*35C3 Intro music*
Herald Angel: We at the Congress, we not only talk about technology, we also talk
about social and ethical responsibility. About how we can change the world for
good. The Good Technology Collective supports the development guidelines...
sorry, it supports the development process of new technology with ethical engineering
guidelines, that offer a practical way to take ethic and social impact into account.
Yannick Leretaille - and I hope this was okay - will tell you more about it.
Please welcome on stage with a very warm applause Yann Leretaille.
*applause*
Yannick Leretaille: Hi, thanks for the introduction. So before we start, can you
kind of show me your hand if you, like, work in tech building products as
designers, engineers, coders, product management? OK, so it's like 95 percent,
90 percent. Great. Yeah. So, today we kind of try to answer the question: What is
good technology and how can we build better technology. Before that, shortly
something of me. So I am Yann. I'm French- German. Kind of a hacker, among the CCC
for a long time, entrepreneur, like, co- founder of a startup in Berlin. And I'm
also founding member of the Good Technology Collective. The Good
Technology Collective was founded about a year ago or almost over a year now actually
by a very diverse expert council and we kinda have like 3 areas of work.
The first one is trying to educate the public about current issues with
technology, then, to educate engineers or to build better technology, and then
long-term hopefully one day we'll be able to work like in legislation as well.
Here, it's a bit of what we achieved so far. We've like 27 council members now. We
have several media partnerships and published around 20 articles, that's kind
of the public education part. Then we organized or participated in roughly 15
events already. And we are now publishing one standard, well, kind of today
actually, and *applause*
and if you're interested in what we do, then, yeah, sign up for the newsletter and
we keep you up to date and you can join events. So as I said the Expert Council is
really, really diverse. We have everything from people in academia, to people in
government, to technology makers, to philosophers, all sorts, journalists.
And the reason that is the case is that a year ago we kind of noticed that in our own
circles, like, as technology makers or academics, we were all talking about a lot
of, kind of, voice on development and technology, but no one was really kind of
getting together and looking at it from all angles. And there have been a lot of
very weird and troublesome developments in the last two years. I think we really
finally feel, you know like, the impact of filter bubbles. Something we have talked
for like five years, but now it's like, really like, you know, deciding over
elections and people become politically radicalized and society is, kind of,
polarized more because they only see a certain opinion. And we have situations
that we only knew, like, from science fiction, just kind of, you know, pre-crime,
like, governments, kind of, over-arching and trying to use machine learning to make
decisions on whether or not you should go to jail. We have more and more machine
learning and big data and automization going into basically every single aspect
of our lives and not all of it been has been positive. You know, like, literally
everything from e-commerce to banking to navigating to moving to the vault now goes
through these interfaces. That present us the data and a slice of the world at a time.
And then at the same time we have really positive developments. Right? We have
things like this, you know, like space travel, finally something's happening.
And we have huge advances in medicine. Maybe soon we'll have, like, self-driving cars
and great renewable technology. And it kind of begs the question: How can it be that
good and bad use of technology are kind of showing up at such an increasing rate in
this, like, on such extremes, right? And maybe the reason is just that everything
got so complicated, right? Data is basically doubling every couple of years,
so no human can possibly process anymore. So we had to build more and more complex
algorithms to process it, connecting more and more parts together. And no one really
seems to understand it anymore, it seems. And that leads to unintended consequences.
I've an example here: So, Google Photos – this is actually only two years ago –
launched a classifier to automatically go through all of your pictures and tell you
what it is. You could say "Show me the picture of the bird in summer at this
location" and it would find it for you. Kind of really cool technology, and they
released it to, like, a planetary user base until someone figured out that people
of color were always marked as gorillas. Of course it was a huge PR disaster, why
somehow no one found out about this before it came out... But now the interesting thing
is: In two years they didn't even manage to fix it! Their solution was to just
block all kind of apes, so they're just not found anymore. And that's how they
solved it, right? But if even Google can't solve this... what does it mean?
And then, at the same time, you know, sometimes we seem to have, kind of,
intended consequences?
I have an example... another example here: Uber Greyball. I don't know if anyone
heard about it. So Uber was very eager to change regulation and push the services
globally as much as possible, and kind of starting a fight with, you know, all the
taxi laws and regulation, and taxi drivers in the various countries around the
world. And what they realized, of course, is that they didn't really want people to
be able to, like, investigate what they were doing or, like, finding individual
drivers. So they built this absolutely massive operation which was like following
data in social media profiles, linking, like, your credit card and location data
to find out if you were working for the government. And if you did, you would just
never find a car. It would just not show up, right? And that was clearly
intentional, all right. So at the same time they were pushing, like, on, like,
the lobbyism, political side to change regulation, while heavily manipulating the
people that were pushing to change the regulation, right? Which is really not a
very nice thing to do, I would say.
