- Hey, welcome to Adobe's Think Tank.
We're gonna get through this interview
pretty quickly because I want to get back
to watching Wild Wild
Country in the hotel room
but with me is Jake Porway,
founder and executive director of DataKind
and it's kind of the easiest
question in the world
to ask first but I kind of wanted to talk
to you about data for
good and just using data
for the best of intentions.
- [Jake] What does that mean?
- What does that mean, right?
That was like kind of a question
but not a full question.
That was me going like hey, let's just,
let's just throw some stuff.
- Yeah, sure, no worries.
Are you testing right now or are we going?
- Oh we're totally going.
- Oh great, just making sure.
I was like, should I give a test answer?
Oh great.
- It's so comfortable
that we can just rip off.
- I know, make it so comfy.
So, you know, we talk at DataKind a lot
about doing data for good and using AI
in the service of
humanity and the thing is
we say the best of intentions
because so many people
out there are realizing they
can give their skills back.
There's opportunities to help
nonprofits and governments
use data to predict where
famines are gonna occur
or get clean water to people more safely
but it's actually tricky to do, you know,
sometimes we try our
best and get out there
and say we're gonna
volunteer some time out there
but doing it right can
actually take a lot of work
and a lot more practice
than you might expect
so we're always trying to
help people do it better
whether it's with DataKind
or in their own companies.
- Yeah and talk to me like
maybe through a sample thing
or just kind of that
process of how do you make
that more efficient, how
do you make that effective?
Because, again, you probably don't know
the unintended consequences of trying
to get into a space but
what does that look like?
- Yeah so the way that we
work at DataKind, as you say,
we're a volunteer organization,
we get data scientists
to go team up with
nonprofits to help solve
their data science machine
learning challenges.
And so actually a lot
of people think great,
just give me a project, let's just jump in.
but, like you said, we don't always know
the right thing to build, we don't know
how it's gonna be used and, importantly,
with this whole conversation about ethics
in the AI space, we want to make sure
that we're thinking really critically
about how we're building algorithms
that don't cause harm
so what we'll often do
is we have a pretty long kind
of get to know you process,
a scoping process where
we'll bring in NGO's
that say we think we
could use machine learning
or AI and then data scientists sit down
and sort of listen to their challenges,
knock on some data, see
what we can actually build
and I'll actually say that's probably
one of the most enriching
parts for both sides
because a lot of
nonprofits come in and say,
you know, my AI need is
a I need a new database
'cause that's like sort
of what they know, right,
just like a corporate client might say
I think I need Hadoop but instead,
a good data scientist may step on back
and say well what are the
challenges you're trying to solve?
We talked to one group that was trying
to prevent fires that were occurring.
Red Cross was saying we want to stop fires
from happening where
they can be preventable
so data scientists step back
and say well how do you do that?
They say well we just kind of go out there
and put out brochures and
after kicking it around,
they said ya know, the
data you've got, yeah,
the data you've got and
data we see out in the world
could be put together to build
a kind of predictive model
to show you where you should go
and that was really kind of
the watershed moment for them.
Scales fell from their
eyes and they're like
oh we didn't even think
about predicting the data,
well now we can do this and
that and the other thing
so it's really that process
of talking with people,
getting to their real
needs and understanding
what the data can do to help
you build a kind of AI solution
that's really gonna be useful for humans.
- That's good and that's kind
of like the reactive stuff
but you talk about the proactive stuff
where finding problems can actually
be harder than finding solutions.
Talk a little bit about that.
- Yeah, that's right.
- That stuff that's kind of unseen.
- Yeah, so that's a great
way of putting it, unseen.
I think a lot of times we see folks
who come in and say I want to go build
a satellite imagery tool that's
gonna help find poachers.
You may hear that from either side, NGO's
but a lot of the time, that's not as easy
as just getting a bunch
of satellite imagery
and running it, right, you
know, and just getting into that
because you don't know who's gonna use it
or how they're gonna use it or if the data
can even support it.
