URS GASSER: All right.
Hello, everyone and welcome.
How are you?
Let's try again.
How are you this morning?
[APPLAUSE]
A very warm welcome to the Berkman Klein Center,
to Harvard Law School on this sunny
but windy and slightly chilly day.
We're really delighted to have you here.
My name is Urs Gasser.
I serve as the executive director
of the Berkman Klein Center.
I'm also on the Harvard Law School
faculty with my colleague, that I will introduce just in a bit.
And of course, I am very, very pleased
to moderate this special conversation about the ethics
of digital transformation.
And I'm of course, even more pleased and actually
honored to welcome the federal president of Germany,
Frank-Walter Steinmeier.
[APPLAUSE]
FRANK-WALTER STEINMEIER: Hello.
URS GASSER: [SPEAKING GERMAN]
FRANK-WALTER STEINMEIER: [INAUDIBLE]..
URS GASSER: We are joined by a wonderful group
of colleagues and experts.
And I will-- if you are not mad with me,
I will just briefly introduce you,
and we will get to know each other a bit better as we
go along and talk about your work, and of course
as we engage in the opening conversations.
So this should be interactive as much as we can.
Eva Weber-Gurska is a ethicist, a philosopher currently
at Ruhr University in Bochum.
She's doing amazing work, and I'm already looking forward
to learning from you today.
Quite often, these debates about ethics
happen without having philosophers around us.
I'm grateful that you're here.
Matthew Liao is at NYU, and is a professor of bioethics.
He also runs a center on the same topic,
and I'm particularly curious to hear also
some of the lessons learned from past cycles
of technological innovation, as we now
talk about digital things and AI, IT, and the like.
Jeanette Hofmann, welcome back.
Great to have you here.
Jeanette is a professor of internet policy at the Free
University in Berlin.
She's also the director of the Alexander von Humboldt
Institute for Internet and Society in Berlin.
I've introduced the president already,
and he doesn't need an introduction.
So next we have Dean Melissa Nobles,
who is the Dean of the School of Humanities, Arts,
and Social Sciences at MIT.
Really great to have you here, as we will hear more about MIT
is building a new school of computing.
MELISSA NOBLES: Correct.
URS GASSER: The Schwarzman College.
MELISSA NOBLES: That's right.
URS GASSER: Lots of interesting things
happening there at the intersection
of engineering and ethics.
So we're looking forward to your thoughts on this conversation.
Wolfgang Schulz, professor for media
and public law at the University of Hamburg.
He's also the director-- and now I have to read that,
because I still cannot remember it--
the director of the Leibnitz Institute for Media Research,
which is known to me as the Hans-Bredow-Institut,
but I learned that it's important to emphasize
the Leibnitz part.
Crystal Yang, really great faculty colleague here,
professor of law at Harvard Law School.
Does wonderful work, important work on criminal justice
and the use of algorithms and data in that area.
We'll talk more about that.
So as you can see, a fantastic lineup.
And of course, I'm so grateful to you, Mr. President,
that you joined this group as a participant,
and I get a sense already that you're ready to jump in
and will take over the moderation
function in due course, which is totally fine
and will make my job easier.
So one or two logistical notes.
First, we will end at roughly 11:30.
That's the plan.
Some seconds of the conversation may be in German.
You should have translation.
If it's OK with you, I will continue
to moderate in English, and I think
the reason is straightforward, because my Swiss accent is
so strong when I speak German, that it's
easier for the Germans when I talk English, so
just to make that [? clear. ?]
[APPLAUSE]
So with that, Herr Bundespresident,
here's the question for you to start us off.
We met the last time in 2012 in Berlin, and had a conversation
about what does it mean to make good policies
for the internet age?
And I googled this morning actually,
and tried to remember what happened in 2012, right?
It seems like in internet times it's more like 70 years
ago than seven years ago.
FRANK-WALTER STEINMEIER: Yeah.
URS GASSER: And when I googled things that happened there
in the technology space was the Google Glass project
was kicked off.
The iPad Mini was introduced.
Facebook went public.
And a bill was signed in California
that self-driving cars are now allowed and regulated.
So I'm wondering, that seems to be a very different stage
in our digital transformation process,
if you look back only a few years and now fast forward
to 2019.
Where have we arrived?
What are you thinking about?
What are your concerns?
What are your hopes and how does that connect
with the topic of today?
Mr. President.
FRANK-WALTER STEINMEIER: [SPEAKING GERMAN]
INTERPRETER: Well, thank you very much indeed
for these kind words of welcome.
Ladies and gentlemen, dear students,
I think it is fantastic.
You know, you've been given the alternative
to enjoy a sunshiny day, though somewhat windy, late autumn,
but you've nevertheless decided in favor
of the alternative-- coming here into an enclosed room
to listen to us.
Thank you very much for that.
And thank you also for reminding me of the year 2012,
which I remember well for quite different reasons,
and I'll get back to that in a second.
You know, my visit to Boston in [? 1220. ?]
But allow me to begin by saying that I haven't been here
for the very first time, and I'm always
happy to be back in this academic, scientific center,
a center not only with regard to the United States of America
but also in a much broader sense, because it
is a center that is exemplary in bringing together researchers
and academics from all parts of the world,
from all countries of the world, to make them work
on subjects of common concern.
And when I remember that visit in 2012, or earlier visits
too and later visits, you know, no matter
whether we talked about foreign policy issues or other issues,
either here in the hall or in other places
in Harvard, whether we talked about questions to do
with climate policy, about the state of affairs
of transatlantic relations, rest assured,
every time that I came here, I returned home
having benefited to a large extent
from the discussions I had at Harvard.
There was one exception, and that brings me back to 2012,
really.
Just once did I risk myself, my whole existence
here in Harvard, because I allowed myself
to be talked into throwing the first pitch in a baseball
match.
And I was extremely naive.
Never ever had I before I attended a baseball match,
held a baseball in my hands, nor been in a baseball stadium.
And my then colleague, secretary of state Condoleezza Rice,
we all remember her, was aghast when she
heard what I was about to do.
And the only comment she gave to me was, "don't do it."
But you know, that is typical of us Germans.
I had to accepted and I didn't want to go back on my promise.
So once we entered the stadium on the afternoon of that day,
I got an inkling of why my colleague Condoleezza
Rice was so aghast, because the stadium was filled
to the very last seat, 40,000 or 50,000 people in the audience.
And I had a certain feeling that they hadn't come just
because of me, but they'd come because they
wanted to watch the match, a match that
was the match really.
Here in the United States, the Red Sox
were playing the New York Yankees.
And I realized all of a sudden that this is not just
any match.
It's about religious issues, really.
Still, it worked out somehow.
I survived it.
And having survived that experience,
I was happy to come back every time.
I wasn't shy of returning to Boston, to Harvard.
But today, it's a different topic, really,
that brings me here, different from the topics we focused on
in the past years.
We're no longer on the threshold of the digitization,
of digitization, of the digital age.
But we have already entered that age.
I've come here because the topic we will be talking about
directly refers back to topics I'm
focusing on in my presidency, the future
of liberal democracy, that is.
How does the internet, how do Facebook, Twitter, algorithms,
anonymity in the internet, how do all these things change
the democratic culture of debate, which
is of such great importance to us in Germany,
just as much as you do in the United States of America?
Despite the daily waves of outrage
that you have to live with, how can we
make sure that we keep a general overview?
How can we distinguish what is important from what
is unimportant?
And does this culture of thinking
in simple opposites, yes or no, black or white,
harsh approaches, whether that takes away from us
our ability to see the nuances between black and white?
Are we capable of doing that?
