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OK, welcome, everybody,
to our next talk,
which is called Ethics in a Data Society,
and our next speaker
here, Maya Garnette.
She is director of applied research
at Tactical Tech Collective.
You might know that as she calls herself
a research practitioner, writer and
activist.
And she has a master's degree in media
and culture studies, but also in applied
technology was a technology
psychology sorry,
which is an interesting mix, I think.
And her issues are the issues she's
working on technology and activism,
human rights, defense with a focus on
gender and technology.
But today, because
Myers, also a doctoral candidate
at Loyola University, which is not far
away from here at Loon Brook
University, she
has her doctoral project,
Project on Ethics and the context of
driverless cars, which is
the topic of our talk today.
And in her lecture, Maya will
discuss discuss her ongoing research
on the emergence of this new technology
and how it shapes our understanding
and the meaning of ethics in a data
society.
Give a warm welcome to Maya and
enjoy the talk.
Thank you. Thanks a lot.
It's then thank you very much to 33 C
three for giving me this opportunity
to talk about my work.
I've actually talked about this
topic a couple of times this past year.
However, this is a brand new talk because
everything is recorded these days, so you
can't recycle at all.
However, it's weird
to actually feel slightly nervous, even
though it's something that you know very
well. And, you know, like they say,
nobody knows your Ph.D.
topic like you do.
Maybe that's because of all of the
numbers of really wonderful people who I
know and love and respect in the
audience. Maybe a little bit of
nervousness coming from there.
It's always hard to talk to people who
you think are smarter than you
anyway. So so that's
why it's a it's a nice way to kind
of title my talk.
Also change the title entanglements.
It's not ethics in the data society.
I just did that so that 32 C three would
accept the talk.
This was always supposed to be the title.
So when I
started thinking about ethics,
I actually started in a very different
place. I wasn't really that interested in
driverless cars.
I was interested in this thing that I
discovered called an ethical app, which
is actually a shopping app about making
the right kinds of consumer choices.
And it struck me as very odd that
people thought that ethics could come out
of an app.
And I'm actually not a big like
I'm not really into ethics.
If I can get through the next four years
without reading Aristotle, I'd be really
happy.
But I was curious about what it is
about technology that makes people feel
like it will give us answers
to these really complex social and
political situations problems
that we face. And that's where I come
from in trying to sort of understand the
workings of society and technology.
So when we are talking about ethics,
what are we really talking about?
That's the first thing I kind of had to
engage with, that we're actually having
a discussion about a technology that's
emerging in society.
And driverless
cars are kind of at the peak of what
Gartner calls the hype cycle.
So every day there's a story
about driverless cars.
A lot of this technology, which I believe
is not very robust, not very well tested,
is actually being developed in the open.
So it's very difficult for the public to
also develop a sense of confidence
in what this technology means.
And things like cars are
so banal and so mundane, but they are
very intimate machines and
we have a long history of relating to
them. So when we talk about ethics, what
are we really talking about?
And I go through some of these things in
the course of my talk today, I will also
skip over some things in the interest of
time. But I think that there's space for
questions as well at the end.
So at one level, we're talking about
things that can be programed into
software, moral decisions
that will result in certain outcomes
that we Tomaz, ethics when we
say ethics and driverless cars, ethics
and autonomous technologies more broadly,
we're also thinking about who's
responsible when something goes wrong,
but also who is going to pay when
something goes wrong. And I think the
financialization of risk and the rise
of the insurance industry is something
I'm personally quite interested in.
And I'll talk a little bit about that.
But the most interesting thing that I
think I discovered
in the course of doing this work is my
own interest in science fiction and
how we relate to machines.
And I think that a lot of our
understanding around new technologies
today has to do with the fact that,
you know, we grew up watching things like
Star Wars or we watched Ex Machina
or, you know, it's cinema and literature
and fantasy are such a big part of our
relationship with the technologies that
we create and technologies that we
inhabit.
So I'm also going to say, well, I'm not
going to talk about that so much in this
talk, but maybe maybe in some other.
So we're seeing the combination of all
of these multiple conversations going on
at the same time,
and I'll start by talking about ethics
as something that we think can be
programed.
And there's some really interesting
new projects that kind of think that, you
know, ethics can be an outcome of a
software program.
I usually start by talking about this
thing called the trolley problem, which
has become very popular recently thanks
to Google's self-driving car project.
Google recently renamed it
self-driving project, car driving,
self-driving car project Wimoweh.