And...
The thing that I find, kind of... worrisome about this:
No matter if it's intended or unintended, is that it actually gets worse, right?
The more and more systems we interconnect, the worse these consequences
can get. And I've an example here: So this is a screenshot I took of Google Maps
yesterday and you notice there are, like, certain locations... So they're kind of
highlighted on this map and I don't know if you knew it but this map and the
locations that Google highlight look different for every single person.
Actually, I went again and looked today and it looked different again. So, Google
is already heavily filtering and kind of highlighting certain places, like, maybe
this restaurant over there, if you can see it. And I would say, like, from just
opening the map, that's not obvious to you that it's doing that. Or that it's trying
to decide for you which place is interesting for you. However, that's
probably not such a big issue. But the same company, Google with Waymo, is also
developing this – and they just started deploying them: self-driving cars. They're...
...still a good couple of years away from actually making it reality, but they are
really – in terms of, like, all the others trying it at the moment – the farthest, I
would say, and in some cities they started deploying self-driving cars. So now, just
think like 5, 10 years into the future and you have signed up in your Google...
...self-driving car. Probably you don't have your own car, right? And you go in
the car and you are like: "Hey, Yann, where do you want to go?" Do you want to go to
work? Because, I mean obviously that's why I probably go most of the time. Do you
want to go to your favorite Asian restaurant, like the one we just saw on the
map? Which is actually not my favorite, but the first one I went to. So Google
just assumed it was. Do you want to go to another Asian restaurant? Because,
obviously, that's all I like. And then McDonald's. Because, everyone goes there.
And maybe the fifth entry is an advertisement. And you would say: Well,
Yann, you know, that's still kind of fine, but it's OK because I can still click on:
'No, I don't want these 5 options, give me, like, the full map.' But now, we went back
here. So, even though you are seeing the map, you're not actually not seeing all
the choices, right? Google is actually filtering for you where it thinks you want
to go. So now we have, you know, the car like this symbol of mobility and freedom.
It enables so much change in our society that it's actually reducing the part of
the world that you see. And because, I mean these days they call it AI, I think
it's just machine learning, because these machine learning algorithms all do pattern
matching and basically just can recognize similarities. When you open the map and
you zoom in and you select a random place, it would only suggest places to you where
other people have been before. So now the restaurant that opened around the corner
you'll probably not even discover it anymore. And no one will. And it will
probably close. And the only ones that will stay are the ones that are already
established now. And all of that without being really obvious to anyone who would
use the technology. Because it has become like kind of a black box. So, I do want
self-driving cars, I really do. I don't want a future like this. Right. And if we
want to prevent that future, I think we have to first ask a very simple question,
which is: Who is responsible for designing these products? So, do you know the
answer? audience: *inaudible*
Yann: Say it louder. audience: We are.
Yann: Yeah, we are. Right. That's a really frustrating thing about it that actually
gets us, right, as engineers and developers. You know we are always driven
by perfection. We want to create, like, the perfect code sources. One problem,
really, really nice. You know. Chasing the next challenge over and over trying to be
first. But we have to realize that at the same time we are kind of working on
frontier technologies, right, on things, technology, that are really kind of on the
edge of values and norms we have in society. And if we are not careful and
just, like, focus on our small problem and don't look at the big picture, then we
have no say in on which side of the coin the technology will fall. And probably it
will take a couple of years, or by that time we alreaday moved on, I guess. So.
It's just that technology has become so powerful and interconnected and impactful,
because we are not building stuff that it's not affecting like 10 or 100 people
or a city but literally millions of people, that we really have to take a step
back and not only look at the individual problem as the challenge but also the big
picture. And I think if you want to do that we have to start by asking the right
questions. And the first question of course is: What is good technology? So,
that's also the name of the talk. Unfortunately, I don't have a perfect
answer for that. And probably we will never find a perfect answer for that. So,
what I would like to propose is to establish some guidelines and engineering
processes that help us to build better technology. To kind of ensure the same
where we have quality insurance and project management systems and processes
to, like, kind of, this you were tasked with. And companies that what we build is
actually, has a net positive outcome for society. And we call it the good
technology standard. We've kind of been working that over, the last year, and we
really wanted to make it really practical. And what we kind of realized is that if you
want to make it practical you have to make it very easy to use and also mostly,
actually what was surprising, just ask the right questions. So, what is important
though, is that if you adapt the standard, it has to be in all project phases. It has
to involve everyone. So, from, like, the CTO to, like, the product managers to
actually legal. Today, legal has this interesting role, where you develop
something and then you're like: Okay, now, legal, make sure that we can actually ship it.
