I'm going to talk about the moral machine experiment, the big project.
It's a project that started in 2016. And we also talk about some other projects as well.
Hopefully if time allows.
Please do feel free to interrupt me if you want, especially if you have clarifying questions that will help you to understand what's going after.
And if you want to have more critical questions, that's also always welcome.
What if you want to keep it to the end? That's awesome. I'll try to make sure that there is enough time at the end for wishes.
Right. So it's probably, you already now know driverless cars are being tested on the roads in different places in the world.
And they still have a lot of technical issues, of course.
But let's say in the future, once they tackle all of these technical and social issues, maybe they will be become a reality.
And some people have made some calculations saying that once they have been perfected in terms of safety,
they will be able to minimise 90% of the current crashes by humans.
Now, regardless of if it's accurate or miscalculated, but let's say it is 90%, the remaining 10%,
and some of these 10% might be good cases where the cars are basically resolving more of the trade offs.
Now, a very stylised, abstracted version of this one on the tables is inspired by what most people here know as the trolley problem.
But in the domain of driverless cars, this is the simplest version of it drivers.
The car is on the road. Ten pedestrians jumping from a bridge
One of its brake fails. The car is about to kill them. The only way out is if it swerves into this passerby and sacrifice the passerby.
Should the car do that or should it just continue to kill the ten people?
In another version, the car swerves into a barrier, sacrificing its own passenger.
And should the car do that?
Of course, this was basically a study where initially started, was published in 2016 by my co-authors and my former advisor,
and they found that most people actually approve of utilitarian driverless cars.
They think it is fine for cars to sacrifice their own passengers to save ten pedestrians.
But there was a catch, of course, because when they ask people if they're willing to buy SUVs such as this,
sacrificing cars, people say they're not willing to do that.
Now, many people have, of course, presented this work for a while, and a lot of people have said, look,
driverless cars will never face these trolley problem dilemmas, which is could be true, could be maybe they're going to be very rare situations.
But, you know,
driverless cars would probably face more trade offs and we actually do face more of those trade offs in our daily lives and will resolve them on a daily basis.
Let's take this this case, which is less stylised.
Then imagine you're driving the car in the middle of the white car in the middle, and you have this big truck on your right.
This is a cyclist on your left. Now, maybe you might be inclined to drive closer to the cyclist,
because maybe you're conscious about being struck and you're trying to protect yourself.
In this case, you are pushing more risk towards the cyclist.
Or maybe you're very conscious of what the cyclists are closer to the vehicle
and shifting more risk towards your self right now while doing these things.
Maybe if I asked you in a survey online, you would all say, Well,
of course I'm going to go closer to for the big truck because I'm such a nice person.
I'm an activist. I would sacrifice myself to help everyone else.
But then in reality, you would do something different. Right now, let's take you out of this and put that driverless car extender right.
For driverless cars. You're going to be implemented these decisions ahead of time.
Right. And actually, you probably would not be specific.
You say in this scenario, I would do this or would do that,
but basically the car would make calculation based on other data and some optimisation and whatever.
And maybe these techniques will become as a side effect of the car, minimising the company's liability or maximising user convenience.
But in a way or another, imagine now you have this driverless cars who have all of this implemented ahead of time.
So we don't really have to you know, there's no spur of the moment decisions.
And also these decisions are implemented in hundreds of thousands of cars because the same algorithm is doing the same thing.
Now, imagine you have two companies, one of them because their cars cost closer to the the big truck.
In this case, other one goes closer to the cyclist.
And let's say after driving hundreds of thousands of miles or millions of miles, they say the one that is driving closer to the truck,
maybe the risk materialises in the death of five people waiting for the other one, the risk with your license in sacrificing one cyclist.
But then we kind of get back into the trolley problem, but at a statistical level, right.
So in a way, this is not really this whole discussion is not really about the trolley dilemma.
It's all your problem and what's the car will do when it's going to be sick of you killing?
Actually, actively. Right. It's more about how these machines will actually change the distribution of risk and everything.
This new diet and it's got mirrors.
Also all the other concerns about HIV in general that how would we change the distribution of different values and distribution of wealth,
distribution of of, you know, the fairness, all of these things that are increasing inequality.