And they popularized this thing called
the Trolley Problem, which comes from the
1960s, and a philosopher called Philip
Barfoot, who actually came up with the
trolley problem in order to think through
the permissibility of abortion.
But and so I usually kind of give this
big description of what the trolley
problem is. It is kind of jargon
heavy.
But a good thing is that a team out
of MIT developed something called the
Moral Machine Project, which is based
on this classical thought experiment
called the Trolley Project.
So talk about the modern machines
project, the ethics bots and
the application of another quite old 400
year old problem called the Pascaline
Wager. I will try not to make this.
I will try to make this funny and not too
complicated. I mean, like even I don't
get it sometimes.
So modern machines.
So you can look at this modern machine at
MIT dot edu.
It's based on the trolley problem, like I
said. And the idea in the trolley problem
is a car is going down
a trolley, a train trolley is
going down a track, the brakes fail
and the trolley is not going to stop.
But there is a fork in the track
and the trolley has a lever.
So you can either make the trolley go
left or you can make the trolley go.
Right now, the thing is on the left,
there are five people working on the
track and on the right there's one
person. So the decision you have to make
is, is it better to kill five people or
better to kill one person?
This is absolutely impossible, as
you know, to resolve.
But this is what the trolley problem
does.
So you have to decide who you're going
to kill, but you also have to come up
with the rationale for how you're going
to kill them. So this is sort of
this classic tension between utilitarian
ethics and Kantian deontological
ethics, which simply means the outcome
matters. Does the outcome matter or
does how the rationale for why you
want to kill either one or five matter?
And again, I mean, this is impossible.
It's really difficult to do.
But the modern machine project does does
this. So they set up a scenario and these
are just like screen grabs from the
website, which you can look at.
So a car is going down, the brakes
fail and the people crossing
are in this scenario.
You have there's a thief, there's
an old lady, there's a baby in a pram,
there's a cat.
And you have to decide who
is going to be saved.
There are little skull and crossbones
signs to see who will be who will be
killed and who will not be killed in this
scenario. There are others as well.
In this one, it's like five dogs versus
five cats.
And.
This is another scenario, so this is part
of a research project that the scholars
at MIT are doing, so you browse through
13 scenarios and you decide who
you're going to sacrifice in this in this
hypothetical test.
And then you can go on to fill in
details, if you wish, about who
you are and your opinions, why you made
certain choices.
This is all in the interest of research.
You can also choose not to give your data
to MIT.
And I did the test.
I've actually done it a number of times.
And I have to say a couple of times I
just kind of randomly I mean,
whatever, I just kind of like hit any
option. So, you know, the test taker
wanted to know, like, why did I prefer,
you know, like younger people to older
people or thieves to like, you know,
athletes.
And this is how you kind of develop this
model scenario where you have to decide
who's more important.
And the idea is the driverless car
software has to make this choice, which,
you know, even the best of us cannot make
a choice.
I found this quote two days ago from one
of the French philosophers who behind
this project.
And he's actually kind of it's
interesting that he would say this, that
at this point we are looking into various
forms of shared control, which means
that any accident is going to have a
complicated story about the exact
sequence of decisions.
So it's not easy
to program these things even in this age
of machine learning.
I don't think it's entirely easy to
program these things. And I think the
develop of the test, I think so, too.
So the trolley problem kind of
pits consequentialist with deontological
ethics. Really, really difficult to
resolve why and how you should
kill X or Y people.
So other scholars have thought about
other approaches where you have
different choices you can make of these
terrible choices you have to make.
You make a list of them and you assign
numerical values to the outcomes.
And then a computer program will
will pass those outcomes and decide
quite randomly which one to
choose.
And this I've kind of said, does God
exist? Because the Pascaline wager that
this is based on actually comes from
an idea in the sixteen hundreds about
the existence of God.
And it's actually kind of interesting
because it's about how how would you feel
if you discovered that God did or did not
exist?
There are feelings you would have about
them. So the Pascaline wager is a way
to rank numerically
these different outcomes.
So Vikram Bhargava thinks that you could
apply the Pascaline approach to
these terrible outcomes in the case of a
car accident.
And then we have ethics board, which is a
newer idea by the Etzioni is
they looked at users
of Nest, the house, the thermostat
heat regulation system in the home, and
they said you could develop
A.I. assisted ethics so you could have
a meta bot to rule
the algorithms.
This meta, what would
this ethics board would identify?
