- Okay, last, but certainly not least, we have Brett Gaylor,
who's commissioning editor of the Mozilla Foundation.
So, take it away, thanks.
- Hi.
So, I'm a filmmaker and I make films about the internet.
This poster is from when I made,
called Rip, a Remix Manifesto,
which really looked at how the internet was transforming
copyright law and how we thought of intellectual property.
I also made one called Do Not Track,
which was an interactive documentary series
that collected data about you
while you were watching the film.
That won a Peabody Award for really helping people
understand what information they were sharing online.
My day job is I work for the Mozilla Foundation,
we're the non-profit behind Firefox,
and I support other media makers like myself
who are examining the health of the internet.
But I want to talk to you about a film that I made last year
with my son with my son, Rowan.
So, can I just get a quick show of hands,
who here uses an iPhone?
What about Android?
Okay, so more of you have, may be familiar with Siri
than are familiar with the Google Assistant, which I use.
If you use that, like my son who loves it,
you may have asked, you ask it all kinds of questions.
My son asks it, like, where is Texas?
What are Lego Ninjas?
We used to play with it all the time together,
and one day I happened to be browsing my Google settings
and I noticed this thing on the right,
which is basically a log of every single request
that he or I had made to the Google Assistant,
which I can play back as an audio file.
So I decided to make a film with that material,
and I want to show it to you.
Navigate to 4955 Highgate Road.
I started using voice commands
about a year ago on my phone.
I'd used the voice feature to try and find any of the stuff
I'd normally Google.
What is the weather in San Francisco?
- [Google Assistant] It's 14 degrees
and partly cloudy in San Francisco.
- [Brett] What is a warlock?
- [Google Assistant] Sure, pictures of a warlock.
- [Brett] What is the world's longest burp?
(burping)
My family started using it too.
- [Child] Google, now what is my belly button?
(child laughing)
- [Woman] Is wood ash good for all rooting plants?
- [Brett] But no one in the family used it more
than my five year old son, Rowan.
- [Rowan] What is a iPad?
- [Brett] It's Googling it, Rowan.
What are other things that you don't know about?
These are all different ways
that you can use the Google thing.
How do you say computer in French?
- [Rowan] What is stupid in French?
- [Google Assistant] Stupide.
(Rowan laughing)
- [Brett] Then I realized that a year of these searches
had actually been saved on Google servers.
I can play back everything he'd searched for on the device.
- [Rowan] What is poo?
What is naked?
What is inappropriate?
- [Google Assistant] Alright.
- [Rowan] What's Charmander?
- [Google Assistant] Triangle, a plain figure
with three straight sides and three angles.
- [Rowan] Charmander.
- [Google Assistant] There you go, pictures of a shaman.
- [Rowan] What is Charmander!?
- [Brett] As the year progressed,
I noticed the two of them beginning to bond.
- [Rowan] What are blue jellyfish?
Where is Mexico?
What is dancing?
What is gymnastics?
What is flying?
What is a bee?
What is a lollipop?
What's trick or treating?
What are ninjas?
- [Brett] Google has been with Rowan from the start.
And I get the feeling this is a lifelong relationship.
Even though I know them both intimately,
I can't imagine what it'll be like when they grow up.
But Rowan is already asking the right questions.
- [Rowan] What is everything?
- [Google Assistant] Everything, all things,
all the things in a group or class.
(Google Assistant answers overlap)
(Rowan laughing)
♪ Google now, Google now, Google now now now ♪
(laughing)
(applauding)
- [Brett] Thank you.
So, most people ask me when they watch that, so like,
is he still allowed to use the phone?
But, behind their eyes,
and I'm sort of seeing some of it here, the question is,
is this guy a terrible parent?
(audience laughing)
So let's actually look at,
what does Google do with this data?
And the answer is we don't really know,
but there's some things that we do know,
what's going on in these data centers,
and the first is that it's used to improve search,
simple search.
It turns out that what we speak gets out
what we're looking for much better than what we type.
So, those voices are used to train the algorithm of search,
and it's the same with translation.
Those voice searches have radically improved
the Google Translate engine,
the natural language processors that improve it.
But then there's DeepMind, which is
the leading artificial intelligence platform
that Google acquired.
You might be familiar with DeepMind as the technology
that powers AlphaGo, I'm getting a lot of nods.
DeepMind is powerful because it uses machine learning,
so it can examine large data sets and then learn on its own
the best way to solve a problem.
And Google, obviously, has vast datasets.
Datasets that we give it, often without really understanding
that those datasets are being used
to train an artificial intelligence.
In one of DeepMind's recent projects,
here is the dataset from that,
is to learn how to recognize human activity.
So, we're familiar with facial recognition,
which is what's used to recognize a person,
but kinetics, what this study looked at,
was how to recognize human motion.
So, y'know, this would be throwing a football
and this would be shooting a gun and this would be dancing.