Do we continue to be capable of entering into compromise, which
I believe to be vital for any democracy,
if we no longer have the time to differentiate or to see things
in nuances in carefully weighing the pros and cons,
because it's no longer popular?
We talked about this yesterday in Boston
with American and German academics in great depth.
Today, though, we are again talking
about digital transformation, how
that has changed our lives and daily experiences.
But as Mr. Gasser kindly indicated,
we will be focusing on a different priority.
We're not really focusing on the question,
whether we need digital technologies.
They're there, anyway.
No one is denying the fact that they open up
enormous opportunities for all of us, when
it comes to fighting poverty, for example,
when it comes to tackling the impact of climate change, when
it comes to combating diseases and their effects.
Undoubtedly, Germany is a country
that has no resources over its own,
outside the humanities and human resources.
We want to be a country that has technology to offer,
and we want to participate in the developments they entail.
And that is a kind of introductory remark on my part.
As regards to the topic we intend
to talk about today the ethic-- a code of ethics
for the digital transformation, I
would like to just briefly focus on
why this topic is so important to me.
I actually have come from two visits,
and I refer back to those visits.
I visited Stanford last year, focusing again
on the future of digitization.
When we traveled there, a few days before we left we read
in the papers that Elon Musk had bought up a company that was
engaging in the research in brain implants,
and that was doing very well in that regard,
that this might help tackle diseases like
Parkinson's and Alzheimer's.
I learned a lot about the imagination of researchers
during my encounters there, how one
can influence brain activities with the help
of implants and algorithms.
This has undeniable and obvious consequences.
But at the end of the discussion, it wasn't I,
but it was someone who is very well known in the United
States, George Shultz.
That is the former Secretary of State under Ronald Reagan, who
also is or was at the time a member of the board of Stanford
University.
He said at the end of the discussion, guys, really,
I am fascinated by the scenario you have been painting,
drawing of the future.
But let's not forget, we are living in a democracy.
And democracy relies on independent, self-determined,
confident human beings if it is to survive
And he addressed himself to the researchers and the academics.
So when developing these technologies,
don't forget to think of the consequences of your inventions
and how that fits into democracy and its principles.
In my second trip, and I'm going to be
brief about this, with my visit to China, again,
we also [? focused ?] on this topic.
And we also talked about social scoring, the opportunities,
the perspectives that result for the members of a society.
The debates we had were not easy, because at the beginning
the Chinese didn't understand why
we were asking these questions at all,
and why we would find some of these things complicated
that come up in the context of social scoring.
Because they said, we have 80, 90% of support, popular support
for these topics.
Why are you against it?
You know, we live under different political
circumstances, are scared and shocked
by the idea of having to submit to a total surveillance
of all aspects of our lives, that no matter what we do,
this might be linked up to a system
that assesses our performance in a negative or a positive way,
and that this, of course, has an effect on the way we developed
as human beings, that hopes, wishes,
and dreams are becoming externalized,
that they are stored on a software I no
longer have any influence over.
For our concept of individual responsibility
and of personal freedom is being called into question
by such an approach.
We, however, know that this is not a problem that
is exclusive to the Chinese.
German companies, American companies
that invest in the United States,
that employ people in the United States,
they will be working under the very same conditions.
And thus we have to have an interest
in what is happening here.
But let me close by saying that the debate in China
hasn't yet come to an end yet.
It is still ongoing.
We don't know what will be the outcome, the result of all
those tests and experiments that are being
carried out in China right now.
But the obvious question is on the table.
Is there something like a minimum
of morals for the digital age?
Shouldn't we work to have something like that like, like
a common expression of the limits
of the digital future in the decades or centuries to come?
Which brings me down to the question of whether we do not
really need a much more intensive exchange of thoughts
between the tech community, the political scientists
about the philosophy of the individual than
is happening at this point in time, at least as I see it.
Well, you know, if I were to choose, I would very much like
to be in a position where I could leave Boston today,
having received the confirmation from all of you
that I need not be afraid, that I need not be concerned,
that the debate is taking place in the very intensity
that I would wish to see attributed to it.
But whether that is the case or not,
we will have to hear and see from you.
I very much look forward to this debate.
[APPLAUSE]
URS GASSER: Thank you so much for setting
the stage so beautifully.
And I realized while you were speaking
that my American colleagues didn't have translation,
but you followed it very nicely.
CRYSTAL YANG: And I very much appreciated the baseball
reference.
MELISSA NOBLES: Yeah, I got that part.
There were a couple of words in there I kind of got.
URS GASSER: Yes, but you really set the stage so well.
Yes, that may be helpful.
MELISSA NOBLES: Great, thank you.
FRANK-WALTER STEINMEIER: Should I repeat it?
MELISSA NOBLES: Yeah, thank you.
Bit of a summary.
I heard "Stanford."
I heard--
URS GASSER: Right, right.
And of course you picked up on that, which is exactly
the segue to my question.
MELISSA NOBLES: Sure.
URS GASSER: So the president was putting
sort of the societal change that we're going through,
where technologies of different sorts
play such a vital role in the larger
context of the future of democracy,
and the question of how do we want to live our lives
and interact with each other and shape our future.
And within that, he also referred to,
as you picked up on, a trip to Stanford,
and pointed out already, and that's
a theme I want to follow up on for a few minutes--
MELISSA NOBLES: Sure.
URS GASSER: --that there are tremendous opportunities,
although currently the focus is really
on the risks of new technologies essentially
in public discourse, for sure, particularly in Europe.
But before we go into risk mode and talk
about all the pitfalls of these new technologies,
I would like to pause and really zoom
in a little bit on this question, what can technology
do for climate change and other areas
that the president mentioned.
FRANK-WALTER STEINMEIER: Against climate change.
URS GASSER: Against, yes, against.
MELISSA NOBLES: Right, right, exactly.
URS GASSER: To address some of the big, big challenges
of our time.
MELISSA NOBLES: Sure.
URS GASSER: And there is this other place closer to home,
MIT--
MELISSA NOBLES: Right.
URS GASSER: --where many of these technologies are
developed in the lab, and I was wondering whether you would be
willing to share maybe two, three examples from also
your humanities perspective--
MELISSA NOBLES: Sure.
URS GASSER: --that give you hope and optimism, maybe.
MELISSA NOBLES: Sure, I'm glad to do that.
Good morning, everyone.
Well, you know, one of the things about MIT, I
kind of hesitate in a certain way
to be able to say two or three, since the Institute
is connected to technological innovation.
So think I'd rather say a bit about what
has made MIT such a leader in thinking innovatively.
And a big part of that has been the commitment to collaboration
across all five schools.
So it's in recognition that many of the problems that the world
faces are obviously global in nature,
and they require knowledge from all domains.
It isn't just a scientific problem.
It isn't just an engineering problem.
It isn't just an economic problem or a social problem.
It is all of these things together.
And part of our strengths have been putting together research
programs to deal with these.
So we have, for example, the MIT Energy Initiative,
which brings together professors from engineering, science,
humanities, also social sciences, the Sloane School,
to look at the economics and the business
models of what is sustainable and what not, as well
as architecture and planning to look
at the ways in which climate change and the way we
use energy is changing how we structure cities.
So it is the scope of the problems
and a commitment to putting intellectual energies that
are commensurate with them that, I think,
has set MIT in a better way for thinking about the future.
So I hesitate to say any particular,
except to say that the problems are so massive,
there is no way that technology cannot be a part of it.
And the issue is, how do we think creatively
about technology to make sure that's happening.
And that's a big part of what education has to do,
to connect students to understand
that the technology is an expression,
is a human endeavor, right?
We create the technology.