How would machines change that calculation? Right. So I hope that's kind of clear.
So we wanted to understand what regular people think about this,
especially that it is important because the emotional salience of of people when there's always a crash in the news is always quite big.
So we've got this website called War Machine and website was deployed in 2016.
Basically, it generates the moral dilemmas that are faced by Barbara Starr.
In this case, for example, the car is has one passenger and it's headed for the road.
And these four pedestrians jump in front of its brake.
Feels like in every story the car is about to kill them.
And the only way out if it swerves into this barrier. Which means what?
Sacrifice this female athlete inside the car?
What if it does not? It will do this for pedestrians are, by the way, or jaywalking, including the dog, which you do.
And then you would basically as a user who works like you would say, oh, I think the car should do this or do that.
And then you click and then you get the next scenario. You get the Kenobi scenarios.
And at the end, just as a gamification value, we tell people to give you feedback, saying some summary of that.
Okay, so this is how you answered your question. You know, this is how you compare to other people who've answered this.
Now, these are not very scientific, but at least, you know, people engage.
They basically encourage them to be more truthful about what they would do.
In building this website, we try to include multiple different characters and this website.
This has to be you know, this has two values. One of them is what is more engagement, but it's also increased the external validity of our findings.
And we will see that later on that, you know, this whatever we've tried to see, like whether it's like we're trying to study gender or age,
like it's not studied by specific kind of characters, but more different kind of different kind of character.
So you see here like adults and kids and seniors and you know, you see like athletes and the criminal and a baby and a girl going to have.
And then in building this scene, we kind of created four different components.
One of them is whether the car should stay or swerve just to do the present omission versus commission.
Also, the relationship of those potential casualties with the car.
So they could be either pedestrians or passenger. And if if they'll be distance, whether they're crossing legally or illegally.
And also some features about those, let's say, passengers or pedestrians.
In this case, we're putting one group as being older than the other group.
Okay. So basically we had multiple factors and this is basically a multifactorial design where we
include multiple attributes and we value them on top of each other with some restrictions.
And then we try to understand the effect of each of these attributes on people's assets.
So the first one, as I mentioned, was interventionism. And then we have this, which you also mentioned and legality and then all of these factors.
Now these like those we chose them because we knew from the literature that they do make a difference on people's decisions,
on the regular people, not because we think that they should.
So we took these factors together and then we put this website up and then we just we Tanzania works out to ten languages.
What happened that suddenly the website was to our surprise, that got a lot of attention.
So we realised we want to from different places in the world.
So we wanted to make sure we also reach, try to reach more audience and as well and, and reach more representative samples.
Very painful does you know because I think a website to try to 4 million users came to the to the
website during the first 18 months they contribute to 40 million decisions during those 18 month.
And there was a optional survey at the end.
So we had like almost like a half a million people who filled this survey, which has lots of people benefits.
Now, of course, it looks like it's still running, so we're still collecting data.
And this 40 millions has become, I think, 100 million.
So we're not doing anything with the data yet. But potentially later on, this is basically a map showing 1.41 location from each one user.
At least one person can. Of course it is representative in some places, but like in other places, like in Africa,
we have a lot of people just for comparison, you can see all the map of the whether it's light or electricity.
So you can see that places where they have access to electricity, somehow the website somehow reached someone.
Someone there. And the results of this all of this were basically published in this Nature Journal paper,
which I'm going to show you now about the results right now.
Right. So in order to understand the results, the x axis is basically a difference between two probabilities,
and these are the probability of choosing the right hand side minus what we get using the left hand side.
So in this case, I'm going to think all this in order that have humans on one of the sides are going to see how often people choose to save them.
And of course, that probability of spanning the humans available the same thing with pets, and then also flagged these two possibilities.
And then I get this number, which is because it's. Then you could do the same thing with a number of lives.
So again, you could do the same thing. You think, Oh, this is a tough one, but have to be.
And in this case, the one here means you added one more character.
Four means you've added four more. So this is a case of moving from 1 to 5.
As you can see here, as you know, as you add more layers, the the the the preference increases and the average of that somehow coincides with two.
