When we are entering this field of a very competitive industry,
and we know it's competitive, and we know what's out there,
when we step into this field, we are going to show up
and we're going to decide from the beginning
to make responsible decisions.
And what I have learned is that it's much easier
if you make those decisions from the get-go,
because what takes more energy and more time
is going back and fixing something
that's fundamentally broken.
HTTTA
It's how to talk to a I with your host,
go-to-go and with the synth mind.
Ladies and gentlemen, boys and girls, children of all ages,
dogs, cats, robots and everybody in between,
especially you, trustworthy users of artificial intelligence,
this is HTTTA, how to talk to AI.
I am your host, Wes the Synth Mind, Synth Mind Wes,
and as always, in a galaxy gleaming with gigabytes
where gadgets scavenize our future,
one gallant glowing voice gears up to grapple
with artificial intelligence, grandest queries,
the grander gateway to AI's greatest grace by genius,
guided by gusto, she delves gallantly
where machines glean and grow.
Might you ask, ladies and gentlemen,
gravitates towards the genuine, the gregarious go-to-go.
Gee, how are you this week?
Hi, I'm fantastic, but especially on this occasion,
I don't feel deserving of so many adjectives
because we have this incredible guest joining us.
Joining us today is M. Alejandra Parra-Orelani,
or MAPO for short, way easier to say.
MAPO enjoys tackling challenges at the intersection
of technology, organization, leadership and society.
So trustworthy tech is a sweet spot,
and that's what today's episode is gonna be about,
trustworthy and ethical applications of AI.
She's currently the VP of Ethical Innovation and Policy
at the Global Portfolio Division at Takeda,
where she builds solutions that enable teams
to develop trustworthy tech.
Prior to that, she led a think tank at Meta
focused on privacy enhancing technologies,
digital identity and XR.
She also developed responsible AI products and services
at Quantum Black, which is AI by McKinsey,
and helped launch the firm's inaugural open source project.
MAPO has previous experience as an engineer,
naval officer and attorney, and holds degrees
from these little tiny schools you may have heard of
called MIT, Harvard and the United States Naval Academy,
also known as Canoe U, where I happen to go.
She writes and speaks about leadership in tech
and advises startups.
She also volunteers her time mentoring women
and transitioning military veterans in her spare time,
MAPO, left to paint, invent silly songs,
debate the meaning of life and play with dogs.
What else is there to say MAPO?
Thank you so much for being on the podcast.
Thank you so much for having me on the podcast.
I'm really excited to join, especially with so much energy
and excitement from that amazing intro.
Thank you so much.
We bring it, we bring it.
I'd like to start, if we may,
we're gonna have, I think, plenty of time
to talk about the applications and the ethical use of AI,
both by just the casual user,
as well as corporate employment.
How did you find yourself into the world
of artificial intelligence,
given the diverse background that you have?
I wish I could say that there was some grand design,
but there wasn't.
I really lead my life through curiosity.
What am I curious about?
What am I interested in?
And very early on, back in the high school days
and college days, I was really into engineering.
I tried it out.
I learned that I wasn't as genius at it
as some of my colleagues at one of the schools
that you mentioned earlier, MIT.
So I thought to myself, hmm,
I wanna do something that I could be really, really awesome at,
just like these people were awesome at engineering.
And so I started to explore and just see what was out there.
And while I was in the Navy,
I had the chance to work in this little weird role
called legal officer.
So it was kind of like, I don't know,
paralegal role within a ship,
because there aren't enough lawyers to go around
to every ship in the Navy.
And so I got really interested in the law,
went to law school.
And then while I was practicing law,
I tried out different kinds of practices.
Some were very interesting on paper in theory,
but in my lived experience,
they just weren't really hitting the right notes for me.
And so I was able to reconnect back with technology
and try out legal practice in tech law
and policy and compliance.
And this was around the time that AI was really starting
to finally pick up more traction.
And topics around ethical AI were really starting to get
a little bit more mainstream.
And so I think just the confluence of my own interests,
my own experiences,
and what was actually happening out in industry
led me to this world of AI.
I think you have a very topsy turvy little path.
You have a unique perspective there,
both from the engineering side,
the zeros and ones and math and science,
and then also the more philosophical side
with the legal approach.
Yeah.
I just have to say that this is so fascinating
to hear all the rules that you have undertaken
and impressive results on your energy shines through.
You know, as being a female,
I weirdly urge to ask like, how was it being in the Navy,
going through engineering, being in AI,
because I have my small experiences
with male dominated industry.
I am an architect by training
and I worked in big data analytics company,
so a bunch of developers,
but maybe if we could touch on that,
just for my own curiosity and interest,
just what was your experience
and how you're experiencing AI space yourself right now?
Yeah, it's a great question.
I don't reflect on it probably as much as I should.
I will say when I look back at my journey,
my professional journey, it hasn't always been easy.