Patterns in an individual
individual users nest use.
The assumption is if you're somebody
who cares about the environment, you are
more ethical and you will
you will not use so much heat in your
home. You will be very conscious about
the regulation of temperature
in your home and you will try to use less
energy.
This is seen as an assumption of what
ethics is,
which I actually just kind of started
laughing out loud when I saw this, not
only because of like a bot having to
now oversee algorithms, which is
kind of crazy, but that they say
we want this to be personalized.
We don't you know, we want individuals
to have control and individual moral
decision making to sort of come into this
framework.
But what if you're actually somebody who
you may sort of regulate the heat in your
home, but you may take like a hundred
flights a year or maybe
you're a menopausal woman who actually
just wants to regulate the heat in your
house because you can't deal with the hot
flashes. You know, it may have nothing to
do with how you actually feel about the
environment, but the attorneys think
that ethics boards
could be a way to kind of evolve
moral decision making or kind of find
a way for machines to learn about moral
decision making.
Now, why all of these are kind of
interesting and are of a set is
because we believe that ethics
is an outcome. We believe that ethics can
be something that software will
that that software can be programed to
do, can learn.
And I like kind of always trying to
introduce Don Oharu in any of my
talks because I think she's really
amazing and
and I have kind of some of my work kind
of tries to look at what does it mean
when we think that these wonderful
machines that we've created will have
the answers for us.
And James Bridle today talked a little
bit about things like machine vision and
computer vision.
What happens when we start creating
systems and environments
for machines to inhabit
and for machines to understand and for
machines to pass.
So I kind of ask myself that
it's not so much about ethics
alone. I mean, ethics are important for
sure.
But what ethics are is sort of
complicated at this point.
But what is the code and what does the
city become when it's made for a
driverless car?
And I came across this video,
which is by a design company called Big.
The name of the video is driverless is
more.
Barbara.
This.
Harry.
This.
So, of course, it's sort of interesting
that there's there are no humans in
any of these scenarios of
the future with driverless cars, and
there's this quote that I also really
like from a paper called Crap Hilarity
Hermeneutics by Florian Kramer,
where he thinks about what happens if we
actually start building systems for
for machines to inhabit.
And yeah, I mean, if
you can read the paper, please do.
It's great. But then this particular
quote is about trying to imagine what the
city looks like when it's made for cars.
And this is not actually such a
distant possibility, because
if you talk to people who are involved in
regulation and industry
and government, this is I
mean, driverless cars, no matter what
Gizmodo tells you, are not going to just
kind of show up on the street.
There's actually like a long process,
long painful process of technology
actually being integrated into society.
And you have to have a lot of law and
regulation as kind of part
of that. And it's really interesting that
way to look at the history of
electricity, which is
electricity and telephones and how it
completely disrupted society.
And I think the driverless cars are kind
of are going to
do similar kinds of things, maybe not so
visibly at first, but I think it's going
to kind of reshape a lot of our social
relations. I mean, you have to think
about, you know, old people
or seven year old children who now have
kind of independent lives and
mobility as well.
And I think the sort of update
on Turing's old question is
not, you know, what do we think of
machines that think, but what do we think
about machines that can learn?
And what we know about machines that
learn right now is that they don't learn
very well.
They learn like like toddlers.
And this is I mean, we've seen that,
you know, machines basically running
machine learning algorithms will replay
a lot of the biases that are embedded in
original data sets that they're that
they're learning from and that they're
based on.
And another great Donna, Haruhiko,
does that. She refers to A.I.
and primates and children as almost minds
to kind of refer to this way in which
that they learn.
So I think we're still very much in the
sort of early stages, but there's a lot
riding on the fact that someday
the software is going to do kind of
amazing things.
But before we get there, sort of the
other register in which we talk about
ethics is also about
about who's going to pay for it and who's
accountable to it.
And I think that the history of crash
testing, the car industry
and crash testing and the concurrent
rise of the insurance industry
is also really pertinent,
because I think that some of these shifts
are happening right now.
And I'm personally hoping to look at more
of this in the coming year or so.
In the 1920s and 30s, like there
are a lot of car crashes happened,
but people don't always know why.
And so car manufacturers would spend
a lot of effort kind of building cars
just to crash them.
And the insurance industry kind of grew
along with this and was able to learn how
to understand how crashes happen.
Now, this also allowed science and
technology to develop because people
understood a lot more about like physics
and the materials that they were using.
