost writing papers for college
00:20:40
students I've got really bad news for
00:20:42
you um stack Overflow has just been
00:20:44
laying off people kind of providing
00:20:46
advice with
00:20:47
programs but we're see we're seeing kind
00:20:50
of these narrow kind of slightly strange
00:20:52
kind of niches getting wiped out but I'm
00:20:55
not yeah I'm nervous about making big
00:20:57
predic
00:20:58
about what's going to what's going to be
00:21:00
affective I'm with Matthew on this I'm
00:21:02
I'm relatively uh optimistic in the
00:21:05
sense that uh we certainly you might
00:21:08
expect to see some verticals uh affected
00:21:11
quite a bit maybe maybe customer service
00:21:13
operations customer basing operations
00:21:15
you know but that's been sort of true of
00:21:17
of tractors and zeros copers and
00:21:18
everything in between by and large the
00:21:20
evidence seems to be saying you know for
00:21:23
large language models for uh for example
00:21:26
that all of us are going to be using
00:21:28
them to some degree will make us a
00:21:29
little bit more productive the
00:21:31
productivity gains will be uh promising
00:21:33
but it'll be gradual and so uh we'll
00:21:35
able to be maybe be able to get rid of
00:21:37
some parts of our job we don't like
00:21:38
become a little bit more productive and
00:21:40
any job loss will be at a pace that the
00:21:42
economy hopefully will be able to absorb
00:21:44
it without any problems yeah I mean I do
00:21:45
think if You' been if you'd predicted
00:21:48
when the internet came in that one of
00:21:49
the occupations that would be worst
00:21:51
affected was journalists you know we'd
00:21:53
have asked you to show your working
00:21:54
right I mean there are kind of it it's
00:21:57
quite unpredictable how these things
00:21:58
play out so another question I'm sure
00:22:00
our listeners here on SiriusXM uh and
00:22:03
our podcast would like to know about is
00:22:05
how are you to as Educators using it in
00:22:08
your own classes you like for example
00:22:10
are you going to allow students to
00:22:12
submit assignments using generative AI
00:22:14
are you going to encourage its use are
00:22:16
you going to take certain parts of the
00:22:18
material that you're teaching students
00:22:19
and say actually you'd be better off
00:22:21
just learning it through a generative AI
00:22:23
engine so Matthew I'll start with you
00:22:25
and then I'll go to Sunny who you know
00:22:27
one could argue your entire course is
00:22:29
about this so I'd like to start with you
00:22:30
Matthew how are you going to use it in
00:22:31
the courses you teach um it's still a
00:22:34
work in progress I have to say I mean so
00:22:36
I teach class on people analytics part
00:22:38
of that I get people to um analyze data
00:22:41
sets um you know I've tried throwing my
00:22:44
problem sets into chat GPT it's made
00:22:47
some fairly Elementary errors which has
00:22:50
made me reassured that it's not going to
00:22:52
make me completely redundant but I think
00:22:54
I'll be encouraging my students that
00:22:56
that is a way to to work on it but that
00:22:59
they need to understand what the answers
00:23:02
are that just expecting chat GPT to get
00:23:04
it right is going to lead them astray I
00:23:07
think it's it's a big problem I think
00:23:10
for us though I mean as a kind of
00:23:12
particularly saying the management
00:23:14
department you know a lot of the way
00:23:16
kind of in the social sciences we have
00:23:18
tended to evaluate people and get them
00:23:19
to learn is go write a paper and it's
00:23:22
going to take us a while to figure out
00:23:24
how to redo pedagogy when that is just
00:23:27
so easy just to kind of get an AI to do
00:23:30
so it it we are one of the industries
00:23:32
actually that I think is most affected
00:23:34
in in some ways and we're not going to
00:23:35
lose our jobs but we're going to really
00:23:37
have to I hope but we're really going to
00:23:39
have to change how we do what we do and
00:23:41
sunny both in your answer I'd love to
00:23:42
hear since I know there's a at least in
00:23:44
one of the two courses I'm well aware of
00:23:46
that you teach there's actually a
00:23:47
significant coding portion so if you
00:23:49
could talk you know I've heard some
00:23:51
people say it doesn't matter therefore
00:23:53
whether you know r or python because you
00:23:56
can do the conversion back and forth
00:23:58
therefore it's just you got to be able
00:23:59
to program in something and we'll let
00:24:00
chat GPT do the rest but how how are you
00:24:03
thinking about it yeah no so absolutely
00:24:05
so so I'm I'm somewhat fortunate in a
00:24:07
way because Ai and analytics are so
00:24:09
Central to the courses I teach that
00:24:11
these questions are I can just move them
00:24:13
directly to the center you know and I
00:24:15
think what you said is absolutely right
00:24:16
it's first order these days for students
00:24:18
to understand how you think about a
00:24:21
coding workflow that involves large
00:24:23
language models right so what changes
00:24:25
where does the time go how much coding
00:24:27
do you need to be able to know to use
00:24:28
this effectively these are all questions
00:24:30
we don't quite know the answer to but I
00:24:31
think belong in the center of these
00:24:33
types of courses and then another course
00:24:35
I teach on AI uh asked some of the
00:24:37
bigger questions the questions Matthew
00:24:39
raised bias ethics those sorts of things
00:24:41
that we're just not quite prepared for
00:24:42
yet but that managers are absolutely
00:24:44
going to have to deal with over the next
00:24:45
two decades or so uh those are also
00:24:47
Central to uh how we how we how we spend
00:24:49
our our class time and the the uh
00:24:52
there's just there's there's there's so
00:24:53
many emerging questions every year new
00:24:55
questions it's been um I mean there's
00:24:57
there never enough time so maybe just in
00:24:59
the last minute or so that we have I'll
00:25:00
ask you each for a 15-second answer so
00:25:02
I'm an
00:25:03
employee what do I need to know about AI
00:25:07
that's going to help me do my job better
00:25:08
like what's the one thing I should know
00:25:10
how to do as an employee Matthew any
00:25:12
thoughts um experiment I think basically
00:25:16
just try things play with the technology
00:25:19
get online and see where it can take
00:25:21
over parts of your job and make you more
00:25:23
effective sunny I would say Embrace uh
00:25:25
we prepared to embrace change we're just
00:25:27
during a period where I think the way we
00:25:29
do uh functions and operations and
00:25:30
business processes is going to start to
00:25:32
change quite rapidly and from an
00:25:34
employees pect perspective I think just
00:25:36
mentally they should have that mindset
00:25:37
that I should I need to uh stay on top
00:25:39
of how these things are changing well on
00:25:41
behalf of analytics at Warton and AI at
00:25:43
Warton um I'd like to thank my
00:25:45
colleagues Matthew Bidwell and sunny Tay
00:25:47
for our episode here on AI and Human
00:25:49
Resources thank you for joining us
00:25:51
thanks s thank
00:25:56
you oh