And sometimes it hasn't been easy
because of just being in, as you mentioned,
these kind of male dominated industries or settings.
The Naval Academy for me in particular was tough
because at the time when I was there,
I think there were about 12% women.
So it's very, very few numbers of people
that you can have certain shared experiences with
and connect with.
But I think that looking back,
what all of these experiences have taught me is,
number one, find your allies.
You have to find your allies and they're out there,
not just women.
I've had a lot of really wonderful sponsors,
allies, supporters, friends,
just plain old friends from all different backgrounds.
And I think that looking for those people
and really connecting with them and focusing on them
instead of the bad stuff,
I think is really, really helpful
because I think this is true for everyone,
not just women, but everyone.
None of us find success or build careers by ourselves.
We all do it with the help of other people around us.
And I think that just focusing on finding those people
and connecting with them has played a huge role.
But I will say, it's not always easy.
There have been so many different masks
that I've tried to wear to see if I can communicate
in a different way that is more impactful,
that helps people to see me differently
because they might have preconceived notions
based on how I look or what my gender is
or what my voice sounds like.
I'll never forget, there was a guy when I was in the military
who, when I took the lead on some organization,
he said to me, you're never gonna succeed.
You're too giggly.
You laugh too much.
People are never gonna take you seriously.
And wrong, thanks.
Thanks for letting me know.
But I became self-conscious about the giggling
and the laughter and all of that.
And so I tried on different masks
to see if I could show up differently.
And I did learn some communication skills
with that kind of, we'll call it that kind of feedback.
But in the end, I had to find myself.
I'm a giggly, happy person,
and I had to find ways to lead through that.
And at the very end of this leadership experience,
that same guy came back up to me and he said,
I'm sorry, I was wrong.
I actually, I was wrong.
And you've changed my mind on what leadership looks like.
Now I'm paraphrasing,
it wasn't quite that powerful of a moment,
but it was really, really amazing for me to experience
someone being, number one, so bold enough to come
and tell me you're gonna fail because of how you sound
or because of how you come across.
And then number two, that same person being humble enough
to come back and say, you know what,
I was wrong and I learned something.
And I've just taken that with me the rest of my career
and tried to remember that people come
with preconceived notions.
Some of them will be bold enough to tell you to your face,
others won't actually,
and they'll just hold it in the backs of their minds
and prejudge you secretly.
And then at the same time,
a lot of these people are on their own learning journeys
and they can learn new and different ways
of working with people.
I really love what you bring up and I want to,
I will transition to a very responsible AI with this,
but I also want to say that for me personally,
jumping into YouTube space,
there is internet comment section.
I've seen all the type of comments on our channel,
so I was very much ready.
And I am actually happy that I joined
when I'm a bit more mature.
And I expected everything.
I was like, okay, whatever comes.
And I have to share,
I don't know, Wes, if I shared that with you,
but the support and also respectful communication,
my audience is 90% male.
On top of that, the engagement, the comments,
I never experienced something
which would be really disrespectful
besides some funny jokes.
And also not just that,
but also like our community at SimphMinds,
everyone is so supportive, so respectful,
and everyone willing to help.
So yeah, I have to say my experience
throughout architecture with some Russian constructors
were not the easiest,
but I have to say maybe we as a society
are moving towards that more inclusivity.
And this is my transition to responsible AI
about representation and that we have diversity.
When we talk about ethical AI,
what we are teaching that female male experiences
are also integrated in a learning data set, let's say that.
So any kind of your experience
from when you talk about responsible AI and representation,
any thoughts that you have preconceived?
Yeah, I think one of the things that really interests me
about the general topic of ethical AI or responsible AI,
whatever label you wanna put on it,
is that there are so many questions
that sit at a very technical level
all the way up through big, huge systemic questions.
And what's really interesting
is that the technical solutions filter up
and might aggregate and accumulate
to become these big systemic impacts.
And then the systemic goals
that we might want to be achieving,
if you break them down,
they sometimes filter down through to very technical
or design or very kind of specific product level solutions.
And so I think that this question of representation
becomes both a complex, it's a complicated question, right?
Because there's so many different parts to it,
which is one of the things
that I think is interesting about it.
But I find it such a fascinating topic to think about
because it requires us to focus on the goals
of whatever it is that we're trying to build.
What are the goals that we're trying to achieve
when we build an AI system?
And then what are the implications
of how we are building it?
So it's not just like the end product
and what its impact might be, but how are we building it?
And will that, again, like those little decisions
that we make along the way,
will they accumulate and add up to something
that is representative, that is inclusive,
that is going to ultimately on a grand scale,
achieve the types of goals we want
for how we might be interacting with that product
or what the second and third order consequences
might be across society.
So this question of representation, I think,
is a really big and important thread in the conversation,
that broader conversation of ethical AI.
And it's really hard.