And so this was, of course, very
expensive. But in the 1990s, you saw
car manufacturers using
modeling and simulations to actually
feed in a lot of different
variables about crash scenarios
and to understand how crashes were
actually going to happen.
So scholars have actually written about
this idea of road to lab to map.
And so one of them, Nigel Gale, says that
road to lab to math is basically an idea
that you want to be as advanced on the
evolutionary scale of engineering as
possible. Math is the next logical
step in the process over testing on the
road and in the lab, math is much more
cost effective because you don't have to
build preproduction vehicles and then
waste them.
We've got to get in front of the
technology so it doesn't leave us behind.
We have to live and breathe math.
When we do that, we can pass the savings
on to the consumer.
So so, you know what's kind of already
in the works and already being
developed.
And so I'm sort of like quickly moving
on to the next part of what I think about
how we think about ethics or what are we
really talking about when we're talking
about ethics?
There is a lot of interest in how
technology is made now.
People are kind of looking at the man
behind the curtain. It is usually a man
behind the curtain who's actually making
the magic happens somewhere.
Where is the stuff being designed and
what are the sort of politics of
technology design?
On the one hand, of course, you have
things like Diesel Gate, which there have
been some talks here as well about Diesel
Gate, which are really interesting.
Volkswagen's very
blatant cheating on their emissions,
using the defeat device and telling
us about the fact that, you know,
corruption happens in business.
Now, you've got that scenario, but you've
also got a lot of other scenarios just
around the development of technology
itself, and
there are lots of good examples of how
technologies that we experience
today, especially on social media and
consumer devices, betray
the biases of their designers.
But I was really interested to learn just
last week that the Institute for
Electrical and Electronics Engineers I
tabouli came up with a really good
document around ethically aligned design.
It's a 138 page document
and only on page 36.
So if I'd read more, I would tell you a
lot more about it. But it just came out
on Monday.
But what is interesting to
kind of note in this is that there's
something much more serious going on
where there is, I think, a discussion
about looking at the fact that
we cannot embed values in software.
It is really hard to to
align human ethics with
machine programing.
And I think this is the first time I've
actually seen a document that
is so global in its scale and its scope
in the way that it's talking to many
different kinds of people, including
people that, you know, some of us in this
room know, like people like Neales from
Article 19. I mean, it's interesting that
they're actually trying to look at human
rights issues in
sort of what they're calling ethically
aligned design.
So I think that there is going to
be a lot more interest in looking at the
context of production.
As well.
And.
So we've got we've got this
idea that ethics is something that is an
outcome, that software will will
will tell us the answer of what is the
right thing to do.
We've got the idea that maybe ethics
is something that we have to think about
in terms of from the point of production
and who's actually making these things
and what are their intentions in
producing these things.
Or maybe ethics is reduced to a question
of the law and insurance.
But when I kind of try to think about it
for myself, like really, honestly, what's
going to happen and what's this sort of,
you know, great future?
I'm not futurologist at all.
But if you do have to have
some sense of how this is going to pan
out based on what you're already seeing,
I think that we're seeing
well, we're kind of moving towards this
place where we have to think about a
different relationship with some
of these machines that we're making and
we're going to have to be using soon.
And we already have levels of autonomy in
cars like cruise control and
parking.
And the history of cars will, you
know, sort of driving will tell you that
and crashes tells you that machines
are very, very accurate and they are
fantastic at performing certain
computational tasks.
But they are really crap at doing a lot
of things that humans are very good at
in the testing of Google's
self-driving cars, Google cars,
and were constantly being rebranded or
getting into accidents because they were
programed to be to follow the rules.
But as anybody who kind of drives
a car or if you're a taxi
driver in Berlin, perhaps maybe not a
regular German driver, because I think
German drivers really follow the rules.
But where I come from, I mean, like,
you know, if it's an open road,
if it's late at night, you're going to
speed, you're going to, you know, bend
the rules a little bit. And actually, it
seems like human beings do this
and human beings also
do believe robots.
They do believe cars.
So it's actually kind of bizarre that
companies like Volvo would be concerned
about, you know, humans pulling their
poor little autonomous vehicles.
And the history and the story behind
the Tesla car crash, which happened in
May, is about
auto pilot and how
this idea of autopilot, anybody goes up
in a plane is that, oh, you know, the
plane just drives itself under
autopilot. But it's quite different with
driverless cars because what happened was
the test driver
was supposedly watching a Harry
Potter DVD and put the Tesla into
autopilot mode and the car
could not distinguish between
the sky. It was a bright day, the bright
white sky and the
the white side of this one of those big,
long trailer trucks, it just
couldn't tell, which tells you something
about the computer vision in Mobileye,
which is in Tesla. But anyway, so
the car went into the big trailer truck
because it thought it was just the sky.