They used it to study 250,000 videos
that were uploaded to YouTube, so it learned to recognize
things like eating cake or playing golf.
But this is where it gets problematic.
I downloaded this data set,
which, to their credit, is freely available,
and looked at the entry for person carrying baby.
And as I looked at the entries I saw a problem.
The videos of carrying baby are only people that are women.
I put in many years of carrying babies,
but what bothers me about this isn't because
I want credit for that, it's because those videos
are a dataset that's training a machine to think
that the only people who carry babies are women.
And just like I didn't know that Google was storing
all those recordings of Rowan,
and these moms probably didn't know that they were being
used as a data point to teach a machine,
we just don't know, in the future,
what this algorithm is going to be used for,
what DeepMind will do next.
Just like none of us knew that our likes on Facebook
would teach an algorithm how to persuade people
to vote for Brexit.
And these algorithms are increasingly influencing
the public sphere.
So, sort of piling on to Megan's examples here,
but this is a study by ProPublica in the U.S.
that looked at algorithms being used in the justice system,
to do things like predict whether somebody is going
to recommit a crime, to re-offend,
which can shape things like parole and bail.
So, in this case, they looked at two people
who had both committed petty crime.
And the system rated as a high risk
a black defendant on the right,
while assessed a white defendant as a low risk.
Meanwhile, the white defendant had much stronger
prior offenses, as you can see, armed robbery,
while the black defendant had minor offenses,
such as juvenile misdemeanors.
And, in fact, the system was wrong.
It turned out that the white defendant went on
to recommit a very serious crime,
while the black defendant did not.
So the data that trained this algorithm
is from a justice system that disproportionally
incarcerates black people.
The system isn't predicting the future,
it's reinforcing some of the terrible patterns of our past.
Even though we live in the future,
it's really hard to predict the future.
So that's what we have to think about,
so the answer to that question, no, sorry kids, no Alexa,
no Facebook accounts,
and we're really stingy with the photos.
So maybe, in the future, they'll be able to apply
for a Facebook account, so thanks Silicon Valley,
another reason to be terrified of them becoming teenagers.
(audience laughs)
(audience applauds)
- I hated to cut you off, that was a fantastic film
and I think you're a wonderful parent.
Y'know, what do I know, but that seems very, I was inspired.
I was like, I want to live in that house.
So, you know, this idea of how our data
is being collected and how it's being used in ways
that we don't necessarily know,
it brings up this issue of informed consent.
And right now, it currently takes this form of things
that I don't read and a lot of people don't read.
And so, do you have any thoughts on ways that we can,
talking about translation, that was a brilliant translation
of how you make data relevant to people's lives
and certain ideas into their heads,
but do you have any ideas of user friendly tools
that could be created for better consent?
- Yeah, I think a lot of times we look at what we can do
as individuals and consumers, but lately at Mozilla
we've really tried to flip that script and it's...
Y'know, when we thought about car accidents
and how to present those, we didn't ask people
to install their own seat belts,
so we sort of think of it in the same way,
that it's time for the manufacturers
to build these systems into the products that they make.
And we really think about that in terms of,
as you said, consent.
And so we funded a really interesting project with
the Allied Media Project in Detroit
that looked a report called Building Consentful Tech,
which you can Google and find, and it really followed
Planned Parenthood's FRIES model
for evaluating sexual encounters.
So, FRIES stands for is it freely given?
Is it reversible?
So just because you give consent at one point,
you may not give consent for other uses of your data.
Is it informed?
Do you understand the full use of how your data
is being used?
Is it enthusiastic?
So, you know, if you have to give,
make an account to get wifi, that is not enthusiastic.
And is it specific?
So is it specific to that use?
So, we really think that, y'know, the makers of technology,
so whether you're engineers or funding projects,
we really need to think about whether the technologies
that we build,
and the platforms that we enter are consentful ones.
- Mm-hmm. Great.
And, fries are delicious, so.
- And fries are delicious.
- That's also...
- It's really easy to remember.
(both speakers laughing)
- Okay, so now I want to make sure there's time
I know we ran out last time for questions for the audience.
Okay, over here, yes.
- [Melissa] Hi, I'm Melissa with the Skoll Foundation and I,
this is a topic that I've come across a lot,
just in my general reading, and I'm just wondering
what are kind of like the resources I,
as like a lay person I would say,
has in order to like tell the companies
that are using this data that I'm really frustrated
that their data is biased for, like,
with that example of only women carrying babies?
- Yeah, I think that there is an element
that you're seeing right now of, y'know,
voting with your dollars or with your feed,
and I think that is something that we're starting to see
and that's something we're really excited about at Mozilla,
that there's beginning to be a ecosystem around
much more healthy platforms and partners.
So, I think that there is an aspect that we need
to start thinking about the internet as an ecosystem
that we are a part of and that we can support.
So, choosing technology that is respectful of you
I think is a really powerful way to do that,
and there are beginning to be more and more
examples of that.