It's a really hard one.
It's hard for us outside of AI, I think, as a society too.
Just grappling with these tools
because they're so compelling what they're able to do
and what they're able to produce.
But if you get a peek behind the curtain,
there's some ugly stuff in there
based on that training data.
And also too, just if you want something that's this,
if the goal is artificial general intelligence,
we would hope it's representative of our society's bests.
And there's just the people building it right now.
It's still not representative enough with females.
Only about 22% in big AI positions
that are putting together these tools,
that are making those technical decisions
that ultimately have to be circular.
Yeah, Wes, 100%.
And you have this beautiful visualization
on one of your blog posts,
which is responsible as much about the small stuff
as it is about the big stuff.
And this visualization, it's like the circle
where it says the first is, should we build it?
Can we build it?
And then finally, are we building it right?
Just to give you a bit of a context
why this stood out to me recently,
I went and tested 432 AI writing tools.
Wow, that is awesome.
Check out the list on her page.
It's nothing short of sheer madness and insanity,
but go to bore the strain for all you
so you wouldn't have to just wade down in the muck.
That is some of these things.
Yeah, I was saying to Wes, I think after around 100,
I was like, okay, I want to give up,
but then I'm already at 100.
I have full data set that I scraped, which is 432.
Okay, keep going.
At 250, I was like thinking I'm losing my mind.
But the reason I'm bringing this up
because this gave me such an insight
because we always talk,
where is startups popping up left and right.
I think this list I did probably already outdated,
where is probably another 400.
But what I saw is I have this comprehensive overview
that the amount of startups which spin out things
probably on a hackathon over the weekend,
no considerations, is huge, is really huge.
And I understand where, like I understand competition,
speed, like your investors are pushing you.
So I'm curious to hear your thoughts
when there is these forces in the world,
especially when you're trying to build something.
And in case you are trying to think responsibly,
but then you have the whole competition, the market,
everyone breathing into your neck.
If you're not going to release something right now,
there is five other startups next week,
which beat you to the market,
and they are now claiming the branding of text to whatever.
So my question probably is like,
how startups could address this outside forces,
even though if they have a really nice intentions
from within.
Yeah, I have to be very honest.
I don't think there's a magic answer to this.
I really don't.
I do think that there are founders out there
who are really committed to being responsible
in their innovations and have decided,
have made the decision to say, okay,
when we are entering this field of a very competitive industry
and we know it's competitive and we know what's out there,
when we step into this field, we are going to show up
and we're going to decide from the beginning
to make responsible decisions.
And usually those companies are led by
really incredible people who maybe choose some principles
that they want to align to,
some values that they want their startup
or their new organization to have.
And they design everything to those values
and those principles, just like you would design a product
to design principles or technical specifications.
They're part of the business model.
They're part of the product in and of itself.
And it's doable.
It's not easy.
It's certainly not easy, but it's doable.
And what I have learned is that it's much easier
if you make those decisions from the get-go,
because what takes more energy and more time
is going back and fixing something
that's fundamentally broken.
You look at a lot of the big tech companies,
sure, they're very successful.
When you look at how many people they have employed
in their PR, communications, policy,
legal, compliance departments,
to go back and fix stuff or reclaim some sort of level
of just their perception and their reputation,
they spend a lot of money on that.
They have the money to spend on it,
but they spend a lot of money and time on that.
Whereas if they had been building with these principles
and values from the get-go,
it's not that they wouldn't have to spend money
on compliance and legal and all of that,
but the focus would be different.
If they wouldn't be sweeping up a mess,
they would be building upon
a really strong foundation of trust.
And so I don't know if you've noticed,
but a lot of the newer startups that are popping up
around AI have safety at their core
or trustworthy tech at their core,
because I think people have realized
that it's not just like a good, nice thing to do.
It's actually a very smart business decision also.
It's just better overall.
And so on the startup side, I don't think it's easy,
but I think it's doable.
I think the part that is interesting
and also shifting a little bit on the investor side,
I think that when investors expect certain types
of performance, then the incentives are there
to perform in that way.
And so increasingly we're seeing,
I'm sure you've noticed a lot of investors
who are focused on certain principles
or have certain areas of social impact
that they want to aim toward,
whether it's something around the environment
or something around closing equity gaps
or whatever the case may be.
And those incentives from the people that bring the money
I think are really helpful to shaping the future
of startups and technology in the world.
So it's not easy, but I think the incentives
are starting to shift.
And I think that startup founders
are really starting to prioritize
some of these kind of net positive things
for however it is that their product will impact their users
and then society at large.
I don't know what your thoughts are,
but I think it's hard.
It's very hard.
I think we get some benefit from the fact
that we have more altruistic younger generations
that give more to charity
that are generally more supportive of causes.
