Speaker 1: It is my pleasure today to welcome to the program Professor Olivia Simone at
HGC Paris, as well as coauthor of the book Noise A Flaw in Human Judgment, co-written
by Daniel Kahneman, as well as Cass Sunstein case, a multi time guest on the program before
Olivia.
So great to have you on today.
Speaker 5: Thanks for having me.
Speaker 1: So maybe to start with, to kind of introduce some of the things we're talking
about, many of us can imagine situations in which the inputs seem to be the same.
It might be a situation where a patient goes to two different doctors with the same symptoms
or a driver is driving.
And there is a situation with some pedestrians and vehicles where the circumstances seem
to be the same.
But there's something we call human judgment that is applied, which sometimes gets each
individual to make a different decision, whether it's driving, whether it's about a diagnostic
test that a doctor wants to do in in your world.
How do you start quantifying this X factor of human judgment and thinking about it in
a way that we can start making some observations about it?
Speaker 5: Let's first start by double clicking on what you call human judgment here, because
in the examples you've taken, yes, we have human judgment in some cases.
We also have human judgment, but we don't mind variation.
So say we are deciding, you know, what what hotel we prefer in a foreign city or what
dish we prefer in the menu.
It's fine if you and I disagree.
That's not something where we where we expect uniformity.
The problem that we've been writing about in lawyers is when we have human judgments
on which we expect uniformity, on which we think there is a correct answer.
For instance, as you said, when the patient is visiting a doctor, the diagnosis should
not be a matter of opinion.
It should not be a matter of taste or preference.
It should be a matter of professional judgment.
And we pretty much expect that different doctors would come to the same judgment.
And the problem is quite often they don't.
And that's what we call noise, the unwanted variability in human judgments.
Speaker 1: What are the factors that we what are the categories of factors that can create
these differences?
You know, I remember reading about in fact, it may have been a Daniel Kahneman book.
Now, I don't remember offhand about sentencing in criminal cases and how one factor that
affected sentencing was, is it close to lunchtime or is it after lunchtime?
In other words, how hungry is the judge seemed to play a role in determining a sentence?
What what category would that type of of a judgment discrepancy fall under and what types
of categories are there?
Speaker 5: So this category, the example you take here, which is a famous study of judicial
decisions, is what we call occasion noize occasion.
Noise is the fact that the same people looking at the same facts on two different occasions
will sometimes not have exactly the same judgment.
So a judge looking at the same defendants tried for the same case before lunch or after
lunch will not have exactly the same judgment.
And by the way, this is true also of factors such as the temperature, whether the weather
is sunny or rainy, whether the judge's favorite football team won or lost the game the previous
day, and whether the previous cases that the judge looked at were favorable or unfavorable.
So there are a lot of factors that are going to influence the judge that are completely
extraneous to the case that should have nothing to do with the case but influence it.
That's what we call occasion noise.
It's variation within the person.
And we can all feel this when we when we know, oh, I made that decision because I was in
a bad mood.
If I think back on it, I would have made a different decision.
Now that I'm in a good mood, that's that's fairly intuitive.
We're aware of that.
Who probably underestimates it, but we're aware of it.
That's one of the categories.
Speaker 1: Can you give us some of the other other categories?
Speaker 5: Sure.
So another category which is relatively easy to wrap your mind around is if you take again
the example of judges that people have different average levels of severity.
So some judges are going to be tough hanging judges, other judges are going to be more
lenient.
They're, you know, the bleeding hard judges who feel for the defendant.
If you give these two judges the same 20 different cases, of course, each of the twenty cases
is different.
But the average sentence that the bleeding heart judge gives is going to be a lot less
than the average sentence.
And the judge gives.
That's what we call level noise.
And then there is a third category, which is a bit harder to wrap your mind around,
but which is actually the most important one.
And it's easy to to see if you think again, if the judges take these two judges, the lenient
one and the and the tough one is not only do they disagree on the average level of their
sentences, they're also going to disagree on each case.
If you ask them to rank the cases from the the worst case to the least civilly punishable
case, they are not going to agree on the ranking because basically they have different tastes.
They are different individuals.
They have different life experiences.
They have different preferences.