The technology doesn't create us,
and we have to start with some basic commitments.
So that's where we are now.
And I look forward to saying a bit more later
on about the College of Computing.
URS GASSER: Fabulous.
Thank you so much.
That's very helpful.
Some sort of an iteration on this theme,
and taking your point, where you argue,
well, there's no future without getting technology right
in a way that helps us to address some
of these big challenges we face as humanity,
but also to embrace the opportunities.
And I was wondering whether you would
be willing to share your thinking around this topic.
Much of the ethical debates of these days
are focused on ethics in the sense of telling
us what not to do, right?
What lines not to cross.
And we will definitely return to that,
and this will be the key part of the panel.
But before we go there, I was wondering,
is there some sort of an ethical obligation
for the good use of technology?
And basically a moral imperative that would almost
be in contrast to the precautionary principle that's
so popular in Europe these days, and say,
no, we have to double down on developing technologies
for the social good and in the public interest.
How does a philosopher or ethicist think about that?
EVA WEBER-GURSKA: Yeah, thank you for that question.
I'm happy to answer.
So I think there are at least two ways
to understand your question.
First, we may ask if there is a moral obligation
to generally use digital technology now
that it has been invented and developed up
to a point where so much concrete applications are
possible.
But my answer to this would be no.
There is no general moral obligation
to do what can be done.
Because digitization is just a means,
and moral obligation refers to ends, to purposes, not the way
we get there.
And so it is an open question if digitization
is the best way to get there, where we want to go,
to our moral purpose, which is, as you already
pointed out, good democracy, human flourishing, and so on.
And we have just to see exactly where digitization is helpful
and where not.
But on the other hand, if you ask
if we theorists, theorists like us here on the panel,
should point out possible positive uses of technology
more often, I would say yes, and that's important, too.
Because otherwise, the development
of digital technology mostly is driven
by interests for financial profit,
and that is not the best premise for the best
outcome in a moral perspective, from a moral perspective.
So it would be surely good to have more people pointing out
the positive uses, but I think there are already
quite examples for that, too.
And also where reflection and realization goes together.
For example, at the Weizenbaum Institute
there was a fellow in summer in Berlin.
A young colleague went and developed
an app, which enables people from different parts
of the political spectrum to chat and discuss it
with each other online, for example.
And yeah, I mean, there are a lot of opportunities,
and we should point them out, but also I
want to add that although these projects all
have to be chosen carefully, because you mentioned already
with climate change, we can do good things against climate
change with digitalization.
But on the other hand, we also have to be aware of the fact
that digitization, all digital technologies
themselves are consuming masses of energy.
So it would be best to choose only those projects which
have a really urgent reason.
There has to be something important at stake
that we invent and apply new technologies.
And I remember the British philosopher Dave Parfit
saying that all people, all humans with healthy,
with two healthy legs should use the stairs instead
of the elevator in order to save energy.
Because he said elevators are just made for people who cannot
walk.
And a bit in a similar way, we should always
watch out where are the urgent reasons that we invent
digital technologies for.
And what is urgent, what is important always
depends on the domain.
It's different in every domain.
In medicine, for example, it's the diminishing of suffering.
In law, it's justice.
In a democracy, it's participation and about
[? founded ?] formation of political opinion.
And only then, when we have identified precise moral
purposes and we see that we cannot attain them
but by digital technologies, then I think then we might
be seen as obliged to use them.
URS GASSER: Wonderful.
Great segue.
You pointed out sort of the big questions,
but also that these questions can only
be answered or worked through in a particular application
context.
You mentioned already a few.
And if I may, to get a little bit more specific
and put the conversation from 34--
you know, go from 30,000 feet a bit lower to 10,000 feet,
maybe, and take two examples that
illustrate some sort of the struggle, how
we embrace opportunities, but also protect against risks.
And Matthew and Crystal, as I already
mentioned in the introduction, you
have interesting work that's sort of serves
as a case study in our context.
Matthew, focusing on health and public health and the role
of technology, whether it's AI or IoT,
how are some of these questions that you've
identified crystallizing, and where do you see things going?
What are some of the concerns?
What's the state of play?
MATTHEW LIAO: Yeah, so good morning, everybody.
So as a Professor Gasser has said, I'm a philosopher.
And I have a book coming out called
The Ethics of Artificial Intelligence coming out
next March.
And we cover a number of these different issues
in the ethics of AI.
And one of the applications of the ethics of AI
is in the realm of health care.
They are actually a lot of really exciting opportunities
and a lot of development, a lot of things being done
in the area of health care.
So for example, machine learning is being deployed
to screen cancer cells.
It's found that it's almost as effective as radiologists.
It's also been used in ophthalmology.
It's been used to screen, to figure out whether a embryo is
going to be viable or not.
Natural language processing is being
used to figure out whether people
are having suicidal thoughts.
So there are a lot of really exciting developments
that are currently underway.
And what that means for us is that it
can really, for example, reduce health care costs in the US.
I think we spend about $3 to $4 trillion in health care
each year.
And so one of the things that machine learning can do
is reduce administrative costs in health care, for example.
It can also assist facilitating drug discovery.
And finally, another example is, it
can really realize the vision of precision medicine.
So for example, Fitbits, wearables,
to figure out healthy lifestyles,
what you should be eating, your calorie
intakes and so on and so forth.
So all those are really, really exciting developments.
I'm an ethicist, so I also think about some
of the ethical problems.
And I just want to very quickly share
some of the ethical concerns with you as well.
So one of the biggest challenges with machine learning
is that it requires a lot of data.
And so what that means is, someone's
got to go out there and collect all these data.
And then you get into issues about privacy,
especially in health care.
It's personal data that we're talking about.
So one obvious example is Facebook and Cambridge
Analytica collecting a lot of information
from Facebook users.
Another example is GlaxoSmithKline.
They just recently bought this company 23andMe,
which is ancestry type, you upload your information,
it gets your genetic information.
So now they have all the database.
And so one of the things we need to really be worried about
is whether they're collecting the data appropriately,
are they violating rights, what are the implications
of the individuals?
Another issue is going to be the garbage in,
garbage out problem.
So the algorithms that we're using today
are going to only be as good as the data themselves.
And so what we're finding is that
sometimes the data sets that we're collecting are not--
they don't have accurate representations
of the subjects.
So take for example self-driving cars.
It turns out that self-driving cars,
they're not so good at detecting people of color,
because the training sets, the training data that they use,
they don't have enough of a set of people of color in the data
set.
And so that's a problem when we deploy those sort of data, sort
of the algorithm in the wild.
And I'll just say one more thing.
The biggest concern I have with machine learning right now
is there's something called deep learning.
And deep learning is actually a technical term.
It just means that it's using a big network to figure out
how a machine should act.
And it's powered a lot of the recent developments since 2012.
It's powered a lot of the new breakthroughs.
But one of the problems with deep learning
is that it just doesn't capture the causality,
the causal relations.
It doesn't really understand what it's doing.
And so it's linear regression.
It's a lot of math.
But here's one problem.
So there's something called generative adversarial network.
It's a type of single--
so one type of attack is something
called a single pixel attack.
So machine learning is very good at image classification.
They can take images, and they classify them very accurately.
But science-- researchers have found
that if you just take an image, say an image of a car,
and you just take one pixel and you change it
from black to white, the machine learning
will completely screw it up.
So for example, with the image of the car,
it will now classify that image as a dog with 99% confidence.
And just imagine deploying that type of machine learning
in the context of health care, when people's lives are
at stake, or in the context of self-driving cars, right?
And so I think we're going to get
into more of these discussions later,
but I think that's where we have to be careful about rolling out
these technologies.