And so that kind of
made, you know, sort of brought this idea
of autopilot on out in
a big way. And Tesla's kind of going to a
lot of pains to say that, you know,
autopilot does not mean your hands are
off the wheel. The human has to be in the
loop constantly.
Even the German government
recently just told Tesla that if they
were when they bring their cars into
this country, they have to change the
world. They cannot use the word autopilot
because it is too misleading.
But I think this kind of thinking about
autopilot and
what is the kind of relationship with
this sort of handshake that we have with
machines is is something
that's going to have to be explored more
and more. It's not just about regulation
and lanes and zoning for the car to
actually be on the road, but maybe other
kinds of licensing, maybe other kinds of
personal insurance as well if
you have to actually inhabit a space with
with machines like this quite closely.
So I was actually maybe I have time to
talk about the ethical fund, which is
sort of sideways, but it's this it's
this kind of speculative project by these
to Shanghai based French artists.
And it's it's trying to kind
of like, you know, poke fun at the idea
that the machine will have the idea.
But it's also about design.
And can you design ethics into machines
so they have this little just a fan
and you can have settings on the fan
of people.
So two people come and sit in front of
the fan and the fan has to make a
decision about which way it will turn
and who it will fan.
So you can put in values about the people
that, you know, so-and-so is a Christian
or a Muslim or a Buddhist.
So and so has this kind of education.
This is how tall they are, how fat they
are, how thin they are.
And then these variables are sent to
mechanical tokers Mechanical Turk workers
in Dubai or Capetown or Washington,
D.C. And then these humans make a
decision about which way the fan could
turn, not being in that context at
all or seeing anything about those
people.
But this is I mean, I would like
to think that we have this moment of
love in Greece with machines where we
work together really smoothly.
But I think before that happens, there's
actually going to be a lot of like really
rubbish things like this where, you know,
Mechanical Turk goes halfway around the
world will make a decision about
what a machine should do.
And so I'm kind of coming to the end of
this and I'm thinking
about other sorts of approaches to ethics
as well, that it's not something
that software can tell you the answer to.
It's not something that insurance or
even just kind of, you know, car
manufacturers will come up with.
But that ethics gets produced
contextually in different situations
to a number of different actors
and factors that are human and non-human.
And McNerney has this great quote about
it. And I love the fact that he calls it
it is not a test to be passed or a
culture to be interrogated, but a complex
social and cultural achievement,
which is kind of music to like, you know,
a science and technology study scholar
like myself. But it's just like it's
really difficult if you have to think
about how does this happen practically
how does. And so I'm
kind of interested in seeing can you
actually have used design fiction as
a methodology to get people to think
through how this will happen?
So I recently had an opportunity to work
with some programmers and designers
working with a company that I called
Bellino Tech.
They said that I had to sign an NDA after
the workshop, but the paperwork
was a bit slow and they forgot to give me
the NDA to sign so I could tell you who
they are. But I don't want to because
they're really nice people.
I shouldn't tell you who they are.
So I got to work with this group of
people and put them through a series of
exercises and they work
on building technology for autonomous
vehicles or kind of
so this is very much in their world.
And I didn't realize. But these are
questions they have to think about every
day that what if you had to
build a map earlier for parents
and children to share information about
children's independent journeys in
autonomous driverless cars or
the second situation, which is actually
kind of happening, that whatever route
finding app wants to avoid
high crime neighborhoods.
But the databases for high crime
neighborhoods are already so
racially loaded and come from such sort
of suspect sources that it's going to
the car is going to maneuver itself away
from.
You know, we know what high crime
neighborhoods are.
So, I mean, I'm
still in the process of actually writing
up the notes and thinking about what they
talked about.
But what I can say is that I was so
struck by how
thoughtful people were, and these are all
programmers who work in big technology
companies or kind of allied with car
companies. And people
want to find ways to be political about
these things. Engineers and technologists
and scientists want to find ways to
actually say, no, this is terrible.
I see what the implications are of what
I'm doing and I want to find a way to
push back. But I am part of a much bigger
corporate machine. At the end of the day,
I'm paid money to build this technology.