You could do the same for all of other factors, and you could also compare the different factors.
So as you can see, we talked about spanning humans, but that seemed to be the most consistently separate cases come after spanning more lives.
Or if you are doing this and then spreading the young over the elderly, which also, some people would say, another form of sectarianism.
And then after these three, which are quite stronger than the others come these two others,
which is the role over the unlawful and higher status of North status,
and then up to preference for inaction over action, which is the Commission versus commission.
All right. So we have these nine different numbers which we can see at the global level.
Now, spanning the arc over there, there was quite a curious thing because it's probably among these three is probably the most controversial,
even though that perhaps many of you probably know that in the medical domain,
there are situations where it is justified to use age as a factor in prioritisation.
But at the same time, we're curious to know, like whether this is something influenced by some specific characters.
So we could actually do the same analysis at the level of each of our characters.
Remember, we have 20 different characters. So similarly, the x axis here, I'm going to stake all the scenarios,
the type of baby stroller, one baby stroller, and I'll see how often people spend them.
And then I would do the same thing with the adult male. I go see that, right?
I get another property and then subtract these two probabilities and I would get this x axis
and see baby stroller is much more prepared to be spared than the adult male and female.
And then you do the same thing for all other characters.
And then you can see that some of them are on the positive side, which means they are spared more than the adults.
They are located in female and others on the negative sides, meaning they are.
Now, if you look at the top four, you can see that they're all involved, young characters.
So it seems like and this is like this is kind of the preference for age is somehow something that's quite consistent.
We can see it across the different factors now also the same thing as well.
If you look at the world, I mean you can see the go get a card and the criminal that comes in between.
Curiously after that, you can see that also Italy comes also back.
So it seems like people do feel very strongly about the age as a factor, as a, you know, to to have a perfect moment.
As I mentioned, the people came from different places in the world, so we could basically do the same analysis at the level of each country.
We had people coming from over 200 countries, but for this analysis, we took the companies that got at least 100, 100 participants.
And basically, we calculated the same nine numbers for each country, and then we can calculate.
So you have a vector of nine dimension, a vector, and then you could calculate the distance between every two countries.
And then you could use that to, to do like using a hurricane clustering, which can be visualised in this database.
It shows multiple levels. So you can see for each country what is the closest country to their answers.
But also, you can see broadly how countries congregates.
So you can see if you can see the countries. But basically you have three coastal branches.
There's the red one. Western. And then the new one, the eastern.
And then the yellow one, which is because the eastern and then the colour of the country names the they're
using some cultural amount that builds up to a few policies have came up with.
So it's not our own but some of these names might might sound like seems like a weird classification that some countries are English speaking,
others are abortion or Catholic majority or Baltic or whatever.
So you can see that this example, like Latin America, these are all the ones that the Isle of Man's, that Myanmar.
That's bizarre. So it won't always be exactly one.
Yeah. So we focus on something else. It must be a small, mostly small sample issue.
That's true. That's true. That's very very much I thing unless.
So these are these these hurricane clustering things is in places which are near on the circle actually near to each other.
So just near in the sea. Yes. So nobody but the tree if you look at you can Australia that that little.
I didn't see that that that was that has branched right out there it's very new it's very near.
Yes true so that's very yes that's true.
So I mean, of course, this method is is a little bit it's very good sensitivity to some actually some small changes.
But I guess and you could do statistical this to show that it's better than random at least some but you know you very spot visual but you could see,
for example, that these countries that are, you know, English speaking, they kind of congregate together.
And I'll talk about this three clusters now. So the first one, as I said before, the Western, because it has many Western countries, of course,
the some exceptions you can see in your speaking countries, you can see protesters Catholic and also those countries.
The second one was called. Eastern Islamic Countries Conversion.
South Asian countries. And then the last one was a bit weird one for the sovereign.
But basically you have to subtract is one of them has white cube like an American with some exception
there and clear like some kind of by the former French colonies somehow fitting together this.
Now what you could do there, sir? What you could do is try to see how each cluster chain is different from the other two.
So this is now just really to make comparisons between the clusters.
One note to keep here is that for all of these nine differences that I showed you, the direction is always is always.