URS GASSER: Crystal, does that sound familiar,
listening to these stories from health
when you look at your work on the use of algorithms and data
in the criminal justice system or where are differences?
CRYSTAL YANG: Yeah, I think there
are a lot of similarities.
And I think as some of the other panelists
have pointed out, while algorithms are now basically
used in so many parts of society,
one of the areas where they have had
a very dramatic increase in usage
is the United States criminal justice system.
And the algorithms here we often call risk assessment
instruments, because what the algorithms are trying to do
is predict somebody's future criminality.
And these instruments now are used
at various stages of the criminal justice system, things
from policing, to pretrial and bail decisions,
to sentencing, to probation and parole as well.
And just some examples, take predictive policing.
One of the common technologies is
called Pred Poll, which is used by the Los Angeles Police
Department and over 60 other police departments
across the United States.
It uses historical data on crime types,
where crimes have happened, to predict
the future incidents of different criminal incidents.
In sentencing, now many states allow
judges to consider risk scores that
are meant to predict the future risk of committing
new criminal behavior.
One common algorithm here, which has been in the news a lot,
you may have heard of, is the Compass algorithm.
It's a proprietary algorithm, so we actually
don't know exactly the underlying algorithmic
structure, that classifies individuals
on a scale of 1 to 10 in terms of their predictive likelihood
of recidivism, using how that person answers questions
on a 137 question survey.
So these are just some of the examples,
and I think they raise a huge host of issues and challenges.
One that I think requires a lot of understanding
from philosophers and ethicists is, do these risk assessment
tools have a role to play, even, in the criminal justice system?
I think some view the endeavor of predicting future risk
as wrongheaded, and believe that because of this input data
garbage in, garbage out type of problem, that using algorithms
to predict future risk will only entrench or potentially
exacerbate inequalities and inequities that we
see in society at large.
On the other hand, and I place myself more in this camp,
while there is acknowledgment that the algorithms are often
imperfect, I think it's also important to consider
the relevant counterfactual.
The counterfactual is not a world free of inequality,
of inequity.
It's a counterfactual in which we have human decision makers.
And guess what?
There's lots of evidence that human decision makers
have a big role to play in perpetuating inequalities
through bias and inconsistency.
So there's a role to, I think, to consider what are
we comparing the algorithms to?
It's not a perfect world.
It's humans.
I think another set of design questions
that Matthew has gotten at is, in the criminal justice system,
there are lots of open, unresolved questions
about how do we design an algorithm?
If we're going to predict risk, can we
consider individual characteristics
like somebody's race or ethnicity, what we often
call protected characteristics?
If you can't, can you consider non-protected characteristics,
things like education, where somebody lives,
which can effectively proxy for a person's race or ethnicity?
There is also complicated questions
about how to evaluate if an algorithm is
doing what we want it to do.
What does it mean, for instance, for an algorithm to be fair?
It turns out here the law has not so much
to say so far about how to define or measure fairness.
And even outside the law, there's a very lively computer
science debate about algorithmic fairness, where there's
very different definitions of fairness,
that in many circumstances we could all
say that one sounds great or that one sounds great,
but it's actually been shown mathematically
that in many instances it is impossible to simultaneously
satisfy all those notions of algorithmic fairness.
And so then that requires a normative choice by us
as a society or a legal system to choose
which of those definitions of algorithmic fairness
should dominate.
So I think those are just a couple of the key issues
and challenges that I see in the criminal justice system.
URS GASSER: No shortage of challenges.
CRYSTAL YANG: No shortage, absolutely.
URS GASSER: It was clear from both stories.
I'd love to build up on this and ask Wolfgang,
Crystal made the point that part of it
is a story about technology, but part of it
also seems to be a story about society at large,
about the institutions we already have in place.
Part of it seems to be about human nature,
with our own biases.
So how do you think about that as we have this intense debate,
debates about AI decision making versus human decision making?
And should we replace judges by AIs or not?
How much is it about technology, really?
WOLFGANG SCHULZ: I think to respond to that,
I have to go to flight level 10,000 again.
I would say that I am descending later.
URS GASSER: Only in a good way.
Don't worry.
WOLFGANG SCHULZ: In a good way, yes.
I hope.
So when we talk about technology in expert circles
but in society at large, then we very often
have a distinction between here is the society,
here is the technology.
And that is a dangerous thing, because then we
frame technology as a kind of natural disaster
that is coming, and we have to build walls to cope with that.
And we are not in the mode of creating
the technology as a society and together
with the different disciplines.
So I think we have to be very careful
how we talk about these things and where
we talk about tensions.
And I can, I think, build on what Crystal said, because we
are doing some research on the criminal justice system
as well, and have done recently.
And what I find interesting is that when
we talk about technology coming into processes, then
we start thinking about what our quality
measures are as a society here.
And I had a discussion with German judges
a couple of months ago, and we are talking about sentencing.
And we are talking about AI supporting that,
and then I raised the question of explainability,
which is one of the issues in AI,
that say we cannot really see and explain what happens there.
And then one of the judges said, wait a moment.
Ask me, can I explain what I am doing
when I come to this decision?
And I'm not sure that I really can do that.
I can give a reason that is valid in the legal system,
but I cannot really explain what my motives were here.
And then we had a debate on what are the factors there.
And in the German legal system and the criminal law,
it's not very well elaborated what the criteria are.
So it's very, very vague.
And so we had a very fruitful debate
on what the values actually are.
And you can have the same in other fields of society.
We have next week or the week after,
a workshop with computer scientists
and people from communication science and law,
talking about how to understand diversity in recommender
systems for the media.
And we want to come up with ideas what that actually is.
And then you have to go back, and what
do you want as a society actually,
when you talk about diversity?
So I think that's a good thing that technology forces
us to ask those hard questions about societal values
and to better understand what makes human decision making
so special.
We are talking a lot about things like tested knowledge
and tested norms, things that we all understand because we
are part of the society.
And we cannot really explain why we do that this
way or that way, because it's tacit knowledge or tacit norms.
And that is something that you cannot really, now,
I would say, build into technology.
That would require technology to be part of society
and learn in interaction, and I think we are far from that
so far.
So I believe that this is a twist of the debate
that very often we do not really include
in our conversation, when we talk about this society
here, technology there aspect.
URS GASSER: So if Wolfgang is right,
and he is most often right, as we know--
JEANETTE HOFMANN: I don't deny that.
URS GASSER: And technology is deeply embedded in society,
and as we heard the president opening, in his opening
remarks, we as societies are in a learning
process ourselves, how to cope with massive challenges
and transformations of all sorts.
Based on the work you've been doing, following
early debates around internet regulation
and approaches to governance, what's currently
happening in this societal learning process
as we try to identify and agree and regulate
good uses versus bad uses across different contexts,
and we only highlighted two examples
and could add many more.
What sorts of norms are emerging and what
some of the dynamics around these norms as you observe?
JEANETTE HOFMANN: Thank you, Urs.
I could talk for hours on this question.
I really like it.
Let me go one step back.
At the time when the internet and digital technologies--
URS GASSER: Switch off the mic.
On the mic.
FRANK-WALTER STEINMEIER: [SPEAKING GERMAN]
JEANETTE HOFMANN: OK.
Around the time--
FRANK-WALTER STEINMEIER: Well, that's better.
JEANETTE HOFMANN: --Digital technologies really
became more present in our societies,
Western societies went through a long period
of privatization and liberation from old state monopolies.
And we thought of that the force of the internet
as a form of liberalization, and that kind of idea
of self-regulation and "let the markets determine the future,"
we thought that this was a very good alternative.