And that's what usually happens. And, yeah, down to the individual engineer. And
if it's not applied globally and people start making exceptions then of course it
won't be worth very much. Generally, we kind of identified four main areas that we
think are important, kind of defining, kind of an abstract way, if a product is
good. And the first one is empowerment. A good product should empower its users. And
that's kind of a tricky thing. So, as humans we have very limited decision
power. Right? And we are faced with, as I said before, like, this huge amount of
data and choices. So it seems very natural to build machines and interfaces that try
to make a lot of decisions for us. Like the Google Maps one we saw before. But we
have to be careful because if we do that too much then the machine ends up making
all decisions for us. So often, when you develop something you should really ask
yourself, like, in the end if I take everything together am I actually
empowering users, or am I taking responsibility away from them? Do I
respect the individual choice? Why does he say: I don't want this, or they give you
their preference, do we actually respect it or do we still try to, you know, just
figure out what is better for them. Do my users actually feel like they benefit from
using the product? So, I couldn't, actually not a lot of people ask themselves,
because usually you think like in terms of: Are you benefiting your company? And I
think what's really pressing in that aspect: does it help the users, the humans
behind it, to grow in any way. If it helps them to be more effective or faster or do
more things or be more relaxed or more healthy, right, then it's probably positive.
But if you can't identify any of these, then you really have to think about it.
And then, in terms of AI, in machine learning, are we actually kind of
impacting their own reasoning so that they can't make proper decisions anymore. The
second one is Purposeful Product Design. That one is one that, it's been kind of a
pet peeve for me for a really long time. So these days we have a lot of products
that are kind of like this. I don't have something specifically against Philips
Hue, but there seems to be, like, this trend that is kind of, making smart
things, right? You take a product, put a Wi-Fi chip on it, just slap it on there.
Label it "smart", and then you make tons of profit, right? And a lot of these new
products we've been seeing around us, like, everyone is saying, like, oh yeah,
we will have this great interconnected feature, but most of them are actually not
changing the actual product, right, like, the Wi-Fi connected washing machine today
is still a boring washing machine that breaks down after two years. But it has
Wi-Fi, so you can see what it's doing when you're in the park. And we think we should
really think more in terms of intelligent design. How can we design it in the first
place so it's intelligent, not smart. That the different components interact in a
way, that it serves a purpose well, and the kind of intelligent by design
philosophy is, when you start using your product you kind of try to identify the
core purpose of it. And based on that, you just use all the technologies available to
rebuild it from scratch. So, instead of building a Wi-Fi connect washing machine
would actually try to build a better washing machine. And if it ends up having
Wi-Fi, then that's good, but it doesn't has to. And along each step actually try
to ask yourself: Am I actually improving washing machines here? Or am I just
creating another data point? And yeah, a good example for that is, kind of, a
watch. Of course it's very old and old technology, it was invented a long time
ago. But back when it was invented it was for something you could have on your arm
or in your pocket in the beginning and it was kind of a natural extension of
yourself, right, that kind of enhances your senses because it's never there, you
don't really feel it. But when you need it it's always there and then you can just
look at it and you know the time. And that profoundly changed how, like, we humans
actually worked in society because we couldn't meet in the same place at the
same time. So, when you build a new product try to ask yourself what is the
purpose of the product, who is it for. Often I talk to people and they talk to me
for one hour, what like, literally the details of how they solved the problem but
they can't tell me who their customer is. Then does this product actually make
sense? Do I have features, and these distract my users, that I maybe just don't
need. And can I find more intelligent solutions by kind of thinking outside of
the box and focusing on the purpose of it. And then of course what is the long term
product vision like, where do we want this to go? This kind of technology I'm
developing in the next years. The next one is kind of, Societal Impact, that goes
into what I talked about in the beginning with all the negative consequences we have
seen. A lot of people these days don't realize that even if you're, like, in a
small start up and you're working on, I don't know, a technology, or robots, or
whatever. You don't know if your algorithm, or your mechanism, or whatever
you build, will be used by 100 million people in five years. Because this has
happened a lot, right? So, only when starting to build it you have to think: If
this product would be used by 10 million, 100, maybe even a billion people, like
Facebook, would it have negative consequences? Right, because then you get
completely different effects in society, completely different engagement cycles and
so on. Then, are we taking advantage of human weaknesses? So this is arguably
something that's just their technology. A lot of products these days kind of try to
hack your brain, what, we understand really well how, like, engagement works
and addiction. So a lot of things, like social networks, actually have been
focusing, you know, and also built by engineers, you know, trying to get a
little number from 0.1% to 0.2%, can mean that you just do extensive A/B testing,
create an interface that no one can stop looking at. You just continue scrolling,
right? You just continue, and two hours have passed and you haven't actually
talked to anyone. And this attention grabbing is kind of an issue and we can
see that Apple actually now implemented screen time and they actually tell you how