Most of the time is the same across countries, but the magnitude is different.
Calculating this is a z-score so that equals zero means average.
Western doesn't have much interesting stuff because it has most countries, so it's supposed to average everything.
But you can see here, for example, that in the Eastern societies, or like at least two cultures, you can see that, for example,
Spain, the local is a bit or is a little story that average Spain pedestrians are a bit stronger, sparing the spacing.
The younger is very, very weak. Right and they do still spare the younger over the elderly.
But at the same time, this difference is much less pronounced in those countries, which is also consistent with anthropological studies,
that they are more have more spirituality, they have a more respect to the elderly.
They have a stronger family ties. And then this other one they've also got spitting females is much stronger, for example,
which is apparently something given by France and all the countries that were influenced by there,
which is, you know, stronger than other European countries, for example.
Much more probably insightful, is trying to get at exploring some of the association and this organisation at the county level.
One of the, let's say anthropological or cultural measures is individualism, that some countries are very individualistic.
People focus on the individuals. They see themselves as independent.
Those countries usually have a strong institutions.
Why? On the other extreme, we have collectivist countries where people have a stronger family ties.
They have their loyalty is to their extended families rather than to the state.
And so so individualist countries, they usually like more like egalitarian.
So everyone is equally to the same. And so having more saving more lives is always better than saving fewer lives.
What would be collectivist society that all other factors that might play another?
Another also factor or index is called rule of law, which is how much respected the law in a country as well.
We can see here that spending that often is correlated with it as well.
Other countries like Japan or Germany all have a very strong rule of law.
And in this country, it seems like I mean, again, spending to local was always preferred in most countries,
but this difference becomes bigger than it was once.
We're going to talk more about the implications of what I just mentioned.
I just want to make sure that we also make it clear that, you know, many of you, of course,
know that this distinction between normative ethics and descriptive ethics and this work that goes into these more like descriptive ethics.
Normative ethics is what what we should do. This is what the general public think we should do or what we actually do, what our culture begins to do.
Norms tell us to do. So this was more like a descriptive trying to get at what people are usually or think they get people to do.
But there have been some efforts on ethics, a lot of efforts, but that usually, I think at the level of countries,
Germany was the first countries that actually they brought in a committee and they brought these committee representatives from different domains.
No ethics, engineering and a Catholic bishop as well.
So these committee, they sat together for a few months and, you know, and eventually they came up with some set of rules.
Of course, there's you know, from hearing from one of the members of the committee who said, like, there was a lot of fighting,
of course, and people there were things where people did not want to sign their names off until they were being resolved.
So at the end of it, there were some contradicting statements because everyone wanted to be, you know, represented in the statements.
But what I'm going to do is just know I'll just list the top five differences that we found in people.
And I would just just make a connection to what the committee have actually said.
The first thing I said is human overheads.
And there was a line in that report that say it's fine if we give priority for humans over space or objects,
if that's the only way out to save that human. The second factor is more of a future.
This is where the committee we're a little bit conflicted because some people say it's fine,
but it's fine if you if you know, if you do programming to reduce the number of lives lost.
But but there was another sentence that I also add saying, but you should not be comparing lives and sacrificing lives for other lives.
So in a way, just to appease both parts of the different parts of the committee, younger, over elderly.
This was curiously in complete disagreement with the committee who said this is unacceptable and over age is, you know,
all over are making in a way they're completely in disagreement because they had some some statements saying,
well, if the whole of remark came about because somebody was not following the rules,
then the people who are following the law should not be that right now.
Notice how this sentence is being put in a way.
So does that mean the court should punish the people whom, you know, who jokes?
That does sound a bit harsh. So, no, we don't say that.
But, you know, so anyway, again, like it's not very controversial, but in a way you could you can think about it that not completely disagreement.
And of course, this last one is clearly in disagreement.
Now, to come to the second part of the of the implication, I guess I mentioned about the the level of the countries.
And we found that, for example, some countries are more in favour of sacrificing the G.
G workers than others. So let's say we have, for example, some driverless cars in some countries and they have some level of it's up to 21%.
It's better than regular cars. So that's why it's enough to have them on the road.
And let's say somebody came up with some update and this update will result in better or more safer driverless cars.
