Translator: Michele Gianella Reviewer: Nicoletta Pedrana
Good evening.
I decided to start this talk with a question:
have you ever heard of "unrelated data"?
Unlinked data describe an event
but don't tell who underwent it: i.e. who got a medical exam:
they say everything about the visit,
but there is no patient ID;
linked data, instead, come with an identifier
which can may be an anonymous one,
but still allows, then,
to relate different events to the same person.
This distinction is important,
because so many decisions that concern us -
as for example
the decision whether to use a prosthesis, if someone's got a hip disease;
a bank's decision, if one's looking for a home,
whether to grant them a loan or not,
and at what conditions;
or maybe even, if someone is convicted of a misdemeanor,
whether social services custody should be proposed or not.
These kinds of decisions,
that were previously taken qualitatively, by people,
are now increasingly driven by models,
and these models are "AI-powered",
a bit thiumphal way
to say that these models learn from examples,
they're fed with examples
of situations that happened in the past,
of good or bad decisions that were made in the past,
and the model learns to generalize from examples,
and then suggest a decision
which, hopefully, is better than one that what would be taken quantitevely.
What you might ask yourself is,
Should these models,
who are trained with examples, data,
use linked or unrelated data?
The problem with linked data is, they can certainly be formally anonymous,
embedding as they do an anonymous identifier.
But if one knows a bit of the context those data were taken from,
she often manages to track back the identity of the person
those data are referred to.
So I don't want to answer the question right now:
I'd like to tell you a story instead,
because this story leads to answer the question.
The story began in January 2020:
I went to Abu Dhabi and got stuck there
because the pandemic canceled every flight.
And I've been involved with my university
in a project we had to propose,
on estimating - much like we said just now -
the impact of the contagion.
We were asked for a prediction
at two weeks, three weeks, four weeks
of how many people would be infected.
We thought about it for a second, and we got the idea
of starting from position data,
the so-called "digital tracks"
that people no longer leave today, but their phones do.
Please, let me get this straight:
not the GPS location data, which we all use in maps.
Because those data are calculated on your phone,
so in order to make them available
the owner of the mobile phone must authorise their use,
and therefore, by definition, these are partial data.
We're talking about the very data the mobile network knows about us:
it knows which cell we're connected to.
In fact, nowadays they are not exactly cells:
they can limit our position within a pixel,
a square inside a cell
a few tens of meters' side.
The ones you see behind my back
are precisely these digital tracks
featured in databases,
in the archives of mobile phone operators and networks.
So we figured out,
let's put these digital tracks together,
that are complete, involve everyone's phones -
there are only two major mobile network operators in UAE -
let's put this data together with two other elements.
One element is the maps:
we know where these boxes are,
which areas of the city they relate to.
Here, behind my back, what you see now
divided into these pixels, these squares,
is Abu Dhabi.
As you can see, it's a city of islands.
And for each square, I also know if it's outdoors or indoors.
I know if it's part of a supermarket, or a house,
I know what happens there;
and so, somehow,
with this element I can assess
the so-called "Brooks Formula."
Brooks was the director
of the Infectious Disease Control Centre
in the US,
and basically this formula calculates the chance of getting infected
when we're in the same pixel with someone who already got the disease.
This depends on how long we stay there;
and it also depends on some conditions,
e.g. the viral mass transfer rate
between the infected person and other people.
This speed depends on whether we're outdoors, indoors,
if it's windy or not.
Plus, it also depends on the virus variant.
Here is a recent variation:
when we started, the Brooks Formula
talked about "15 minutes for an indoor pixel".
And today, this is a newer version of the virus
which has become smarter,
so it already causes contagion with less viral mass transferred,
and therefore the contact time it takes
to transmit this contagion
is much shorter.
However, we had to put these three elements together:
and through these three elements, knowing where people were going,
and how long they stayed together inside the pixels,
we wanted to predict what the viral mass transfer would be like:
and therefore also, collectively, what the impact would be
in terms of the number of people infected.
And we started working in this direction;
until it occurred to us that, after all,
data on people's mobility are not ordinary data.
Knowing where I am,
and how long I stayed there at different times of the day,
is a quite sensitive information.
Mobile network operators, i.e. service providers, do know this,
but we only revealed them to run the network.
Law enforcement may also know this;
but they need a licence from a judge,
they need a reason to ask.
So,
moving to widespread monitoring of people's mobility,
even if it had given us a very effective model -
Well, we stopped for a second.
And we decided to take a slightly different path.
First of all, we used the unbound data:
we took the data and forgot the identifier,
we didn't even ask to phone operators,
the subject behind those data.
We know there were phones in the various cells,
but we don't know if it was the same from one event to the next.
And then we used this unrelated data
not to follow them, but to profile simulated users.
So we had a model that generates simulated users
who statistically behave like the real ones.
And lo and behold,
the contagion forecasts we made with this model
are of excellent quality,
so much so that now we use it
to assess the risk represented by cross-border arrivals,
that is, when a plane lands, or a ship arrives,
the people on board are added
and the mobility model assesses the risk,
should these people be infected,
of spreading the disease to other people
within one or two weeks.
I told you this story
not to draw your attention to this model,
but rather to the takeaway.
And what's the takeaway?
We have the right to ask ourselves
if the information we transfer to those who apply models,
even assuming it's useful to everyone,
comes in proper quantity.
There is a "Principle of Minimality"
which says that the information that we should transfer
shall be the one strictly needed to do the thing we are promised.
And it is very important to know about this principle;
and we also know that sometimes
the unrelated data we collect are more than enough,
If that app, maybe "Health and Beauty",
that we have in our mobile phone,
collects data about our behavior,
and asks the permission to share them anonymously,
the difference between anonymity and unrelated data
is hopefully, after tonight,
even among those who don't deal with technology
clear in mind.
And how can we transfer these data?
Here, you see how our model populates contagions
based on the landing by a group of people.
But how can we explicitly ask
the respect of the Minimality Principle?
We can do that
because the apps we use, that we have on our phones,
all have, by law, a data management policy.
And this policy can be seen,
it's for us to read.
And if we only asked to read it,
or alternatively, when the app shows it to us,
we didn't mindlessly click "I accept",
but just let some time pass between one click and the next,
you would see that some Artificial Intelligence
of the application provider
would notice that users are actualy reading,
and pay attention to the conditions at which the information is provided
to let the model run.
The control over the informations we provide, even for a just cause,
must stay with their source, let's never forget it.
Thank you.
(Applause)