In fact, I'm pretty sure there's,
this might be more in your realm, Gota,
but a lot of marketers, if they support the causes
of tons of that's important to Generation Z,
millennials, things like that,
that's really the pathway to earning their business.
So we have, I think, a good starting point
because these people are the ones that generally
are engaging in these startup activities.
It's like Gota said, it's gonna be hard
and maybe they don't get a VC
that has some of those central
to their priority that's investing.
How do you, from the perspective of someone
that's just, that's coming up, has a super exciting new idea
and is immediately shot out of a cannon
in terms of the interest in it?
I may be speaking from a little bit of experience here,
but how do you recommend someone continue to hone
and sharpen that kind of steel to remain,
have to have ethical AI use and ethical employment central
to their core values?
And Wes, I would just add that maybe not just startups,
but also corporations who are looking into integrating AI
and building the whole team coming up.
I'm looking, I just want advice from me.
I just, I just, I need to set it for everybody,
but that was for me.
We can get to corpse.
Mappo, please answer, I need to take some notes.
So selfish, our audience.
Whatever.
Okay, first problems, Wes, and then everyone else.
I think, again, I just have to emphasize,
this stuff is not easy.
So one of the things,
and the reason I'm emphasizing this
is because I listen to these discussions all the time
of people saying, oh, all you have to do
is have these principles and do this and do that,
and whatever.
This stuff takes time, it takes energy.
And when you're a small entity,
and even when you're a large corporation,
go to something you said earlier,
it's competitive out there.
Like it's real.
Like you can't just sit there
and think deep thoughts for months and then say,
ah, now we know how to build something responsibly.
It doesn't work that way.
So I wanna say this just with that understanding
of the fact that doing things,
changing the way that we build technology
and adapting it and evolving it
so that things like responsible practices
are built into the way that we work,
that doesn't happen overnight.
We don't change the way we do things overnight.
So I do think though that there are a few things.
Now we are lucky enough
that we've had so many different companies,
the Microstalks, the Googles,
the OECDs, an organization
that's not a private corporation of the world,
come up with this incredible collection
of ethical AI principles.
So that hard work has been done.
Pick one, look it up.
You'll find if you compare and contrast them
that they're mostly similar.
There are nuances here and there,
but they're mostly similar.
And pick one that works for you.
And then start to think about
how is the work that I'm doing?
How is the product that I'm building
aligning to these principles?
Have to sit down for eight months
and do a deep study on it,
but take a couple of hours to sit down
and figure out like,
am I actually even thinking about these principles?
And if so, how is my workflow
laddering up to them in some way, shape or form?
It's a thought exercise.
It shouldn't take you forever.
It takes a couple of hours.
And I think that just having that conversation
with yourself or if you are in a broader,
like in a larger corporation with a few team members,
just having that conversation from time to time,
once a month, a couple of times in a month,
as a starting point,
I think is a really good place to start.
Because if you're priming your brain
to think about those principles frequently enough,
what you will find is you start making decisions
that suddenly you're like, oh, hold on.
Did I even think about the transparency side of this yet?
And then you realize that question,
I'm picking transparency just for fun,
but that question of transparency
suddenly starts to pop into your brain once a day,
twice a day, three times a day.
And you're like, okay,
I need to figure out this transparency thing.
And then suddenly you find yourself looking for the people
that can help you solve that problem.
And it becomes part of the way you build
whatever it is that you're building.
And so it kind of allows for a more intuitive
and organic way for you to build these principles
into the way that you work.
That's one thing that I would say is pretty helpful
and pretty kind of low lift,
especially when you don't have a ton of resources at hand.
The other thing I would say is that,
I mentioned this earlier,
when we talk about things like responsible AI,
there's a lot to be done at the technical level,
which I think is little by little
becoming a little bit more measurable.
So it's still in development,
but we now have tools that can help measure fairness.
What does fairness really mean?
Are we measuring the right thing?
I know that's a big question
to which there's no magic answer,
but at least now there are tools.
A lot of them are open source, they're out there.
So now we can leverage some of that at the technical level.
There are AI system model cards
that a lot of different organizations have developed.
You can borrow those and many of them, again,
they're open source, they're there for your use.
Like you can start to implement those.
And then some of the big questions,
those are the things at the technical level.
The things that the big question side,
your business model, the product itself
and how people are going to interact with it
and what the follow-on effects of that might be.
Those are big questions.
You're not gonna sit down and fix those
because they're deep, big questions.
But you do wanna probably sit down
with your organization's leadership team,
with your investors if you have them,
and actually just talk about it.
Like what is it that we're building here
and what is it going to do in the world?
And a really great example of an area
where we did not do this as a society
until probably a little too late
was ad-driven business models
where a lot of personalized ads
turned out to cause a few bits of discomfort
around the world.
And so these deep questions about business model
that nobody really thought about early on
are now big problems that we're trying to solve
across society.