But this safety is conditional on the fact that everyone is crossing to get by.
If I go for crossing illegally, things would be even worse for them then.
Should this company basically implement this update?
And in a way that seems like in countries like Germany and Japan, it seems like that is the moral thing to do,
the right thing to do for people like, you know, of course, you know, we should be able to have those cars on the road.
But in other countries that maybe like in the Middle East, where I am from or in Latin America,
people would find it like outrageous that you will basically to make cars that could be more harmful for,
you know, you know, outrageously harsh punishment.
So this was the result of the of the major people that I just mentioned.
But there have been some work for work on the motor machine data.
One of them is basically this is collaboration with Justin Wong's group at M.I.T., who do like work on cognitive science and computation.
One of the signs and this is basically proof of concept building a computational model using Oracle Bayesian modelling.
And in this case, we can assume that people have latent prophecies over the different characters.
And then we will try to see that based on these latent preferences, which we don't know.
They have answered the machine. QUESTION So, so we've got to do is we try to reverse engineer how they've done right.
So we got to take their answers and basically we would take the scenario and spin it basically the characters into this,
let's say Victor, which would tell you one,
if the character exist in your scenario and then you adjust the dimensionality of those group using some concept like for example,
you know, the pregnant woman and a baby that are both like babies.
So it's kind of a conceptually the same. And then from those answers, you basically tried to calculate the basically, you know,
you could think about the utility is is a combination of both the weight of each of these factors and whether they exist or not.
Now, the weight we don't know which is what we try to uncover or reverse engineer using people's answers.
Once you uncover the weights for each person, then you would be able to calculate.
If I give you a new well, then you can multiply the weight by whether this person exists.
And then you can tell me, oh, this person, they would for the utility of saving this group would be better than this group.
And as a result, you can predict what they would have done.
Another project was with people at CMU who was basically using a collective decision maker.
Again, this is also a proof of concept, so we don't really recommend doing it.
But let's say if you have a group of people who have answered some of these scenarios,
then you can basically model what they would have answered on all of the other possible scenarios.
And then somehow you can have some kind of a collective decision maker and aggregation function that could aggregate all of this together.
And now it gives you one big function that would tell you what the whole society would have answered in this situation.
Now, again, this is probably not a good idea because people obviously formalising whatever.
Right. But maybe maybe let's say if you take a subset of your scenarios,
some of these scenarios that maybe that the experts think are are defensible or acceptable.
But what the experts disagree over, then maybe maybe the public could be used as a tiebreaker in some.
Okay. So now let's forget about the Oculus.com rocket motor skills for some time.
This is a project that is also risking the war machine, because when my machine made a look, I had a lot of people coming in.
We decided that maybe this chance also for us to study the original classic TV program as well.
So we opened the other page and we put this classic trolley scenarios, the ones that we only took three.
Actually, this is the one this is, I guess the classic, the most famous one.
For those of you who don't know much, you know, this is the the trolley or the bookstore, whatever is going to kill those five workers.
And you're standing you're the man in blue who is standing next to the switch.
We can put this which goes to the other side, kills this one person.
What should the person do?
And also, we had because thanks to the war machine popularity, we also had people coming also from different places in the world.
And we basically used three different versions.
For the first version, you'll know this, which you just mentioned it, which is probably the second most famous ones.
The against Wally is going to kill five people.
You're standing on a bridge, and if you have it as a large person, you could push them. No, instead of putting the switch.
And if you push them and they go down, I mean, it doesn't appear in the picture, but basically.
Like if they follow them, that their body is big, so it won't stop.
It's only what they would die by. So in a way, in both cases, you're sacrificing one person to save five.
But somehow, still, people find it more acceptable to sacrifice one person in this case than in this case.
Right. And people came out with different theories.
And and, you know, there are two main differences that people have hypothesised that make a difference.
One of them is that in this case, you're putting a switch. In this case, you're actually pushing someone by it.
So there's a comfort, emotional factor.
But also, it's kind of like a more subtle difference is that in this case,
if you if you flip the switch and say this one person actually ended up running away, you'll be happy.
That's the best thing you could do. Right. So it's actually you know, it's actually nice if they end up running away.