And this we have driven to a point where we now
regard digital technologies nearly
as a self-driving autonomous force.
We ascribe a lot of power and agency to digital technologies
themselves and the companies who develop them.
I would say that the debate we see now
about AI and ethical frameworks is
an echo of that, the idea that ethical principles might
be good enough to give us an orientation for the future
of artificial intelligence.
But we need to ask ourselves whether we
get enough accountability out of ethical guidelines
and frameworks.
I just came back from the West Coast,
where you see really a change of wind.
Companies now begin to wonder whether they
do not need a legal framework for the future development.
Such a legal framework could be, for example,
anchored in human rights, and legislation could
build on fundamental rights.
They could set limits to future developments,
also to make us see that finally it is
society that shapes technology.
It's not that technology sets its own rules.
But we are not really aware of it, I think, at the moment.
We nearly have lost the capability
to see and to recognize how we change technologies
as societies.
So we need to perhaps turn around a bit,
give up this idea of complete self-regulation,
and come to new models that sit somewhere
in between a market approach and a pure government approach.
We need new regulatory frameworks
that need to work across national boundaries,
even though we can, I think, not hope
for multilateral approaches.
We need something below, and the GDPR, the General Data
Protection Regulation that the European Commission introduced,
is often mentioned as a gold standard
for that kind of approach.
Perhaps some countries can get together,
build a legal framework and export it via trade agreements.
[APPLAUSE]
URS GASSER: That is some good advice.
Thank you, Jeanette.
So a couple of things that I would like to follow up on.
One is this role of the ethics principles.
You mentioned there is a flourishing
of ethical principles around AI in particular.
I think 130 or something are out there.
We tried to map some of them, but it's getting quite a task.
But on the other hand side, given also
Wolfgang's remarks and the opening statement by Herr
Bundespresident, there is value to these ethical debates,
nonetheless, right?
And you also make this point, of course,
that we need all different approaches and tools, probably
including law but also ethics.
And if I may ask you, how do you think
about these ethical principles?
What's the value in these ethical norms
crystallizing guidelines and things like that,
whether it's for companies or an act
by international organizations like OECD or even
by nation states?
What's the promise, but also what
are the limitations of ethical approaches
of this sort when we deal with these complex, messy problems?
EVA WEBER-GURSKA: Yeah, so ethics and law of course
have to be distinguished, although they are connected.
Ethics is, I would say, the explicit formulation
of implicit norms that guide or should
guide our everyday actions in our life, our living together.
And law, it's the core of the organization
of a state or a nation, transforms some of these norms
into concrete rules, the infringement of which
then is bound up with sanctions by the state.
So this is something different.
And not all moral norms are legal norms and vice versa,
of course.
But ethical guidelines now for new topics like digitization
can be, I think, helpful first steps
to show something that then can be transformed into law, too.
URS GASSER: Speaking of law, what's
your hope that, looking at your area of research,
the law will evolve in this dynamic situation, where
maybe ethical principles may lead the way?
Where do you see the promise of law
in these debates, where we're facing
this shift from the human towards the machine?
CRYSTAL YANG: Yeah, I think law has a very important role
to play here.
I think I share Jeanette's general sense
that self-regulation is probably not
going to be a sufficient solution, that there
have to be legal interventions.
And the law is both instrumental in
that it will undoubtedly, by deciding what to permit
and what to prohibit, shape the behavior of governments,
private companies, in terms of how
they design algorithms, how they implement them on the ground.
The law, I think, also has important expressive principles
maybe related to ethics, where if the law allows
for something, then citizens, members of society,
will view something as maybe more socially acceptable.
So I think the law here has a big role to play.
Coming back to the criminal justice system, though,
I think there are many ways in which the current law,
certainly in the United States, falls
short for a lot of the new challenges that
might come with algorithms.
So to give you some examples, many people
are troubled by the use of disparities
that can emerge when you use an algorithm to make decisions.
And that could be because of the data or the structure
of the algorithm.
Now, it turns out there's probably
pretty limited legal remedies for addressing
those disparities.
Under current US law, a finding of discrimination
under the Equal Protection Clause of the US Constitution
would require a showing of discriminatory intent
or purpose.
And that's hard, because when an algorithmic designer chooses
to use a variable or certain types of data,
there's probably often no discriminatory intent
or purpose.
And yet because so many variables
can be proxies for things we're troubled by,
there's often maybe no direct legal remedy.
And so this traditional requirement
we've had in the US Constitution and case
law of requiring intent and motive
is often ill suited to addressing
the new types of problems that the algorithms can introduce.
Moreover, it's actually been the case in the US
that many have interpreted the case law on discrimination
as requiring or prohibiting the use of characteristics
like race or ethnicity.
You can not use them in any way, shape, or form.
But the reality is that because of the complex statistical
relationships underlying many variables, I, other computer
scientists, economists have written and shown
that those proxy effects that we may be worried about
are often created because of the prohibition on the use
of those characteristics.
And that once you take statistics into account,
you may actually want to use protected characteristics
in certain forms to actually remedy those disparities.
And so it's actually this problem right now,
where I think the law is pushing companies, governments
to develop versions of algorithms that may actually
be counterproductive to our larger societal goal
of equality and opportunity.
And I think, to the earlier point about human decision
making, the law often does not consider counterfactuals
in a very easy way.
It often seems to require perfection for algorithms,
explainability.
But as you point out, what is more a black box
than what is in a judge's mind?
Perhaps the judge's mind is more of a black box
than a neural network or other forms of machine learning.
And so I worry that the law, by sometimes requiring perfection
and not considering the counterfactual,
will often chill and deter what may be innovative and good uses
of algorithmic decision making.
URS GASSER: So also the relationship
between technology and law and law and ethics
is very complicated and bi-directional,
with unintended consequences included.
FRANK-WALTER STEINMEIER: [SPEAKING GERMAN]
INTERPRETER: Still, if I may, some of you
have put this on the same, on a par in a way that does not
convince me of any decision taken by an algorithm
being plausible.
You know, the fact that we don't always
understand algorithms and software that
is guided by algorithms, that leaves me very concerned.
We say, and the situation in America
is slightly different from the situation in Germany.
A judge passes a sentence or a judgment,
he or she has to justify that decision.
Not every ruling or decision has to be accepted.
People may have a different opinion.
But as a rule, as far as the tradition in Germany
is concerned, you do have a very extensive duty
to justify your sentence or your ruling.
And that is what lacks when you talk about algorithms.
That is one of the questions that we need to discuss,
I believe.
Is it conceivable at all that algorithms,
that the control of our algorithms
can become or be made more transparent?
Of course, not towards each and every individual person,
but perhaps with regard to those who
consider themselves the representative
of the government.
You know, the body in question responsible for protecting
the rights and the freedoms of the individual.
Can you hear me?
And a second remark that I'd like
to make against the backdrop of what
has just been said, it is good that we have a debate
up here on the rostrum, so to speak,
about the ethical principles for digital transformation.
But what struck me, and I've tried
to refer to that in my introductory remarks,
when I bring together a group of experts in my office,
I have experts briefing me about the technological potential
of AI.
But, you know, I have an idea after these talks of what
is doable, what is conceivable.
But when I have talks about the ethical limits of digitization,
it brings together a wholly different group of people,
because as a rule, I do not meet IT experts or engineers,
but I meet social scientists, philosophers,
political scientists, which is an indication, to some extent,
of something that keeps me deeply troubled.
And that is that we have a debate, not
that that debate takes place within closed circles,
closed communities.
That is to say, we have a debate about the ethical limits
modality of a digital age and that we
have to bear in mind with limits that we should
not to surpass or overstep.