But had we had that opportunity
to have an earlier conversation around the business model
and what the implications were,
could have probably maybe tweaked the different approaches
to how we built those products.
So I think, again, like you're not gonna have a you,
you're not gonna have a team of 1000 ethicists
and designers and engineers to build your,
responsible AI solution suite.
Yeah, yet.
But you can sit down with the principals
and at least as a starting point.
And what you will, I think find is that
there are a few things that are gonna jump out at you
and they're gonna be questions that you wanna answer
over and over again and say,
okay, I guess I better figure out
how I'm going to build this into the way that I work
and the way that I build this product.
And then over time it becomes more sophisticated
and then you have a team of 1000 and then
have a different kind of problem.
That's fantastic advice.
And one I think is very relatable
even for someone building stuff.
We came up against this with a client not too long ago
that the tantalizing drive of potentially a big contract
butts up against something that maybe we haven't sat
and taken enough time to evaluate.
I think we made the right decision with that.
But that was the first time I think
just even at a small scale,
everyone's gonna confront this.
I know we wanna get, we wanna dive into
some of your responsible AI discussion guide,
but you did bring up something that I really wanna
maybe touch on a little bit.
And you talked about open source.
There's some open source information that's used
for some of these ethical value shaping propositions
and things like that that companies do.
What is your opinion on open source versus closed sourced
AI models at this juncture?
And then maybe down the road,
what is the kind of positives and negatives in your eyes
and what do you think is the pathway to not only use
the thing that's most transparent and ethical,
but also keeps us safe and doesn't let this stuff run wild?
That is a really good question.
I have not made up my mind.
Just to, I'll lay that out upfront.
I've not made up my mind on either or,
or if there's one approach that's better than the other.
I think the closed source or non open source stuff,
I think it's great because it allows there to be
a bit more control and safety measures, at least in theory.
There's just more control over what gets released.
And I think that since we're still learning a lot about
and especially generative AI,
and I say we collectively, we humanity,
we're still learning a lot about it,
not just on the technical side,
but how we're gonna use it, what the impact might be on job,
all the things.
It's nice to know that there's maybe a smaller group
of corporations that are very much in the public eye.
A lot of people are watching them,
both for the great things that they'll do,
but also for the things they might do wrong.
And so it's just nice, I think, to know that there's
that kind of a straight line of accountability in some sense.
On the other hand, I think the open source approach
is so cool, right?
Because it allows more people access to experiment
and learn and build.
And I think that, I don't know,
I don't study philosophy or history,
but I do think that part of the magic of tech
for a lot of people is this notion
that it can give so many people opportunity.
It's a way to democratize opportunity in some sense.
And so I love this idea of open source as a general matter,
but especially with something like AI
that I think is increasingly showing itself
to be an economic opportunity for all sorts of people
all over the world.
I don't know, just having that as an open source resource,
I think is really cool, of course.
But that also means that there's less control
and we're not really sure what it means.
It might be fine, like maybe the lack of control
and the experimentation, maybe it'll be fine.
Maybe as we develop the technology,
the norms around how we use it and how we develop it
and other safety measures will grow along with it.
We don't know yet, but it's a gamble
because maybe those things won't happen
and maybe it'll be a disaster and horrible people
will get their hands on this open source stuff
and we're in the world.
I don't know, I see both sides of it.
I haven't made up my mind.
The protector side of me is no, keep it closed.
And then the kind of just like, I don't know,
I want everyone to have an opportunity side of me
loves the open source model.
Maybe there is something open source, as you said,
diversity comes into place because more people
simply can bring in and chip in.
But maybe there is something that even open source community
could pick and learn from the big companies
which have this thousand people staff.
And this brings me that I had this
very fortunate conversation.
One of our friends is sitting two seats below Microsoft CEO.
So then I was talking with her and I asked,
oh, okay, how about your regulations?
And she just laughed.
She was like, Microsoft paved the way.
We wouldn't be able to do what we do
if we didn't install these in our ginormous organization.
So she's saying that Microsoft is like consulting
commissioners on these stuff.
So that kind of brings me thinking that
maybe smaller startups or like open source
could actually pick up some resources,
how to move towards trustworthy AI, responsible AI.
And with that, I know that you created something.
So maybe we could, with this podcast, with this discussion,
also leave listeners with some things
that may need to think about, as you mentioned,
just once a month, maybe sit down.
I need to say that I'm always seeking for content
which simplifies complex things,
which also makes it accessible to more people.
And this is one of the amazing skills to have.
And your analogies are the best.
If you could walk us through and share some kind of breakdowns,
that would be amazing.
If I could too, I can read one of these.
Mapo has put out a discussion guide that we, like I said,
we'll share in the show notes.
And it has some really, really clever analogies.
I think one that is obviously one we talk about a whole lot
is generative AI.
So I'm going to read the analogy in that side.
And then maybe we could talk about some
of your thoughts around it.