They may have different political opinions.
Basically, they see different things in the cases that they look at.
They have different patterns of judgment and that's what we call pattern lawyers.
So we have occasion.
Those we have level knows we have patronized.
When you put all those things together, you have a lot of reasons for judgments that should
be identical, ideally to, in fact, differ and differ quite a bit.
Speaker 1: So there are people who are maybe technocratic by nature who when you present
this to them, they would come up with a solution, maybe when it comes to the sentencing question,
or maybe when it comes to when a patient comes in with certain complaints, what's the path
of testing that that should be done where they would essentially say we need to eliminate
some of this variability, we will use a computer program of some kind, artificial intelligence.
We put in the inputs and it tells us this person should get seven years in prison and
everybody gets seven years in prison or this patient should get the CAT scan.
This one should not.
That would be one approach to this.
Is that does that account for the true nature of complexity with many of these situations,
or would that solution leave something out that's important?
Speaker 5: Well, there is a difficult tradeoff to make here.
What is guaranteed is that if you do what you just described, you will eliminate the
noise.
Yes, because whatever system, whatever rule, whatever algorithm, whatever formula you put
in place is going to be noise free.
And that's why it's often when you actually measure the performance of algorithms and
rules and so on, it's often quite good because it eliminates the noise, the level noise,
the occasional is the better noise.
It basically makes sure that when you have the same inputs, you will have the same output.
Now, does that make does that necessarily make rules and algorithms a good thing?
No, because noise is not the only way you can be wrong.
You can also have bias.
So suppose to take an extreme but not completely crazy case that you have a rule that says
anyone who's been sentenced of this particular crime gets exactly this punishment, or anyone
who's been sentenced three times of a felony goes to jail for life.
Three three strikes and you're out.
Yes.
Well, you've eliminated noise there, at least if you apply the rule.
But a lot of people would say you've created another form of error, which is a form of
bias, which is that you are.
Giving too severe a sentence to a lot of people who would not deserve that sentence.
Yes, you've eliminated the unwanted variability between people who have committed the same
three crimes, but you've added a lot of bias in the form of unwanted severity against people
who have committed three not so bad crimes.
So there's a trade off between how accurate and how unbiased an algorithm or a rule can
be and the fact that it will be noise free because that tradeoff is a difficult one.
There is going to be lots of cases where, in fact, you will have to continue to resort
to human judgment.
You cannot replace it with a rule and therefore you need to try to make it better, less noisy
without eliminating the human component Speaker 1: where where biases affect human
judgment.
Sometimes it has been argued that being aware of these biases can help mitigate their impact.
So whether it's availability bias or whether it's confirmation bias, that understanding
that this is something that might impact our judgment helps us to reduce it when it comes
to noise, is is it is awareness also useful in mitigating it in an individual or is it
less useful?
Speaker 5: So first of all, for bias, there isn't actually a lot of evidence that simply
being aware of the biases is an effective, lasting, sustainable way to reduce your biases.
It doesn't work that well.
Awareness is good.
It makes you more careful, but it's not in itself sufficient for noise.
It's even worse.
OK, because here's the problem of noise.
You don't know which way it's going to make you go wrong.
You don't know of all the possible sources of noise, of everything that affects you in
a particular occasion or for your individual preferences that could be different from somebody
else's.
Of all the factors that can influence you on a particular case, you don't know in what
direction it's going to make you go wrong.
So you need to actually take a stance that we call decision hygene.
That is a form of prevention against noise without knowing how noise is going to affect
you.
The reason we call it decision hygiene, which is slightly off-putting and that's intentional,
is that we we liken this to washing your hands.
When you wash your hands, you don't know what germs you're eliminating.
You you're not saying, oh, this is the covid virus and this is the flu virus.
And if you have these two particular things and in fact, if you're successful, you will
never know what problem you've prevented.
But you know that it's good discipline, that it's good practice, that it's good hygiene.
Likewise, everything that you do in your decision making process, ahead of the decision, ahead
of time to make your decision process more disciplined, is going to improve the quality
of your decisions because it's going to make you less noisy
Speaker 1: when it comes to institutions versus individuals.
Noise at the institutional level sometimes can lead to what what some call systemic types
of biases.