We have a similar debate about the function of democracy,
but it is not carried beyond the respective communities.
Please tell me if I'm wrong.
I'm happy to hear you point that out to me.
But as I see it, and as I wish to see it,
the two communities that I've been mentioning,
the tech community on the one hand
and the more philosophical community
bringing together social scientists and philosophers,
we don't have a discussion that brings both groups together.
We haven't been able to link up that discussion.
Is that impression that I have correct?
Would you agree with that?
Is it limited to Germany, or would you
say that this is also transferable to the debate
in the United States?
URS GASSER: Change that right, over at MIT?
Can you can share your thoughts?
And then I would like to open up for a number of questions.
So be ready with your questions.
MELISSA NOBLES: Sure.
So the Schwarzman, the College of Computing,
which was announced last year, is
intended to get at just this issue,
that much of the technology that is obviously being created
at MIT, we recognize that there has
to be a bridge between technology and the humanities
and the social sciences in an intentional, deliberate way.
And part of why the school was established
is, tons of students are interested in computing.
They're doing it.
They're coming in, wanting to major in computer science,
but many of them don't want to only be computer scientists.
They want to apply that knowledge to something else,
but they want it to be guided by some domain knowledge
outside of computer science.
And so the goal of the college is to eventually-- we're
beginning to see joint, blended degrees between computer
science and economics, computer science and urban studies,
computer science and music.
Not all students are doing this, but the interest is great,
and it's intended to allow for this connection
in a more organic way from the beginning,
such that students will have the type of skills,
so that we won't be talking about disparate communities,
but that students will have enough
of an openness, at least an exposure understanding
that this is what it means to be a computer scientist.
And conversely, for my own discipline,
I'm a political scientist, this is
what it means to be a political scientist,
is to know something about this.
So all of us are going to have to learn more and be
open to learning more, if we're going to successfully deal
with this issue.
One other thing I'd like to say about it
is, we're starting to have these conversations on campus.
They are not easy conversations to have.
As much as we try to be collaborative,
we've really had to work.
We may be using the same terms, but we
speak a different language.
And it requires patience to do this.
So part of what we're doing is also
learning some other principles of generosity and patience
as we deal with one another.
Because if we want to solve this problem,
deal with technology in a way that we all want to see,
then that's what it's going to require,
some other human qualities that we have to bring
to bear for this to happen.
And I think especially just inclusion
is really important for us for our undergraduates,
since many of them will be going into leadership positions.
They know the technologies.
We need them working on the congressional staff.
If you all saw the hearings with Mark Zuckerberg
in the Congress, people didn't know what Google was, right?
Or they didn't know the difference
between Samsung and Apple.
I mean, they didn't know anything
about the technologies.
If they don't understand how to open their phones,
how can you imagine that they could
be responsible and entrusted to do the kinds of things
that you all are describing?
Some of what we also need, our students
to be able to play those kind of roles, precisely because--
but we don't want them to do it only knowing the technology.
They will also have to understand economics, also
have to understand political science, and such.
So that is the task of the college.
And we're just getting started, so stay tuned.
URS GASSER: That's exciting.
That's exciting.
Thank you.
OK, let's open up for a few questions.
[? Becca, ?] our mic-runner is ready and fast on her legs.
Who has a question?
And please end with a question mark.
That would be good.
[LAUGHTER]
AUDIENCE: Me?
First thank you so much for all the interesting insights
you shared today.
My question is, regarding the fact that a lot of you
mentioned today that there's an urgency
to craft tech specific ethics regulations as soon
as possible.
So in a way, this is really a moral discussion
with a deadline.
When would you say is this deadline?
When do we have to formalize our thoughts and put it into law?
EVA WEBER-GURSKA: Oh, was that a question for me?
URS GASSER: Now it is, yes.
[LAUGHTER]
EVA WEBER-GURSKA: No, of course I think the deadline is--
it's not far ahead, but it's just right now.
But there are already a lot of ethic guidelines
being written right now.
So we really have on national level a different one
in Germany.
Then there are international levels,
like, for example, the high level export
group that got tasked by the European Commission.
And they wrote something, and then
we have already part of this ethic guidelines, for example,
extracted, and the G20 group signed it.
And so there are already guidelines,
but I think the guidelines are only the first step.
And then, of course, they're our first step
to transform them into law, as we heard already
with the [GERMAN].
And so yeah, it's right now, and we should go further ahead.
But it's still already something happening, I think.
URS GASSER: Wolfgang?
WOLFGANG SCHULZ: Yeah, maybe I can add a legal perspective
to that, because one of the problems of the law
is that the function of law is to have stability.
And what we need here is some flexibility as well.
And so what we are struggling with, as legal scholars,
is to find ways to make law more flexible,
to have constant evaluation, to have sunset clauses
and things like that, so that we do not have to wait.
I think it was Susan Crawford said the marvelous sentence,
we have to regulate things that we don't understand.
And I think we are-- can't wait until all of the lawmakers
have understood what it actually is.
You have to act before.
But then we need different instruments, especially when
you take into account what you said,
that when you are talking with lawmakers, even if they really
try hard, the problems are really high tech.
Only a couple of people really understands what's happening
there, and even the best member of parliament cannot
be an expert in this field.
So we need constant evaluation and some mechanism
seems to deal with.
Every day, even we as researchers,
every week I have a new understanding of how algorithms
interact with society.
Every meeting, I have an interaction with some software
engineer and oh, OK, it's a little bit different
than I thought before.
And on this basis to create law, that's, I would say,
a fundamentally new challenge.
FRANK-WALTER STEINMEIER: [SPEAKING GERMAN]
INTERPRETER: But if I may add to what you've just said,
the situation is changing.
When you take a look at the German legal culture,
you know, it's based on the assumption
that a law that is passed is, until kingdom come,
it's for eternity, you know?
When we know look at the area of internet law,
something interesting is happening that is
quite difficult, complicated.
When you look to the relationship between the one
passing the law, making the law, and the public,
you know, public comments about the Network Enforcement
Act in Germany, for example, is an indication of that.
In some areas of legislation, we have already reached a point
where we can no longer provide an eternal guarantee
for the legislation that is being passed.
We are taking one step, hesitatingly after the other,
carefully trying out to see how that intervention is
going to affect the reality in the future.
This careful and cautious, tentative approach
in legislation that is taking place right now,
it's not something that is being greeted with enthusiasm.
And I do understand, but there might be no alternative
to that approach, you know.
Trying to time and again refer back
from the instruments to the technology and vice versa
and to amend things when necessary.
URS GASSER: One of the dark sides
of having such an amazing group of people
is, we could discuss 20 minutes just one question.
So you want to jump in quickly with comments, Jeanette
and Matthew?
JEANETTE HOFMANN: I thought perhaps one way forward
could be to pursue a more procedural approach
to these problems.
For example, think of ways to holding companies
accountable to the kinds of technology development
they tried to bring to the market,
introducing auditing requirements
for certain type of algorithms.
Make it mandatory to only use in certain areas machine
learning systems that are self-explainable,
that explain, in at least basic ways,
how they come to certain recommendations
and predictions.
That seems to be a way forward, rather
than relying just on rules.
MATTHEW LIAO: Maybe I can also jump in here.
So I think Professor Gasser's mentioned
that there are about 130 ethical principles that
are being presented by different companies
and so on and so forth.
I think what we really also need is a rationale,
or philosophical--
this is a plug for philosophers--
a philosophical justification for some of these principles.
So for example, they talk about-- a lot
of these principles say things like "we need explainability."
Why do we need explainability?
I mean, we've heard some of the panelists asking this question
and various other things.