much time you spend on your phone. So there's definitely ways to build
technology that even helps you to get away from these. And then for everything that
involves AI and machine learning, you really have to take a really deep look at
your data sets and your algorithms because it's very, very easy to build in biases
and discrimination. And again, if you it applied to all of society many people who
are less fortunate, or more fortunate, or they're just different, you know they just
do different things, kind of fall out of the grid and now suddenly they can't,
like, [unintelligible] anymore. Or use Uber, or Air B'n'B, or just live a normal
life, or do financial transactions. And then, kind of what I said in the
beginning, not only look at your product but also, if you combine it with other
technologies that are upcoming, are there certain combinations that are dangerous?
And for that I kind of recommend to do, like, some techno or litmus test to just
try to come up with the craziest scenario that your technology could entail. And if
it's not too bad then, probably good. The next thing is, kind of, sustainability. I
think in today's world it really should be part of a good product, right. The first
question is of course kind of obvious. Are we limiting product lifetime? Do we maybe
have planned obsolescence, or if we build something that is so dependent on so
many services and we're not only going to support it for one year anyways, that
basically it will have to be thrown in the trash afterwards. Maybe it would be
possible to add a standalone node or a very basic fallback feature so that at
least the products continues to work. Especially if you talk about things like
home appliances. Then, what is the environmental impact? A good example here
would be, you know, crypto currencies who are now using as much energy as certain
countries. And when you consider that just think like is there maybe an alternative solution
that doesn't have such a big impact. And of course we are still capitalism, it has
to be economically viable, but often there aren't, often it's again just really small
tweaks. Then of course: Which other services are you working with? But for
example I would say, like, as european companies, we're in Europe here, maybe try
to work mostly with suppliers from Europe, right, because you know they follow GDPR
and strict rules, and in a sense the US. Or check your supply chain if you build
hardware. And then for hardware specifically that's because also I have,
like, we also do hardware in my company, I always found that interesting. We're kind
of in a world where everyone tries to save, like, the last little bit of money
out of every device that is built and often makes the difference between plastic
and metal screws like half a cent, right. And at that point it doesn't really change
your margins much. And maybe as an engineer, you know, just say no and say:
You know, we don't have to do that. The savings are too small to redesign
everything and it will impact upon our quality so much that it just breaks
earlier. These are kind of the main four points. I hope that makes sense. Then we
have two more, kind of, additional checklists. The first one is data
collection. So really, just if, especially like in terms of like IOT, you know,
everyone focuses on kind of collecting as much data as possible without actually
having an application. And I think we really have to start seeing that as a
liability. And instead try to really define the application first, define which
data we need for it, and then really just collect that. And we can start collecting
more data later on. And that can really prevent a lot of these negative cycles we
have seen. By just having machine learning organisms run on of it kind of
unsupervised and seeing what comes out. Then also kind of really interesting I
found that, many times, like, a lot of people are so fascinated by the amount of
data, right, just try to have as many data points as possible. But very often you can
realize exactly the same application with a fraction of data points. Because what you
really need is, like, trends. And that usually also makes the product more
efficient. Then how privacy intrusive is the data we collect? Right. There's a big
difference between, let's say, the temperature in this building and
everyone's individual movements here. And if it is privacy intrusive then we should
really, really think hard if we want to collect it. Because we don't know how it
might be used at a later point. And then, are we actually collecting data without
people realizing that they do it, right, especially if we look at Facebook and
Google. They're collecting a lot of data without really implicit consent. But of
course at some point you like all agreed to the privacy policy. But it's often not
clear to you when and which data is collected. And that's kind of dangerous
and kind of in the same way if you kind of build dark patterns into your app. They
kind of fool you into sharing even more data. I had, like, an example that someone
told me yesterday. I don't if you know Venmo which is this American system where
you pay each other with your smartphone. Basically to split the bill in a
restaurant. By default, all transactions are public. So, like 200 million public
transactions which everyone can see, including the description of it. So for
some of the more maybe not so legal payments that was also very obvious,
right? And it's totally un-obvious when you use the app that that is happening. So
that's definitely a dark pattern that they're employing here. And then the next
point is User Product Education and Transparency. Is a user able to understand
how the product works? And, of course, we can't really ever have a perfect
explanation of all the intricacies of the technology. But these days for most people
almost all of the apps, the interfaces, the building technology and tech. This is
a complete black box and no one is really doing an effort to explain it to them why
most companies advertise it like this magical thing. But that just leads to kind
of this immunization where you just look at it and you don't even try to understand
it. I'm pretty sure that no one ever, like, these days is still opening up a PC
and trying looking at the components, right, because everything is in tablet and
it's integrated and it's sold to us like this magical media consumption machine.