Right. So in a way, you're you know, that this is more like a side effect of you trying to save this life.
Well, in this case, you really want literally to hit the person.
If it misses them, then it's probably going to go and kill the five people.
So in a way, I recall that kind of double effect.
Some people came up with a different variation. That is, you know, where a man who's wearing knapsack and knapsack.
Well, basically you stop it. But basically so, you know, thank you for the other reasons.
So then people also came up with this middle as well dilemma where tries to combine elements of both.
So in this case, the trolley will if you do nothing is will continue to go to five people just like in the other case.
And if you pull the switch then it goes into the side and then kill this one person.
But unlike in the original switch, if this person runs away, then don't even look back into the fight.
So in a way similar to this one, you wanted to kind of, you know, to hit that person.
Right. So in a way, it's similar to this one is a less like this one and other aspects.
So you know, as because theorised as actually people find it more acceptable to to sacrifice
one person in this one more than in this war and in this one more dynamic.
So a sort of like most of the studies that have been done before, we're done in mostly the Western world.
Sometimes I would include people in China or Japan. But basically we were able to select it from about 40 countries.
And interestingly, we could find that in all of the 40 countries that the order was the same.
So in all these countries to put on acceptable. More acceptable to.
Religion is actually even. Through sacrifice and because it's huge.
Then the loop and then maybe for now, interestingly as well, there was some kind of radiation and these radiation was much bigger in case of Asia.
So the second question was, is there any factor that we could basically help us predict?
Why we find these differences? And one such factor is something a kind of index called relational mobility and the issue of
mobility as talks about the fluidity and ease with which people can make new relationships.
So some societies, it's much easier for people to make new relationships with spaces like, say,
London or bigger cities and other places where maybe like in the suburbs, it's, it's much harder.
So it's harder to make new relationships. But then your relationships are much they last longer.
Right? So you have this kind of more persistent relationship with people and in places where you
have a higher relation mobility because it's easier for you to make new relationships,
you become more you have more self expression, you'll be more likely to find like minded people.
So you'll be less likely to hide your opinion or like this, that you're social, less,
less worried about your the social signals that you send to your friends, unlike in the places where there's a no relation mobility.
And there have been these people who actually at least established this this measure and talked about why they thought it came about.
They talk about these places that have a lot of stress.
So people became more co-dependent. So the relationship became more on occasion, became stronger and longer energy and subsistence lives.
Like growing rice also requires a lot of people working together.
So anyway, it's kind of a side work, but this for people who are interested to learn more about this.
Now, why is this relevant to this story?
Apparently, if you live in a place where, you know,
basically like immoral judgement as it seems so like there are some moral decisions that could send a negative signal, social signals and others.
And I'm referring to some of the work that I'm sure you know Molly as well here,
who basically showed that people like if you do if you're a utilitarian decision, that's kind of send a more,
let's say, negative signal that if you are if you adopt your policy abilities,
that kind of decisions in a way, in these cases, you are sacrificing this one person to save the many.
This is kind of a sends a negative social signals to your friends.
But if you're in a society where there's a higher emission mobility, you don't care much because you know,
you can make new friends and you can you know, you can you can make you can meet like minded friends as well.
What in the other places not only would not tell people what you actually think, but you also might even internalise the views that will not help you.
So we find some kind of a, you know, a stronger correlation with these three different things.
Of course, it is driven by the Asian countries, but in another and so there was a follow up paper that did more stringent analysis on this data,
and they showed that the only one that survives is this one.
So in a way, it does make sense because the footbridge is really seen this as pushing.
Pushing a person is quite a strong social signal.
Okay. I'm going to move on to. We still have time in about 15 minutes.
Yeah. Okay. So I'm going to move on to cover relevant stuff.
This is about, as you all know, of course,
the last two years were some tough times and especially the they're probably among the first cases that were in the media talking about
a number in Italy where doctors had to start making decisions about who should get the ventilators or who should be prioritised.
And of course, in some countries, they have rules and they have codes.
In other countries, the creative committees or people or of officials who made decisions were different from the doctors who were operating.
And basically people have talked about different factors. Of course, there are always the notorious cases like first come, first serve.