And so here in that line, I'm very sympathetic to what
Professor Hofmann's saying, which
is this idea of the human rights framework, which says that, we
need to look towards the goal.
What are these algorithms for?
Fundamentally, they are about promoting human well-being,
right?
We want to make sure that we have a harmonious society, one
that works towards all of us.
And so a human rights framework, I think,
can really move towards that goal.
And there's a rich tradition.
There's a rich literature on these
philosophical justifications of the different rights.
And they go beyond just discrimination, right?
They say-- they're positive rights.
They're rights where we just-- it's not just
about making sure that you don't discriminate, by making sure
that your technologies also work to help people
and so on and so forth.
And the other thing about human rights
is that it's an obligation on everybody.
So it's not just an obligation on the engineers.
It's not just an obligation on the company.
It's not just an obligation on the government.
It's an obligation on all of us.
We need to collectively make sure
that we're working towards making sure
that these technologies work well for everybody.
URS GASSER: Crystal.
CRYSTAL YANG: I promise to be very brief.
I know there are a lot of hands up.
This is such a fascinating question.
I think, I wholeheartedly endorse the perspectives others
have raised, especially Mr. President,
that the laws must be adaptive.
They must be flexible, because we are still learning how they
work, how the algorithms work.
And so we have to also study when a new regulation goes
into effect.
What does that mean about the types
of algorithms we're seeing that flourish after that?
What types of algorithms are now disappearing as a result?
To that point about explainability,
I think we all have this desire to understand
what the algorithm is doing.
And so we often, then, might shift
towards regulation or principles that the algorithm
be explainable.
The complication there is, now there's
emerging work coming from computer science and economics,
showing that when you force an algorithm to be explainable,
you generally will choose an algorithm that's
simpler, because it has to be easier to understand.
But a simpler algorithm, as it turns out in some contexts,
actually can lead to both less efficient results
and less equitable results, which again raises a conundrum
that I think no field alone can address,
but just reveals that there are inherent trade-offs
every time we make a choice, like explainability.
And we have to confront those trade-offs
and decide how do we weigh competing values, which
are inevitably going to be at stake.
URS GASSER: Great.
So let's collect three questions,
and then we'll respond.
One question here, and then maybe one
from over here, this area.
AUDIENCE: Yeah, I'd like to add an observation.
I think it's also observable on the podium
that we are missing economists, and we are
missing behavioral scientists.
And it seems to me that these two components are
crucial in understanding the impact that AI has had
and will have on our society and each of us.
Why do I say this?
Because AI has enormous economic potency.
In this country, the majority of the productivity
of this country comes from AI.
And why is it that Facebook and Google and other companies
have been doing undaunted what they have been doing
is because exactly of that.
And so that is one reality that we have to face.
And this reality is deeply immersed in research as well.
Where is most of our funding going?
It is going to computer science, to computer engineering.
And then we have some alibi, excuse
the term, addition of social sciences,
and if we are lucky, behavioral sciences.
There's no--
URS GASSER: Thank you.
Sorry, we have to stop there.
AUDIENCE: It's just-- it's very-- well,
we've heard a lot from the panel.
[LAUGHTER]
URS GASSER: Fair enough.
AUDIENCE: I'm sorry, but there is no level playing
field between the behavioral sciences
and all the psychological dynamics that
are opened up by AI and computer scientists
and computer engineering.
Unless we change these funding structures-- and we've
heard a lot about the necessity for other regulations
for companies, but these funding structures
have enormous consequences.
President Steinmeier was asking, is there
any example about, at the beginning
of such an enterprise, to bring disciplines together?
I would say, yes.
And actually at the Ruhr Universitat Bochum,
there is one competence cluster that
focuses on cybersecurity, which tries
to give level-headed, equal importance
to social behavioral science on the one, economics
and computer science and computer engineering.
So I just hope that in the future,
such discussions move beyond very important contributions
from philosophers, ethicists, and lawyers
to also have a brighter view.
[APPLAUSE]
URS GASSER: Not really a question.
AUDIENCE: I promise to be short.
URS GASSER: Back up.
We collect there, please.
Go ahead, please.
AUDIENCE: I'll keep myself short.
URS GASSER: Yup.
AUDIENCE: As we are at a German-American conference
right now, I just want to ask, how does the transatlantic
relationship help us in solving all these challenges?
What is needed for an effective transatlantic relationship,
especially the German-American one,
to solve all these challenges together,
as like a world and not as like separate states?
URS GASSER: OK.
Maybe one or two more?
Yes, please.
AUDIENCE: I have a question.
[LAUGHTER]
URS GASSER: I appreciate that.
AUDIENCE: Do you think, talking about social media,
that the time will come that there
will be a reliable algorithm to identify hate speech?
URS GASSER: OK, over there.
Last question.
AUDIENCE: I promised a short question.
AUDIENCE: I'm one of those computer scientists writing
those messy algorithms, and I know
that there are people in my field
who think very critical about this.
And I know there's a lot of discussion.
So I can convince you there are people
behind the current talking about the things.
How can we reach out to other people
who are thinking about this?
AUDIENCE: So my question is short.
Thank you all.
This was delightful.
I would like to understand, are we being human race empowered
by technology?
Or are we powering technology by humans?
URS GASSER: Great.
All right.
We have 12 minutes left.
And I'm Swiss, as I said.
So I want to end on time.
So what I would suggest is that we actually do a closing round
and pick the question that you would like to address,
but put it into the context also of your work
and what we've discussed here.
So we have the question of transatlantic relationships.
We have the question around social media
and the role of technology in creating
a safer environment using the example of hate speech.
And we have this ultimate question,
is technology empowering people, or are people here
somehow to empower technology.
So these are a few of the themes.
Perhaps we start with Eva.
EVA WEBER-GURSKA: Mm-hm.
Yeah, maybe to the two last questions.
Of course, I think that the technology should empower
people, but for that human machine interaction,
it is important that we understand each other,
as we already talked about.
And maybe just into two aspects that philosophy can contribute
here, and the one is, it's not only about explainability
that you said, and it's not only what is also
important about the ethic or moral justification of why it's
so important to explain, but also to see that in morality,
for example, it's about reason giving.
So the whole validity of moral norms
depends, I think, on the fact that they
exist between beings that can give
reasons and understand reasons.
And so one question would be, do we want algorithmic structures
who cannot give reasons in an empathic sense, for example.
And another topic would be the topic of trust,
because the ethic guidelines often highlight
trustworthy AI as a claim.
I would also be skeptical of this as a best aim,
because trustworthiness presupposes
also being a moral subject, because trust
means to believe that someone will hold to his or her
commitment to do something.
And this is also something that is only
possible for a moral subject.
So AI systems cannot be trustworthy agents or subjects.
URS GASSER: Thank you.
Matthew?
MATTHEW LIAO: Yes, I'll take the question on hate speech.
So I mean, there are some attempts using machine learning
algorithms to detect things like fake news and things like that,
but I actually want to give you a very grim picture.
This is like election 2.0, since we're coming up
to another election cycle.
So there's some evidence.
There's something called deep fake, which
is being able to produce all these videos that just look
like they can superimpose your photo
or you onto another video.
And then they can get you to talk and do various things.
And what people are finding is that--
so there's the theory that you tend to vote
for people who look like you.
OK, and so now they're creating deep fake videos, where
they super impose your picture onto a candidate's picture,
so it looks like you.
And now supposedly that's going to influence your voting
behavior, because you're more likely to vote
for people who look like you.
And so that's going to be very worrying in the future.
And then the question is, we're going to get to a point
where it's going to be very hard for human eyes
to be able to detect those differences,
and that's going to be very worrying.