Then, are users informed when decisions are made for them? So we had that in
Empowerment, that we should try to reduce the amount of decisions we make for the
user. But sometimes, that's a good thing to do. But then, is it transparently
communicated? I would be totally fine with Google Maps filtering out for me the
points of interest if it would actually tell me that it's doing that. And if you
can't understand why it made that decision and why it showed me this place. And maybe
also have a way to switch it off if I want. But today we seem to kind of assume
that we know better for the people why it's, so we found the perfect algorithm
that has a perfect answer. So we don't even have to explain how it works, right?
We just do it and people will be happy. But then we end up with is very negative
consequences. And then, that's more like a marketing thing, how is it actually
advertised? I find it, for example, quite worrisome that things like Siri and
Alexa and Google home are, like, sold as these magical AI machines that make your
life better, and are you personal assistant. When in reality they are
actually still pretty dumb, pattern matching. And that also creates a big
disconnect. Because now we have children growing up who actually think that Alexa
is a person. And that's kind of dangerous. And I think we should try to prevent that
because for these children, basically, it kind of creates this veil and it's
humanized. And that's especially dangerous if then the machine starts to make
decisions for them. And suggestions because they will take them as if a human
did it for them. So, what is that? So, these are kind of the main areas. Of course
it's a bit more complicated. So we just published the standard today in the first
draft version. And it's basically three parts of science introduction, kind of the
questions and checklists that you just saw. And then actually how to implement it in
your company, which processes to have, at which point you basically should have
kind of a feature gate. And I would kind of ask everyone to go there, look at it,
contribute, shared it with people. We hope that we'll have a final version ready kind
of in Q1 and that by then people can start to implement it. Oh, yeah. So, even though
we have this standard, right, I want to make it clear having such a standard and
implementing it in your organization or for yourself or your product is great. It
actually doesn't remove your responsibility, right? This can only be
successful if we actually all accept that we are responsible. Right? If today I
build a bridge as a structural engineer and the bridge breaks down because I
miscalculated, I am responsible. And I think, equally, we have to accept that if
we build technology like this we also have to, kind of, assume that responsibility.
And before we kind of move to Q&A, I'd like to kind of take the citations. This
is Chamath Palihapitiya, former Facebook executive, from the really early times.
And also, around a year ago when we actually saw the GTC he said this in a
conference: "I feel tremendous guilt. I think in the back in the deep restlessness
of our mind we knew something bad could happen. But I think the way we defined it
is not like this. It is now literally at a point where I think we have created
tools that are ripping apart the social fabric of how society works." And
personally, and I hope the same for you, I do not want to be that person that five
years down the line realizes that they built that technology. So if there is one
take-away that you can take home from this talk, then to just start asking yourself:
What is good technology, what does it mean for you? What does it mean for the
products you build and what does it mean for your organization? Thanks.
*applause*
Herald: Thank you. Yann Leretaille. Do we have questions in the room? There are
microphones, microphones number 1, 2, 3, 4, 5. If you have a question please speak
loud into the microphone, as the people in the stream want to hear you as well.
I think microphone number 1 was the fastest. So please.