So, I think, y'know, joining some of those groups
that might be a little bit uncomfortable,
might be a little bit, they're not quite as cool yet
as Facebook, and maybe all your friends aren't on them,
but it's gotta start somewhere, so I think starting to...
Y'know, it's almost like food, you know, you have to kinda,
sometimes you've gotta go to the farmer's market,
sometimes you gotta, like, pay a little bit more,
and I think those are some steps
that we kinda need to take as individuals.
- Great.
Thank you for that.
So, we have the three minutes left in the overall session
and I really, this was disparate panel with
a lot of different perspectives and ideas,
so I wanted to just bring everybody back up to stage
to give a lightning answer to a question.
So, if you could all come,
and plus you can all see them again,
'cause Vermesh feels like a long time ago at this point,
(audience laughs) and he was wonderful
and so we need to see him again.
And Mumbwana, get off your phone, y'know, get up here.
(audience laughs)
So bossy, I'm so bossy. - Well done.
- I'm not bossy, I'm the boss.
(audience laughs) So I mean, somebody said that.
- [Man] Do you have those socks?
Because those socks exist.
- Those socks exist?
- [Man] Yeah.
- Oh, okay, well I need those socks apparently.
So, you know, we've been talking a lot about
the dark side of data, but also, of course, the light side,
and we know that these types of data technologies
are here to stay.
They're in business, they're in politics,
they're in society, and so, in my perspective,
we have a window of opportunity right now to address
some of these things that each of you have brought up
in different ways.
And so, from your vantage point,
each of you come at this form a very different perspective,
what is the single most important step
that you can take to advance the cause of digital data
for social good?
Go, 10 seconds.
(audience laughs)
- On that side.
- For me, and this is what I was trying to get at
toward the end of my, like, few minutes speaking,
is not just the inclusion, but the actual revisiting
of the underlying models by which we collect and structure
and calculate upon data
in ways that are more open, that are open,
to diverse stake holders, right?
So, you know, in relation to your talk,
am I pronouncing your name right, Kum-rwana?
- Wana. - Wana, yeah, great.
It's not merely the democratization of coding in Africa,
it's also the underlying models by which
those systems are built.
That's what I was trying to get at when I was using
the word ontology, right?
So it's not just importing existing models from the West
and training them with African data,
it's also the underlying models of learning themselves.
So, this is central, for me, around this question
of democratizing governance around data
and learning and technology itself.
So that's the big thing I'm really--
- Underlying models. - Passionate about, yeah.
- Doug.
- It's not what algorithms can do for you,
it's what you the person needs,
and so when you're designing whatever solution,
have the people in the room who are going to be impacted
by what you're working on so that you can get feedback
realtime on what you're building
and is it going to help the human being.
- Absolutely. - Great. Megan.
- Infrastructure.
And this is going to sound self-serving,
Jake Poray made this point this morning
so maybe if I give him credit, it's less self serving.
There's so much work to be done right now.
I mean, all of us could give
so many examples of data that needs to be collected,
data that needs to be analyzed differently, biased data,
and there's a lot of excitement of data scientists out there
who wanna work on those problems,
and there's not a lot of resources
to actually enable that work.
There's a lot of night and weekend volunteer gigs
and that's awesome, that's a great way to start,
but these problems are complex
and they need a full-time commitment,
so we need an infrastructure to do the work.
- [Woman] Mm-hm. Agreed.
- I guess be stingy.
Be just as careful with your digital body
as you would be with your real body.
- FRIES.
(laughing) - FRIES.
Because, you know, it's attached to you
and it lives forever and you only have one.
(laughing)
- Now I'm terrified.
Okay, Mumbwana.
- So I go back to,
you had a really nice slide on this, the life cycle of data,
the data life cycle and I think in Africa,
and many emerging markets probably,
you have just the beginning of the collection phase.
So, I just remembered that we have
a performer company called Smile Identity.
It's actually, it reminds me of the slide you had about
the sort of AI being able to detect faces,
and so this company is building identity,
you know how I mentioned that identity is
a barrier to scale for some of these data companies?
Outside of Kenya, where they have a national ID?
Well, there's a company that's addressing that in Tanzania
and other countries where they're using
the training AI tools to identify African faces.
Because that's never been done before.
And this will be used as identity.
So I think on the collection side, you know,
the opportunity to actually get ahead of it
and start to kind of, but also include government,
I'm really now becoming very concerned
that some of these amazing companies,
both for social good as well as just pure capitalistic,
in Africa are gonna hit walls because governments quickly
understand that data's the new oil and they're signing
per-regulation very very fast,
like Tanzania and others, I know Nigeria's working on it.
So I think informing, educating government and also allowing
individuals to really get or handle their own data,
because that's not really something African's even expect,
there aren't privacy laws in many countries.
- Right.
Well, these are very modest calls to action.
I think everybody in this room and watching online
can take them on.
So, thank you all to the audience here,
(audience clapping) thank you to our panelists.
Have a great conference.