URS GASSER: Jeanette.
JEANETTE HOFMANN: I'd also like to pick up
the question on hate speech.
What I find really good about this question
is because we have so many examples
that show how deeply ambiguous we are about such wording.
Facebook once told me the example of the term "bitch."
"Bitch" can be really dismissive,
when you call a woman a bitch.
But nowadays, in some circles, "bitch"
can also be appreciative.
Women might refer to each other as bitches.
How is Facebook supposed to regulate--
[? AUDIENCE: ?] Especially on [? Halloween. ?]
JEANETTE HOFMANN: I mean, how is Facebook--
URS GASSER: I did not see that coming.
JEANETTE HOFMANN: --supposed to regulate wordings that
have so different meanings?
And that, I think, shows also the limit,
the limits of technical filtering of language.
Language is changing all the time,
and it differs across cultures also very much.
So there are really limits.
Another point, if I may, the question of empowering
versus disempowering.
I really like this question, because it implicitly refers
to autonomy of human beings.
I think it's a mistake to think autonomy needs to be
defended against technology.
Technology, in many ways, enhances our autonomy.
Think of flying around.
Think of your watch.
I mean, we coordinate as societies
through these technologies.
And at the same time, they are disciplining ourselves.
So it's not an either/or.
And technologies and human beings are not opposites.
It's the matter of how we structure and shape
the relationship between the two.
URS GASSER: Dean Nobles, is it OK if we go last with you?
Mr. President, Dean Nobles.
MELISSA NOBLES: Sure, I'll just take the question
of the computer scientist who said
these kind of conversations are also happening
among computer scientists.
But we need to get better connection with others, who are
thinking critically about it.
I think that obviously education plays a hugely important role
in this.
That is, early on getting students
of different disciplines to work together
and to learn together in a way that addresses
these questions exactly.
The challenge of all knowledge is making sure
that it doesn't stay siloed, that we work
in a truly collaborative way.
And it seems to me that's the challenge for the 21st century.
WOLFGANG SCHULZ: Yeah, I think I'll
pick the comment, if I may, on the funding
and interdisciplinary research.
I think we talk a lot about interdisciplinary research,
and we need it to solve problems,
but the academic system is not really
designed to cater that need.
We still have problems with that,
and I constantly get phone calls from colleagues
that want to apply for project funding next week.
And they said, oh, we have just seen we need some ethics in it,
and we need a lawyer or something like that.
Would you be available?
And normally I'd say no, because it
has to be part of the project question
and not just an icing on the cake that
has already been baked.
That makes no sense.
And so I think we have issues here in the academic system.
And maybe 30 seconds on the transatlantic issue.
I think it's really helpful and good for these questions
that there are really stable research
relationships between our American colleagues
and the research in Germany.
It's really great, and that survives
even if there is a political winter or autumn,
that we have this relationship.
And to solve these kinds of problems,
I think that's extremely helpful.
CRYSTAL YANG: Yeah, I'll just follow up also
on the research question to the excellent question over here.
I failed to mention, I am actually an economist
as well as a lawyer.
And I would welcome many more economists studying this area,
and I hope that the funding structures as well
as the incentives do promote that greater collaboration.
I think the computer science community
is doing amazing work.
It's often siloed from what the economics community
is thinking about, what the legal community
is thinking about.
And so I think initiatives like what Dean Nobles is doing
is probably a really great way of bringing people together.
And to education, there is such a need
for infusing this type of learning in legal systems.
And I don't think that US law schools, at least,
have really been at the forefront of this.
In fact, many of the decisions you read from state
supreme court judges who are ruling on the use of algorithms
and making important case law have explicit acknowledgments.
I'm paraphrasing here, but I'm not paraphrasing so far of,
the judges in this case were limited in their decision
making, because they didn't understand
how the algorithm worked.
Well, that's a really big problem.
And so we need to train the lawyers who
will be deciding these cases, working on behalf of clients
who are both creators of algorithms
and individuals adversely affected
by algorithms to understand how algorithms work.
URS GASSER: [SPEAKING GERMAN]
INTERPRETER: Mr. President, you have the final word.
Thank you.
Thank you, indeed.
I'm not attempting, even attempting
to respond to all the questions that were put to us,
but allow me begin by the following remark.
The debate that we have just been witnessing
with the participation of the audience
would undoubtedly be easier in the future
if we were to keep it clear from any misunderstandings.
If I may come back to the question
that you put at the beginning, why
is there no economist amongst people here?
You know the economic potential of IT
and artificial intelligence is being seen sufficiently,
I believe.
So if you take a look at the expert,
the group of experts brought together here,
you will undoubtedly find, confirm
that everyone here is aware of the economic potential.
Everyone is aware of the technological potential.
Everyone is aware of the potential that
exists when it comes to fighting poverty, fighting
disease, fighting the impact of climate change.
If we want to be successful in those areas,
we need experts at the top level.
And we in Germany intend to participate in that development
just as much as you do.
But that is a kind of advance remark.
I want to be very clear, having said
what I've said doesn't mean that we end up
in an age of unbridled regulation, a crazy approach
towards--
a craze about regulation.
When you look at the field of tension
between new technologies and AI on the one hand
and what is the constituent element of our societies,
and that is democratic decision making
processes in Western societies.
Their is a field of tension.
Shouldn't we make that also a topic of the discussion
every once in a while?
And that is why I suggested to make that the topic
of our discussion today.
So no one should assume or be afraid that this inherently
entails a secret wish to in some way influence the development
or to slow down the developments in the field of AI
and technologies of digitization.
But that's not my intention, really,
but there is this field of tension I mentioned,
and we have to focus on it.
We have to deal with it, and this
is equally true for all those who
participate in the process of technological development
of these means of communication.
This should not be left to philosophers
or individual groups.
It has to be viewed as a topic for all of us.
And if we pursue such an approach, we will, I believe,
reach a point, and that has become obvious here
as a consequence of the discussion,
where we don't leave it to appealing
to the morals and each of every individual
and his or her responsibility.
But we need to have a debate across borders, whether there
should be limits to technological process
that we should not surpass, because this,
at the end of the day, is what it is all about.
It's difficult enough when you look at Germany and the United
States of America, but it will become even more difficult
when you think about those countries that
have a completely different social system or approach.
But we need to have that debate.
We need to have it with a country like China.
And in saying that, I'm not cherishing any illusion
about us having in 10 or five years a kind of UN charter
on artificial intelligence.
We won't get that.
But nevertheless, we should engage
in that kind of a debate, just as much
as we have a debate with China although we
have different views on the issues of bioethics
and genetic engineering.
We are not in agreement on these issues,
but nevertheless we have succeeded
in defining some limits or ceilings or restrictions.
Thus I am not discouraged, you know, in any way,
when I look to the possibility of such a debate,
although it's going to be a complicated one.
But you know, this was mentioned.
What we need is a transatlantic debate on the subject matter,
too.
Apart from all the topics of the day, the conflicts of the day,
and I don't want to downplay their importance,
but we have to tackle the question of the importance
of the freedom of the individual,
of the democratic culture in the states of the Western world.
And we count ourselves amongst those, just as well as
the United States of America, and we
need to have that debate amongst the West first and foremost.
This is what I would wish, us to have the opportunity, time
and again, as I have been trying to seek it during my visit
here to engage in discussions and debates that do not solely
involve focus on the present conflicts, trade
conflicts being just one case in point,
but to have a transatlantic dialogue about the issues
that are really at the essence of what links us and affects us
in the years to come and will be affecting us in the future too.
I very much look forward to my next visit
to Boston and to Harvard, and thank you for having come here.
[APPLAUSE]