Child has a large box of Legos.
The child learns to build all sorts of things using Legos
by looking through a large catalog available
on the internet of pre-built Lego structures,
some with accompanying instructions,
i.e. supervised learning, and some
without, i.e. unsupervised learning.
That is a wonderful analogy for generative AI.
I'm glad you guys think that they're helpful analogies.
And I will say, I feel like it's important for me
to disclaim a little bit.
I didn't come up with all of these by myself.
Again, a culmination of years of trying to myself learn
and understand AI.
And I've read so many analogies.
Colleagues have shared analogies.
Some of these are, especially the earlier ones,
are the classic ones that you might
find in explainers or textbooks, like the teacher teaching
the child if it's a cat or a dog.
That's a classic example by now.
Isn't it funny that AI has been around long enough
that we have classic examples?
Classic, I think that's what happened.
A world before is considered classic examples, right?
So I just want to throw that out there
because I don't want anyone to think I sat down and dreamed
these up myself.
But yeah, I really, again, for me,
as someone who has a technical background but does not build,
I don't build AI products myself.
I'll usually sit down with an engineer
and they'll walk me through all the math.
And then I'll ask a million, I'm sure, very annoying questions.
And then I'll go away and say, I still don't get this
and I still don't get that.
And then I'll go find a simple analogy
that either I read or someone shared with me
or that I cobbled together.
And then I put the two together till I can make sense of it.
And I have just found that to be a very useful tool
to then start to think about, OK, what
are some of the things that might go wrong
or that we want to do better when we build whatever it
is that we're building?
So that's really the origin of the whole analogy thing.
And then I was, I know we were talking about this a little bit
earlier before we started recording,
but I was running this roundtable
where I would have to talk about trustworthy AI issues
to a team that was not very familiar with AI.
And we had very short amount of time.
And I was thinking, how in the world
am I going to introduce the technology
and introduce the issues in a way that makes any sort of sense?
And so this is how we did it.
We did it through analogies, and it was quite successful.
Then I decided to write them all down, and there you go.
Just for listeners who are not checking out us on YouTube,
which you should, the document contains of two kind of tables.
And on one side, we have AI and machine learning
types and analogies.
That's what we are talking about.
But on the right side, we have examples
of trustworthy AI issues.
And what Wes read was on the left side.
So it was under generative AI.
There is also, you broke down deep learning models,
transfer learning models, reinforcement, which is amazing.
But on the generative AI side, on the right side,
you have hallucinations and intellectual property.
I'll read the intellectual property analogy first,
as it pertains, of course, to LEGO blocks.
You ask the child to build you a unique LEGO structure.
The child builds something that seems interesting
and different, and the child claims it's totally unique.
You find out later that the child's unique LEGO structure
was copied directly from an existing proprietary structure
the child had seen when learning about LEGO structures.
There you go.
IP is a great topic to jump into now.
I would love to hear your take on some of the things
that are starting to creep their way through the courts
that I think are gonna be the subject of huge discussions
in the future.
Yeah, let's maybe talk about that for a little bit.
Yeah, sure.
IP, as you said, is definitely an interesting topic de jure
when it comes to generative AI.
I think there are a lot of different elements to it.
One that I think a lot of people are talking about
right now is scraping.
So obviously a lot of these generative AI models,
they feed on data.
They need tons of information,
and what better place to get information
than the worldwide web?
There's so much out there.
And a lot of the stuff that is fed
into the larger generative AI models
comes from sources that maybe weren't meant to feed in
to a commercial product
that was then going to be sold for profit.
So think about, for example, a book that was published,
and there's like a Google Books version
of five of the chapters,
and it's just getting fed into this generative AI model
as training data.
Or let's say all of your blog posts
that you may have some time ago
is just getting fed in there,
or any number of things that might be on the web
that aren't really meant to go into someone else's product
that they are going to sell for a profit.
People are getting a little bit-
Comments on Reddit.
Those literary gems.
It's a huge amount.
Reddit is a huge big block of content on there.
Sorry, didn't mean to interrupt you.
No, no, that's actually a great example
because think about it from Reddit's perspective.
They don't, they probably, and their users probably,
don't want a bunch of their information
being fed into these models as training data,
either for intellectual property purposes,
or also in some cases for some privacy purposes,
or any other kind of concerns that you might have
with your written down comments going into this model
that's going to be used in ways that you can't really control.
And so I think that's one of the levels
of intellectual property that's coming up
is the stuff that's getting fed into these models aren't,
some of the owners or the creators of that data
aren't really getting to control
whether or not their stuff gets fed in.
Though, if I remember this correctly,
I think Wes, or no, go to you were saying
that things in this space change so much.
I feel if I don't read the news every single day,
I've missed everything.
I think I remember reading that open AI
is going to create a control that,
so websites can opt out of having their data
fed into their models.