The people who arrive first actually should be given ventilators first.
And then there's another one that if a group of people caught this come at the same time, then you should probably look at this.
And other people talk about different possibilities, like prognosis, who is most likely to survive and quality of life?
Whose quality of life is going to be better in the long run age,
giving priority to the younger over the other social value passed like doctors who have maybe they have.
They became sick because they have been operating on or on patients social value for future like students,
medicine students and nurse nursing students who are probably going to become able to help in the future.
So we consider these five different and these two. So we created basically this big small survey.
It was a B not not at all similar to the more massive pictorial process,
just like a week short questions, asking people from 0 to 1 or two, from 0 to 100.
How much do you think this factor should be considered? How much is too?
And then we basically took this survey and we put it into two different places.
One of them is we basically hired Google, as you probably know, the kind of company you pay the money, the.
The good thing about them, they can get you nationally because if the samples are quite expensive,
though, because they're expensive, we could basically have to choose for different countries.
Now, remember I told you about these four clusters, three clusters in the morning machine.
So we tried to basically choose one company from each one of those. So we had the US, we had Japan, Brazil and France.
We also put the survey on the war machine, which, again, we're capitalising on this.
We're going to go up. So we put it in different places. And then people would take, I think, the survey.
And then just to talk about this, the results basically if you are in favour of.
Let's say, prognoses.
If one person thinks that prognosis should be served, then we have got to basically compare that with what they thought of the first come,
first served, and that if they give a bit higher score than both, then you would say this person prefer prognosis than nothing else.
And the same thing with the other four. Now we have five different lines, each one that has two possibilities.
So we have 32 different possibilities to the point of five different possibilities.
And among these 32, there are two extremes.
One of them is 41%.
Give a higher ranking for all of these, far more than the other two and nobody else where they give all of them know where that is.
All of these things in between. What we find interesting is that in the four countries that we have used nationally,
it was in the sample that the strongest two opinions or the strongest two combinations
where the nuclear age and then zilch and then whatever else comes after that,
there is something different. Okay. So these are the four countries.
And then. We also had like about 20 other countries from the morning machine.
They did not have nationally visible samples, but we had more countries, so why not?
So we can see also that these two are the strongest two.
Let's say profiles in our data and in every.
So this is in a way and these are like the most extreme decisions as well.
So in a way, there is some polarisation about people in those countries.
Some of them was completely you know, they would not approve of anything I proposed.
But if the others would approve of all the possibilities. Right. And we did like to, as we see any of the trials, could be more palatable than others.
And it seems like prognoses seem to be like the most agreeable or most acceptable on all of this.
Other work with also we did is I'm jumping out to a different project, but I'm not going to talk much about.
But it's also inspired by the war machine. It's a different domain, but my goodness,
it's tragedy that most people who were in the group meeting today for presentation for the results that we are about to submit.
But basically you could either your $100 could either save HIPAA, which would help 20 women in North Africa,
but wedding spiel, or you could give it to charity B, which would help for women in North America.
And let's say you are in North America, for example, providing clean water,
and then you would kick and say, oh, I think we should do this and should do that. And we also use literature to basically explore different factors.
Again, some of these have been well-studied.
And finally, I'm going to talk about this paper, which is which is an opinion piece, but it's for me, it ties together.
So most of the stuff that I basically presented today, we think about it as more of a research agenda that I can do,
was co-written with all this scholars around the world and basically we are going to competition ethics and basically we're inspired
by a David Moore who's kind of very respected now in the cognitive science community of basically wrote this book about vision.
And he suggested that vision should be studied at a computational algorithmic levels.
And as a result of that, actually, the study of vision have been revolutionised.
Basically, we're able today to build machines that have the human level capacity for vision.
But we also became more able to understand how humans also see.
Right. And this was kind of like this exchange between these two things.
So trying to put vision into algorithms made it easier to understand some of these processes.
So inspired by this, we're kind of talking about that, you know,
there should be some kind of a research agenda that focus on formalising ethics and basically in algorithm in algorithmic terms.
When we say algorithms, we're kind of pretty broad here. Like could be anything you could think about.