Question: Thank you for your talk. I just want to make a short comment first and
then ask a question. I think this last thing you mentioned about offering users
the options to have more control of the interface there is also a problem that
users don't want it. Because when you look at the statistics of how people use online
web tools, only maybe 5 percent of them actually use that option. So companies
remove them because for them it seems like it's something not so efficient for user
experience. This was just one thing to mention and maybe you can respond to that.
But what I wanted to ask you was, that all these principles that you presented, they
seem to be very sound and interesting and good. We can all accept them as
developers. But how would you propose to actually sell them to companies. Because
if you adopt a principle like this as an individual based on your ideology or the
way that you think, okay, it's great it will work, but how would you convince a
company which is driven by profits to adopt these practices? Have you thought of
this and what's your idea about this? Thank you.
Yann: Yeah. Maybe to the first part. First, that giving people choice is
something that people do not want and that's why companies removed it. I think
if you look at the development process it's basically like a huge cycle of
optimization and user testing geared towards a very specific goal, right, which
is usually set by leadership which is, like, bringing engagement up or increase
user amount by 200 percent. So I would say the goals were, or are today, mostly
misaligned. And that's why we end up with interfaces that are in a very certain way,
right? If we set the goals differently, and I mean that's why we have
like UI and UX research. I'm very sure we can find ways to build interfaces that are
just different. And still engaging, but also give that choice. To the second
question. I mean it's kind of interesting. So I wouldn't expect a company like Google
to implement something like this, because it's a bit against that. This is more by
that point probably but I've met a lot of, like, also high level executives already,
who were actually very aware of kind of the issues of technology that they built.
And there is definitely interest there. Also, more like industrial side, and so
on, especially, it seems like self-driving cars to actually adopt that. And in the
end I think, you know, if everyone actually demands it, then there's a pretty
high probability that it might actually happen. Especially, as workers in the tech
field, we are quite flexible in the selection of our employer. So I think if
you give it some time, that's definitely something that's very possible. The second
aspect is that, actually, if we looked at something like Facebook, I think they
overdid it. Say, optimize that so far and push the engagement machine and kind of
triggering like your brain cells to never stop being on the site and keeps
scrolling, that people got too much of it. And now they're leaving the platform in
droves. And of course Facebook would not go down, they own all these other social
networks. But for the product itself. as you can see, that, long term it's not even
necessarily a positive business outcome. And everything we are advertising here
still also to have very profitable businesses, right, just tweaking the right screws.
Herald: Thank you. We have a question from the interweb.
Signal Angel: Yes. Fly asks a question
that goes into a similar direction. In recent months we had numerous reports
about social media executives forbidding their children to use the products they
create at work. I think these people know that their products are made addictive
deliberately. Do you think your work is somewhat superfluous because big companies
are doing the opposite on purpose. Yann: Right. I think that's why you have
to draw the line between intentional and unintentional. If we go to intentional
things like what Uber did and so on. At some point it should probably become a
legal issue. Unfortunately we are not there yet and usually regulation is kind
of lagging way behind. So I think for now we should focus on, you know, the more
unintentional consequences, of which there are plentiful and kind of appeal to the
good in humans. Herald: Okay. Microphone number 2 please.
Q: Thank you for sharing your ideas about educating the engineer. What about
educating the customer, the consumer who purchases the product.
Yann: Yeah. So that's a really valid point. Right. As I said I think
[unintelligible] like part of your product development. And the way you build a
product should also be how you educate your users on how it works. Generally, we
have a really big kind of technology illiteracy problem. Things have been
moving so fast in the last year that most people haven't really caught up and they
just don't understand things anymore. And I think again that's like a shared
responsibility, right? You can't just do that in the tech field. You have to talk
to your relatives, to people. That's why we're doing, like, this series of articles
and media partnerships to kind of explain and make these things transparent. One
thing we just started working on is a children's book. Because for children,
like, the entire world just exists with this shiny glass surfaces and they don't
understand at all what is happening. But it's also primetime to explain to them,
like, really simple machine learning algorithms. How they work, how like,
filterbubbles work, how decisions are made. And if you understand that from an
early age on, then maybe you'll be able to deal with what is happening. In a way
better, an educated way. But I do think that is a very long process and so only if
we start and the more work we invest in that, the earlier people will be better
educated. Herald: Thank you. Microphone number 1
please. Q: Thanks for sharing your insights. I
feel like, while you presented these rules along with their meaning, the specific
selection might seem a bit arbitrary. And for my personal acceptance and willingness
to implement them it would be interesting to know the reasoning, besides common
sense, that justifies this specific selection of rules. So, it would be
interesting to know if you looked at examples from history, or if you just sat
down and discussed things, or if you just grabbed some rules out of the air. And so
my question is: What influenced you for the development of these specific rules?