So while a lot of people
think donating their time
involves just going to a nonprofit
and saying let me work on this project,
I think it's cool or working with you,
we actually find that
finding the right problem
to solve takes this kind
of six component process
we talk about where you have to find
the right problem, you
gotta find the right data,
you gotta find the data
scientists, you gotta find
the person who's gonna act on it, fund it
and even subject matter
experts who can say
hey, that poacher thing
that you're building
actually is a good thing or oh, actually
if you tweaked it this
way, it wouldn't just work
for the World Wildlife Fund
but a lot of organizations
and so that's actually, I
think, the trickiest part
is not just saying who's the nonprofit
I'm gonna work with and what do I solve?
It's saying how do I really
get all of those components,
the data, the problem, the
stakeholders, all around
the table so you can really
design collaboratively
to find the right ones so when we say
finding solutions, or–
finding problems
is gonna be harder than
finding the solutions,
that's what we talk about,
setting the right thing up.
- And it's a good way
of positioning it, too.
By the way, most people,
when they list six parts
to a solution, only get through like four,
maybe four and a half.
I don't have the data ahead of me
but I'm pretty sure
that's about the right.
- [Jake] Yeah, yeah.
- You talk a little bit about how,
what is the single biggest kind of barrier
for nonprofit organizations?
Because it could be scale, it could be
a lot of things but what do you see?
- I think, right now, what we see
is there's so much energy.
People want to be able
to use machine learning
and AI and they have
really great interventions.
I mean some of these
nonprofits are transforming
the way that we think about everything
from sanitation to
disposal to curing poverty.
The challenge they have
right now, I think,
is one, resources, just having the money
to actually engage with
data science and AI
is incredibly expensive.
- Right and convincing someone that
that's the best allocation
of the money, right?
- That's exactly right.
You know, for people who
haven't worked in nonprofits,
you don't get money up
front, right, people want
to give you money to say I
want you to go save the whales
and I want to know where
every dollar is gonna go.
- Right 'cause there's an
accountability that you have
as a nonprofit organization
that you don't have
as a corporation because
those stakeholders
want to know is 92% of
this, is 94% of this,
is 45% of this going to
actionable stuff, right?
- That's exactly right
so whereas a startup,
they may say okay here's some seed money
to figure out some stuff
with, it's very hard
to get that in a nonprofit
space so to go to a founder,
or excuse me, a funder, and say hey,
we need a half a million dollars to build
an AI team, you get blank faces,
like what is that gonna do,
why would I give my money?
So that, I think, is one
of the biggest challenges
and it's really a problem with the space
and the way funding works
in the nonprofit space
so that's why we offer
our services pro bono
so that we can get over that barrier
and you can just start working
with expert Googlers right away.
- Well that's the double edged causality
of being a nonprofit is
that people will want
to work with you because it's cause-based
and it applies well to CSR but they also
want to know exactly what you're doing.
- Yes, that's a big challenge.
- To close it out, because
this is really concise advice
but what do you see the
potential for nonprofits
in the next five years,
kind of leveraging it?
What do you think is actually achievable?
- I start to get a little
optimistic over this.
- I like optimistic.
- Oh okay great.
- We've talked and we've been rooted
but give me your most optimistic future.
- Well in my mind, it's
that we don't just look for
good values and data, we use
AI for our own human values.
Almost every field could be
touched by AI in some way
whether it's, again, transporting
water more efficiently
or cracking down on vaccines,
cracking down on diseases
or getting vaccination
rates up, it's everywhere
and really what I'd like to see though
is that it not be on corporations alone
to say we have to give back to do this
and not be on nonprofits alone
to say we have to solve this problem
but that we actually start creating spaces
where the two come together.
Nonprofits who say we know
the space innovation we need
but we don't have the
resources can sit across
from corporations who say
we've got great human capital,
we've got data that no
one else has ever seen,
we can provide some services.
- Find those perfect mergers.
- Yeah, so to me, that's
my big kumbaya moment
is that we're all coming
together to think about
no just how can we make
little drips and drops
of improvements on these problems
but how can we really
push together forward
to use data for social impact?
- But we're at a conference
so it's always good
to end on a kumbaya moment.
- Oh good, good, I tend to always so yeah.
- Well that's perfect.
Thanks so much, Jake.
- Oh yeah, my pleasure,
thank you very much.
- And follow us along at #AdobeTT.
Tweet us.
Thank you.