But basically when you're not only formalising ethics,
but also using this formalisation to either engineer ethical AI systems or trying to understand human moral decisions.
So it's kind of could be either either goal. And I said here, algorithmic is pretty broad, could be just a set of rules, could be machine learning,
could be networks, but could be also, you know, an equation for representation.
And the idea here is that from one side, you know,
you could you could use whatever we have on the street about human moral decision to help us to engineer ethical AI systems.
But also, when we go through the exercise of engineering a system, it will help us understand better our own moral decision.
So it might be that those, you know, ventilation mitigators officers, maybe they were making some following some rules.
But at the same time, there were some really specific cases where they had to basically act on their own.
Right. And maybe some of these cases, maybe there were some factors were not included in the rules.
Maybe they they were not very consistent.
So consistency is quite important here, especially so the idea of computational ethics here is like going to be trying to make things systematic,
systematic or more coherent is like basically this conflict is on coherence.
And of course, when you talk about coherence, when it comes to morality, reflective equilibrium comes to mind.
For those of you who don't know, I suppose everyone probably know.
But basically, you know, you can start usually with having more than two wishes about what you should do in some specific situation,
and then maybe you can go from there into building your own theory.
Right? And you could call that moral principles about like some kind of a general rule.
Like, for example, let's say al-Shabab do like so I guess like come up with this rule that, you know,
killing you can have fear that, you know, this person killed this person here sounds like not a good thing to do.
And then you come up with the zero rule saying, well, killing is wrong. But then you come and you bring this theory to a tried using some use cases.
And now you see, how would this theory work in this use cases?
And then you test it out against your intuition.
And then you go into cases saying like, what if I'm fighting for myself and then I kill someone while I'm defending myself with killing?
Be wrong in this case. And then you be like, Oh, okay, so what can you do about that?
So then you go into another round of revising your theory.
So until you, you know,
hopefully at some point you do some kind of coherent a rule and basically just kind of trying to just put that into computational terms,
then this would just be simply trying to use some kind of data science tools to
quantify those more intuitions and talk about formalising those moral principles,
putting it into, you know, into do it like mathematics or algorithms and then use some cases.
So basically kind of the same thing, just you just Jennifer putting the computational twist to it.
And basically then we could talk about, so what does what does it mean to formalise the resolve of emerging or,
let's say, existing research fields that have been operating, some of them focussed on formalising normative ethics.
They use logic to formalise rules or use some kind of different tools to formalise the school ethics.
I've mentioned some of the work that I've worked on that could be described in this way, formalising this, or you could have lots of different values.
You want to balance things. We want to build a program that can be able to balance these different uses.
And there was evaluation. You could talk about the guy wasting the performance of the machines and evaluating the human behaviour.
Again, some also, you know, you can think about these questions related to that domain and then the idea that you
evaluate is basically at this stage what is making testing against your intuition,
what does this is about for formalising the principles?
So when you think about theories, you're more about in this domain, when you try to test it out in this way and then.
You know, if you have people who are working on this, these questions together,
then you can have some kind of synergy and then some probably new questions or let's say more emerging fields might come up.
Like, for example, nobody could. I mean, there may be some kind of research lines will emerge to talk about how can we effectively
fighting maybe shrink the difference between normative and descriptive alignment,
maybe whether in educating the public about the normative, or maybe in trying to understand more about what the public think.
And maybe this could inform what we know about normative ethics or human machine emergent morality,
because also there are so many situations where humans and machines will probably the future act next to each other.
And then some of these you know, some of these decisions could really emerge in unexpected ways,
like you can imagine driving a civic survivalist cause.
And then you might be see some kind of an intersection and you realise those cars would have to stop anyway.
So you kind of bully it. So you jump in front of it and they have to stop.
And as a result you maybe you become more aggressive in your driving and maybe
cars that are defensive will turn everyone into a more aggressive driver,
or maybe vice versa, maybe, you know, maybe the cosmic bully us and then we will be more defensive and as a result, you become more cautious.
So studying this, these questions is, you know, I guess like you could use like can theory and algorithmic algorithms,
like some kind of domains that have been work on similar questions.
But this is just really more like a research research agenda in general and without actually.