Yann: It's a very complicated question. So how did we come up this specific selection
of rules and also, like, the main building blocks of what we think should good
technology be. Well, let's say first what we didn't want to do, right. We didn't
want to create like a value framework and say, like, this is good, this is bad,
don't do this kind of research or technology. Because this would also be
outdated, it doesn't apply to everyone. We probably couldn't even agree in the expert
council on that because it's very diverse. Generally, we try to get everyone on the
table. And we talked about issues we had, like, for example me as an entrepreneur. And when
I was, like, in developing products with our own engineers. Issues we've seen in terms
of public perception. Issues we've seen, like, on a more governmental level. Then
we also have, like, cryptologists in there. So we looked at that as well and
then we made a really, really long list and kind of started clustering it. And a
couple of things did get cut off. But generally, based on the clustering, these
were kind of the main themes that we saw. And again, it's really more of a tool for
yourself as a company that developers, designers and engineers to really
understand the impact and evaluate it. Right. This is what these questions are
aimed at. And we think that for that they do a very good job.
From microphone 1: Thank you. Herald: Thank you. And I think. Microphone
number 2 has a question again. Q: Hi. I was just wondering how you've
gone about engaging with other standards bodies, that perhaps have a wider
representation. It looks largely like from your team of the council currently that
there's not necessarily a lot of engagement outside of Europe. So how do
you go about getting representation from Asia. For example.
Yann: No, at the moment you're correct the GTC is mostly a European initaitive. We
are in talks with other organizations who work on similar issues and regularly
exchange ideas. But, yeah, we thought we should probably start somewhere. In Europe
is actually a really good place to start. Like a societal discourse about technology
and the impact it has and also to to have change. But I think if for example
compared to things like Asia or the US where is a very different perception of
privacy and technology and progress and like the rights of the individual Europe
is actually a really good place to do that. And we can also see things like GDPR
regulation, that actually, ... It's kind of complicated. It's also kind of a big
step forward in terms of protecting the individual from exactly these kind of
consequences. Of course though, long term we would like to expand this globally.
Herald: Thank you. Microphone number 1 again.
Q: Hello. Just a short question. I couldn't find a donate button on your
website. Do you accept donations? Is money a problem? Like, do you need it?
Yann: Yes, we do need money. However it's a bit complicated because we want to stay
as independent as possible. So we are not accepting project related money. So you can't
like say we want to do certain research product with you, it has to be
unconditional. And the second thing we do is for the events we organize. We usually
have sponsors that provide, like, venue and food and logistics and things like
that. But that's an, ... for the event. And again, I can't, like, change the
program of it. So if you want to do that you can come into contact with us. We
don't have a mechanism yet for individuals to donate. We might add that.
Herald: Thank you. Did you think about Patreon or something like that?
Yann: We thought about quite a few options. Yeah, but it's actually not so
easy to not fall into the trap that, like, as organizations in space have been,
like, Google at some point sweeps in and it's like: Hey, do you want all this cash.
And then very quickly you have a big conflict of interest. Even if you don't
want that to happen it starts happening. Herald: Yeah right. Number 1 please.
Q: I was wondering how do you unite the second and third points in your checklist.
Because the second one is intelligence by design. The third one is to take into
account future technologies. But companies do not want to push back their
technologies endlessly to take into account future technologies. And on the
other hand they don't want to compromise their own design too much.
Yann: Yeah. Okay. Okay. Got it. So you were saying if we should always stop
these, like, future scenarios and the worst case and everything and incorporate
every possible thing that might happen in the future we might end up doing nothing
because everything looks horrible. For that I would say, like, we are not like
technology haters. We are all from areas working in tech. So of course the idea is
that you can just take a look at what is there today and try to make an assessment
based on that. And the idea is if you look it up and meet the standards that over
time actually you try to,... When you add new major features to look back at your
assessment from before and see if it changed. So the idea is you kind of create
a snapshot of how it is now. And this kind of document that you end up as part of
your documentation kind of evolved over time as your product changes and the
technology around it changes as well. Herald: Thank you. Microphone number 2.