I feel like I remember reading that somewhere.
I think it was that, or they open sourced,
they published their crawler, their web crawler bot.
So if websites wanted to make a little defense against it,
they could.
I just want to go back to something though,
because you spoke about people didn't expect to,
or didn't want their content potentially fed
into this commercial product.
But I always happen to think that's just an interesting take
because is it just because these, in this use case,
it's being taken kind of one step further
to train and create a commercial product?
Because when we put a text message into our cell phone
and send it, we don't own it anymore.
Our carrier owns it.
The police can send a warrant
and they can get all our text messages.
And that to me, it's okay, you're putting it out there.
There's an illusion of privacy or control, so to speak,
but it's not yours anymore.
And I don't see us lining up being like,
I'm gonna stop sending text messages now then,
since I don't own them as soon as they're released.
Do you think that there's either some technological pathway
where it's just everything is completely end to end secured,
randomized, the company has no idea what's even being sent,
is the solution.
Or the other side of it could be like,
is there some wild solution, this architecture
that we could create for ourselves
that everyone kind of gets a little like Spotify
streaming credit worth of money
if someone trains on their data.
Oh, six of your Reddit comments got pulled for this model.
You get 0.0002 cents or something like that for that.
And you can actually see this times everybody.
I think to me, that's something that would work.
You'd have a ton of infrastructure you need to build
for that, but where do you fall
or where do you think companies should maybe fall
between those two endpoints?
Like many things, I'm gonna give a dissatisfying answer
of I haven't decided yet.
But the question that you're raising,
I think is the same question that we're continuing
to struggle with when it comes to data driven ads,
personalized ads.
It's the same question, right?
Like you put your information out there,
there's not a human being who's sitting there
reading your personal data.
So in terms of like privacy,
it's not like there's some like magic human being
who's like reading everything about you
and annotating everything about you.
But there is something invasive about knowing
that this information that you're putting out there
about yourself in some way, shape or form,
whether it's clicks, whether it's something
you've actually written, whether it's something
you've shared somehow online, a photo, whatever,
just any sort of information you've put out there
by yourself, there's something weird about it,
I think to people, there's something weird about it.
Then being used by a corporation to do something
for the corporation that makes the corporation money
without you really, I don't know, having control over it
or maybe not having had the transparency around it
or maybe remuneration like you were saying,
like you're not getting paid for it.
Although some corporations would argue
you are getting paid for it in the sense
that you're getting the free use of this product or tool
or whatever the case may be.
But I think that debate, it's such a big debate
and I think it goes back to something
we were talking about earlier where a lot
of the tech industry is digging itself out of a big, big hole
that was created when we started using these kind
of data-driven technologies without transparency,
privacy and all of the principles that we talk about
leading the way and have now put a lot of the tech industry
in a position of trying to earn back the trust of people
whose data are feeding a lot of these products.
And so I don't know what the right answer is
in terms of the right model for how we all get comfortable
with how data might be used and what happens with it
and all of that.
But I do think that to your point
about some technological solutions,
I think there are a lot of solutions,
especially with the rise of privacy enhancing technologies
which are increasingly sophisticated.
There's some really cool way over my head
but some really cool work that's being done
that creates like a level of privacy at the model level.
So you can create model at the, or create privacy
at the data level through both.
But also, yeah, it's fascinating,
like really, really cool stuff.
So I think there's stuff we can do there,
but I don't think that solves that fundamental discomfort
that some people have with this notion
of their data being used for this thing that's making money.
So it's not just this privacy question
which happens to come up a lot.
It's just the general, like, I don't know,
some people don't feel comfortable with it.
I don't know, I'm probably going to be canceled for this,
but I have this alternative thought about this.
The way I see it that people vote with their time or money.
And I was at the conference and people were like,
oh, this is an ethical, I would never use AI,
like they scrape data and stuff.
And I was just saying, if you would really care
about ethics, none of you would carry iPhone
and nobody would shop at H&M.
And I worked with GDPR and then it was such a big thing,
you know, in the EU, like everyone gets power
over your privacy.
And then basically the feedback was like,
the cookies that I have to click and accept is annoying.
And I wonder sometimes the amount of people
who truly, truly care about this
and at what step they are willing to give it away.
If you say that, okay, you will never ever
will be able to use AI unless you give all your privacy
to us, I wonder what will be,
maybe it's a split in society, but yeah,
but also maybe our private thoughts, private comments
are not that unique when it's taken at scale.
Did it for me.
You know how you think that your iPhone
might be like listening to you
or your phone might be listening to you
because you had a thought about something
and maybe you told it to your spouse
and then you get an ad for that?
That's not really it listening in most instances.
That is you are a variable
in these incredibly complex algorithms
that have you so pegged that it knows,
oh, your wife liked a engagement ring ad three weeks ago.