You could imagine how noise and human judgment might lead to racial discrimination in housing
decisions, for example, or transportation decisions about where should the stops for
the subway line be, which could be influenced by all of these types of factors.
Do you approach institutional decision making noise differently than, for example, a doctor
or a driver making a decision about an individual patient, for example?
Speaker 5: We actually view the doctor as part of an institution.
When you only see one doctor, you will never know if your doctor is noisy.
You go to a doctor, your doctor tells you you have the flu, you go home, you take your
medicine and it gets better or it doesn't get better.
You will never know whether another doctor in the same hospital would have told you,
no, it's not the flu, it's covid-19.
Right.
Or it's not the flu.
It's something else.
So if you think about who should take care of noise, it's actually always the institution.
As individuals, we can try to make ourselves more disciplined.
But the institution, whether it's the hospital or the justice system, the the organization
that takes care of hiring individuals or the company that has a hiring process, all those
institutions should try to reduce both the noise and the biases in their decisions.
And if they put in place good decision hiring practices, they will reduce noise.
They will also at the same time reduce the sorts of systemic biases that you are talking
about, because when you put in place good decision practices, you will make your decisions
less exposed, less dependance, less sensitive to the biases of individuals.
Speaker 1: The last thing I wanted to talk about is whether there are some cases where
we're maybe both noise and bias are good in the sense that they are sort of what makes
us uniquely human in the example that I will give is as we learn more about autonomous
vehicles and the.
Decisions that drivers are sometimes making almost intuitively in a fraction of a second
about, for example, merging in a rotary or traffic circle, whether it's the right time
or it's not the right time or about getting through a yellow light or not, this is something
that it's really been reduced to.
It's happening in a very, very small amount of time.
And we're sort of intuitively considering the position of pedestrians and how far we
are from the light.
In some of these cases, there will be variability, but it seems that that it's actually quite
hard to replicate it accurately with an artificial intelligence or with computers.
What about those cases where noise seems to be a sort of uniquely human ability to make
these judgment calls in a very small amount of time?
Speaker 5: So we wouldn't call this noise.
We would say that those quick decisions that you're talking about are system one decisions.
They are decisions that are made without consciously thinking about what you're doing.
And there are based on a lot of learning that has taken place, they can be excellent decisions
that you can have great instincts.
If you have been well-trained, you can have acquired expertize at something.
So if you are the Formula One driver, your your instincts to drive a Formula One car
are extraordinarily fine, extraordinarily fine tuned, and you're able to make your decisions
in a fraction of a second.
That quite likely would be difficult for a computer to make.
Now, is this a case of noise?
You wouldn't call this a case of noise in the sense that those decisions are hypothetically,
as you as you describe them, good.
They're actually consistent.
The the expert would make the same decision.
A lot of times when faced with the same situation where we would see noise is when a person
making a decision is not consistent and different people are not consistent with each other
in the decisions that they make.
And that creates variability.
That is not the case in the example you were talking about, where presumably there are
maybe humans who are better than machines.
That will remain the case for a long time in a lot of important decisions.
Speaker 1: So those it's really a different category altogether.
Speaker 5: Yes.
And there will be we I don't want to give the impression that that machines are the
solution to everything and clearly they aren't.
And that's why we were writing this book that actually talks not about how to eliminate
human judgment, but how to replace it.
There will be lots of decisions that we will want humans to make, either because it's too
difficult to have a machine that makes it or because we don't want the machine to make
it in our place, because we we care about dealing with the different human being.
We want to give people a sense of agency.
We want to give people who interact with the decision the dignity of dealing with the human
being and not with the machine.
There are lots of very valid reasons to retain human judgment.
And when that is the case, we need to make sure that those judgments, if they cannot
be the sort of expert snap second judgments that you are talking about, that they are
made in a considered careful way so that they are less noisy, more just and less prone to
error.
Speaker 1: That that that makes a lot of sense.
We've been speaking about the book Noise A Flaw in Human Judgment, co-written by Daniel
Kahneman, Cass Sunstein and our guest today, Professor Olivia Khubani.
So great having you on.
I really appreciate your time today.
Speaker 5: Thank you very much, David