Q: So thanks for the talk and especially the effort. Just to echo back the
question that was asked a bit before on starting with Europe. I do think it's a
good option. What I'm a little bit worried is it might be the only option. It might
become irrelevant rather quickly because it's easy to do, it's less hard to
implement. Maybe in Europe now. Okay. The question is. It might work in Europe now
but if Europe doesn't have the same economical power it cannot bog in as much
politically with let's say China or the US in Silicon Valley. So will it still be
possible and relevant if the economical balance shifts?
Yann: Yes, I mean we have to start somewhere, right? Just saying "Oh,
economical balance will shift anyway, Google will invent singularity, and that's
why we shouldn't do anything" is, I think, one of the reasons why we actually got
here, why it kind of is this assumption that there is like this really big picture
that is kind of working against us, so we all do our small part to fulfill that
kind of evil vision by not doing anything. I think we have to start somewhere and I
think for having operated for one year, we have been actually quite successful so far
and we have a good progress. And I'm totally looking forward to make it a bit
more global and to start traveling more, I think that like one event outside Europe
last year in the US and that will definitely increase over time, and we're
also working on making kind of our ambassadors more mobile and kind of expand
to other locations. So it's definitely on the roadmap but it's not like yeah, just
staying here. But yeah, you have to start somewhere and that's what we did.
Herald: Nice, thank you. Number 1 please. Mic 1: Yeah. One thing I haven't found was
– all those general rules you formulated fit into the more general rules of
society, like the constitutional rules. Have you considered that and it's just not
clearly stated and will be stated, or did you develop them more from the bottom up?
Yann: Yes, you are completely right. So we are defining the process and the questions
to ask yourself, but we are actually not defining a value framework. The reason for
that is that societies are different, as I said they are widely different
expectations towards technology, privacy, how society should work, all the ones
about. The second one is that every company is also different, right, every
company has their own company culture and things they want to do and they don't want
to do. If I would say, for example, we would have put in there "You should not
build weapons or something like that", right, that would mean that all these
companies that work in that field couldn't try to adapt it. And while I don't want
them to build weapons maybe in their value framework that's OK and we don't want to
impose that, right. That's why I said in the beginning we actually, we're called
the Good Technology Collective, we are not defining what it is and I think that's
really important. We are not trying to impose our opinion here. We want others to
decide for themselves what is good and cannot support them and guide them in
building products that they believe are good.
Herald: Thank you. Number two . Mic 2: Hello, thanks for sharing. As
engineer we always want users to spend more time to use our product, right? But
I'm working at mobile game company. Yep. We are making, we are making a world that
users love our product. So we want users spend more time in our game. So we may
make a lot of money, yeah, but when users spend time to play our game they may lose
something. Yeah. You know. So how do we think about the balance in a game, mobile
game. Yeah. Yann: Hmm. It's a really difficult
question. So the question was like specifically for mobile gaming. Where's
kind of the balance between trying to engage people more and, yeah, basically
making them addicted and having them spend all their money, I guess. I personally
would say it's about intent, right? It's totally fine with a business model where
you make money with a game. I mean that's kind of good and people do want
entertainment. But if you actively use, like, research in how, like, you know,
like the brain actually works and how it get super engaged, and if you basically
build in, like, gamification and lotteries, which a lot of, I think, have
done, where basically your game becomes a slot machine, right, you always want to
see the next opening of a crate and see what you got. Kind of making it a
luck based game, actually. I think if you go too far into that direction, at some
point you cross the line. Where that line is you have to decide yourself, right,
some of it could be a good game and dynamic but there definitely some games
out there, I would say with quite a reason to say that they pushed to the limit quite
a bit too far. And if you actually look how they did it because they wrote about
it, they actually did use very modern research and very extensive testing to
really find out these, all these patterns that make you addicted. And then it's not
much better than an actual slot machine. And that probably we don't want.
Herald: So it's also an ethical question for each and every one of us, right?
Yann: Yes. Herald: I think there is a light and I
think this light means the interwebs has a question.
Signal angel: I, there's another question from ploy about practical usage, I guess.
Are you putting your guidelines at work in your company? You said you're an
entrepeneur. Yann: That's a great question. Yes, we
will. So we kind of just completed some and there was kind of a lot of work to get
there. Once they are finished and released we will definitely be one of the first
adopters. Herald: Nice. And with this I think we're
done for today. Yann: Perfect.
Herald: Yann, people, warm applause!
*applause*
*postroll music*
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