So I think that it's I mean,
I would like to do much more of these
kinds of workshops and using design
fiction as an idea.
And as a methodology and process, excuse
me, and I'm going to end
there because I'm out of time and I
really need to drink some water.
Thank you.
Thanks a lot, Maya, so take
your time to drink something.
You have time for, I think, one or two
questions.
OK, perfect.
And we also have a signal angel in the
room. Who is it? Please raise your hand.
I would say OK.
Yes, please.
Is Kristin OK?
So my question is a bit general.
But when it comes to responsibility
in developing systems that are
very critical
on ethics, for example,
a crime rating, algorithms
that that convicted people
and stuff like that,
we have messed up a lot there.
This is just one instance.
But just assume some ethically relevant
program or algorithm.
Anybody that is involved in developing
that algorithm or developing the
technology has some stake
in responsibility.
What would be your answer to
explain this kind of responsibility
and how to untangle
the other responsibility that goes into
this process?
The short answer is I don't
know.
But I think that and I don't think
anybody knows. I mean, you know,
anybody who kind of works on these kinds
of projects or any kind of projects knows
that there's millions of lines of code
in these things. There's millions of, you
know, discussions about how
a project should evolve.
I mean, like I've been reading a lot
about, like the history of terrible
incidents and crashes, like the
Challenger space shuttle crash in 1986.
And that was something that happened in
one of the best resourced
NASA in, you know, at a time
when there was a lot of money given to
the space race
that happened.
That crash happened because
of bureaucracy and tensions between
bureaucrats and scientists about
the offerings on the side of the space
shuttle, which they knew would break
under certain climatic conditions.
So there's a whole book just called
the not about this thing called the
Normalization of Deviance, that
basically it's very hard to identify
who's responsible, but you have to find a
way to do it. And you can't say that or
this one program or did this thing.
So that's why this idea that technology
ethics have to be thought of as something
that are kind of produced contextually
and you have to actually start kind of
laying out and maybe waiting
and reading.
So in a way, this kind of modeling
approach is inevitable at one level.
So I think that people have to just
try something else apart aside from
just saying, oh, ethics is an answer,
it's killed this person or that person.
It's not ethics.
If you think of it as a framework for
living well and dying well, it's not
about killing well, which is actually
what you see with drone warfare.
And there's a term for this Niekro
ethics, Gregoire washerman who talks
about this, that it's
it's really complex.
I don't know the answer, but I think we
should try to figure it out.
Yes, we have time for one
last question. I'm sorry, but, you
know, we can talk after if that's me,
then, yeah, I.
So my question is.
I believe that this might be the first
time that we have forced to agree on
on a common set of ethics,
I think that is one of the big topics
that that somehow the car manufacturers
are trying to avoid, because that would
mean that they decide the
set of ethics that everybody has to
subscribe to because we are going to
forge this into software and copy
it all over the place.
Is that studied as well or I mean,
I did not see this mentioned in your
talk.
That's that's kind of a problem.
I think that.
Actually, car manufacturers and I know
one of them I'm kind of talking to, I had
some interactions with one of them, they
will be very cagey and they will tell
you about the trolley problem.
Again, that ethics comes down to how
to program the software to act in the
case of an accident, because I don't
think they know and I think we've had
in the last 50, 60 years like incredible
movements and scholarship, saying
that these kind of very hetero
patriarchal notions of ethics, that it's
about logic and rationality,
these come from a certain kind of place.
Why are we reproducing and replaying
these things that are there incredible
kinds of technology, ethics, anticipatory
ethics, computer ethics, robot ethics
that are all being developed as well.
So it's very curious that they go back
and pick up this very kind of
antiquated idea of ethics.
I think this is what they believe.
But and I have a feeling that's what
they're going to actually test and
program. But they are also nervous.
I mean, I read some of the business news
and they're kind of cagey about
it. I mean, my my understanding is that
they're cagey about it, but I have a
feeling they're going to do something
like this anyway, because the other way
is to hide.
If you actually try to
be more contextual and
process oriented, I don't know.
I'd love to actually work with the
computer scientist, a programmer, and
actually trying to develop a program to
say, could you actually map
just out of for speculation sake
if you know all of the different actors
and points at which you would see
responsibility and accountability?
I don't know.
Thanks.
Yes, sir. Thanks again, Maya.
The machines just told us to stop.
I'm sorry.
So thanks again, Maya.
And let's give her a warm round of glass.
And thanks for the glass
today.