So now every sixth ad you're gonna get
because you're in the same family group,
boom, you're gonna start getting that kind of target.
It's not because you even had to have a conversation
about it, like these kinds of connections are there.
And I think you're absolutely right.
I think I don't foresee us as not of recursing,
of coming back.
We've already given away the farm.
It's okay, we're here.
Maybe we can draw some lines a little bit better.
And just to this Lego example on intellectual property,
I loved it because it's exactly experience
I had in architecture school where a teacher asked us
to make a hundred drawings and ideas.
And you know, as a creative person,
I'm so creative, I'm so unique.
And then we all exhibited and 90% we are all having
exactly the same ideas, the same images.
And maybe there is out of hundred, I would say,
okay, maybe two, three where it really stood out.
And I feel it's also comes down to copyright
when we can talk about when you are creating something,
how much you're creating that you closed your eyes
and never seen world around you
and how much it is an influence from everything around you
all the way back to what Wes is saying
that you are just variable in your preferences,
your likability is already determined by these algorithms.
We know you better than your spouse, literally.
No, I actually, I agree with basically everything
both of you said.
And I think that when you start becoming really dogmatic
about concepts like transparency, privacy,
accountability, all of those things,
I think what happens is you start to lose the bigger picture
of why we even have technology in this world.
And one of the most interesting sets of conversations
that I tend to have are when I speak to people, for example,
that are situated in the EU,
there's a very specific type of economic situation there.
They're very human rights focused as a society.
Things like privacy are ingrained in the culture.
Everyone knows about privacy, everyone's heard of it.
It's not new information.
And then I have a conversation about technology
with people from different parts of the world,
maybe Sub-Saharan Africa as an example,
or some of the Southeast Asia countries
that have very different economic situations
are really striving to build their economy.
And when you start to talk about what technology means
for their society and how you balance that against concepts
like security and privacy and safety and things like that,
it's not that things like privacy transparency
and all of those things, it's not that they go away,
they're still really quite important.
And it's important to have, I think, an ethical foundation
in whatever it is that you're building and doing.
But the balance starts to sound a little bit different
as to what's important for people.
And what I have trouble with in my field
is that dogmatic view of these types of,
we'll call them responsible principles or ethical principles.
And I think instead, what's important for us
is to go back to, okay, what is it that we're trying to do?
What are we trying to accomplish
with whatever it is that we're building?
What's the impact we want to have on the world?
What positive things will that build?
Will it build value and how?
And let's just take a few minutes to also think
of how we might destroy value along the way,
maybe by accident, maybe in ways that we didn't anticipate.
And let's try to not do that
because we don't wanna destroy value,
we're sure to create value.
And so I think that all these questions about privacy
and are you a variable or does someone really know you better
than, sorry, does an algorithm really know you better
than someone in your family or in your group of friends?
I think it has a lot less to do
with the technical dogmatic questions
and more to do with people's comfort
and understanding of technology
and what role it plays in their lives.
And so I think that we have to think about
that kind of storytelling side of things.
That's why I always go back to business model.
What is your business model?
What is it that you're trying to create
and really sell with whatever it is that you're building?
If that business model is going to make people
feel uncomfortable with how you're using technology
and how they might fit into the equation,
it might actually be perfectly fine.
And yet still people will critique it
and try to tear it down and try to break it apart,
even if it's objectively okay and fine.
I don't know, that's my view on it.
Stay away from the dogma and take some time
from time to time to look at that big picture
and ask the question of what is it
that we're actually trying to do here and are we doing it?
I love this.
So either you spend time now,
invest your time and effort and energy,
or you're going to pay a lot for PR and rebranding
eventually down the line when everyone starts to come at you
because eventually something will happen
destroying the image of the company.
We could talk to Mapo for hours more,
but want to give her back her Sunday afternoon here
while we record.
So aside from the stages of any AI conference
where they can surely see Mapo,
she spoke at AI4, South by Southwest,
a couple of other big ones right there,
where else can people find what you got going on?
Please feel free to reach out on LinkedIn.
I definitely keep track of that account.
And I also have a website called Call Me Mapo.
Beautiful.
And I don't know if it's appropriate to say,
but I would love to have you on my YouTube channel.
There you go.
This is open in the air invitation.
You?
Look at that.
Yeah, happy to join.
That'd be awesome.
Okay, so that's another plug.
You don't want a bunch of tech bros like me
to decide in the future.
Come on, we need to get the good feminine voices
out there to steer us right.
I don't make any decisions in my family.
I just listen to the real boss.
So with that, for Go2Go, I am Wes Shields.
And for Mapo, our wonderful guest,
saying happy prompting, everybody.
Happy prompting, everybody.
And now Mapo, you do too.
Happy prompting.
Amazing.
As always, you can check out the show notes and links
at howtotalk2.ai.
That's all for this week's episode.
Happy prompting, everyone.