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MAT998P

Fairness and discrimination, PhD Course, #7 Sensitive attributes and proxies

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In our previous post, we discussed “group fairness“. I might have gone a bit fast, and I decided to add some material about sensistive attributes, and proxies.

Sensitive attributes ?

Almost everywhere, we can find a list of variables that are considered, by law, as sensitive, since they will lead to discrimination. As mentioned earlier, sensitive variable might change with time, and accross regions…

Another issue with black boxes is that it might be hard to assess if they are related to sensitive attribute. In order to extract informations in pictures to classify pictures, or detect pictures, algorithm might use information that could be considered as sensitive. First, recall the popular wolf-husky classifier, that detects snow in the background (since wolf were with snow in the training sample)

This can also be the case for health issues, where classifiers can be influenced by the color of the skin (or possibly some unexpected information)

Racism

The first sensitive attribute is probably the race, that has been discussed in insurance for decaded.

One should keep in mind that race is a social information, and most of the time, it is based on self-identification

This leads to popular maps in the U.S.

Racism is usually related to “colourism” (discrimination based on skin tone)

Is it relevant in the context of insurance, and risk ?

It has been observed that African Americans, in the U.S. were usually asked a higher insurance premium.

Have in mind that discrimination has nothing to do with intention, as mentioned previous. An insurance pricing can be racist, without any intention to be so. An important issue to quantify that problem is actually to observe that variable.

Sexism

Sexism is another popular exemple of discrimination, related to sex, or gender.

Actuaries have been using life tables that are gender related for more than 300 years. And indeed, it seems that women live longer than men.

Ageism

Age is another possible sensitive attribute, but it is more complicated. First, it is not a “club” and second, it is (somehow) clearly related to risk.

In dataset, there can also be selection bias, related to the age. For instance, during the COVID pandemic, triage was based on the age of patients. Treatements and tests can be related to the age of patients. So this bias will probably have an impact on observed risks.

Genetics

Another important sensitive variable is related to “genetic information”.

Such information is usually classified as sensitive everywhere.

To conclude, I wanted to mentioned that several important variables considered as sensitive have not much to do with genetics, but more with a social construction.

Finally, let us discuss proxies that can be related to those sensitive variables.

Names and language

The first one was discussed in the introduction : names contain information about race and ethnical origin,

Text and discussion can also reveal sensitive information.

Pictures

Pictures can also provide information. That was discussed 150 years ago, where researchers tried to identify criminals using solely pictures.

Some insurers have been trying, at some point, to detect diseases on facial pictures. And it possible to reveal informations from pictures. Possibly the age, and the gender.

One can also use satellite pictures, or pictures from Google Street View, such as the wealth in the neighborhood. And possibly sensitive information, such as the presence of an access ramp for disabled people.

Credit Scoring

Credit scoring is also a variable used by insurers, that can be related to variables considered as sensitive

Clearly, a bad credit score will have a big impact not only on mortgages and loans,

but also on insurance rates ! As we explained here, it costs a lot to be poor.

Networks

Finally, insurance can use information related to friends, or family, to assess the risk. And netword data capture a lot of sensitive information.

We will talk a little bit about network, to explain why using your friends risks to assess your own risk might not be a great idea…

It is an extension of th friendship paradox.

Proxies

Finally, we will conclude by showing that removing a sensitive attribute from a training dataset will not mitigate discrimination.

Computer

Reading text automatically

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It is now very easy to read (automatically) some text that can be found in a pdf file. For instance, consider the program of the conference we had yesterday – and today – in Rennes

> library(pdftools)
> scan_pdf <- pdf_text("http://crem.univ-rennes1.fr/Documents/Docs_sem_divers/2017_03_10-11_JJD/JDD_prog.pdf")
> cat(scan_pdf)
Journées Jeunes Docteurs
Programme du jeudi 9 mars 2017
Faculty of Economics - Rennes - Amphi Henri Krier
9h- 9h30 - Accueil
9h30-10h15 :      Présentation du CREM, de la faculté et des activités de recherche liées du ou laboratoire
10h15-10h50 :     Emmanuel LORENZON (Université de Bordeaux, GREThA)
Collusion with a rent seeking agency in sponsored search auctions
10h50-11h25 :     Julien BERTHOUMIEU (Université de Bordeaux, GREThA)
The Impact of “At-the-Border” and “Behind-the-Border” Policies on Cost-Reducing Research
and Development
Co-écrit avec Antoine Bouët

(etc). As you can see, it is working well, even in French, where we have those weird letters (with accents). Here, it is working well because the pdf is vectorized, i.e. it was generated properly, by open office.

But sometimes, we can have only a scanned version of a letter

or just a picture with some typed text. I will not mention hand-writing because it is much more complex.

The other day, my friend Fleur did show me a picture, and some very simple lines of code,

> library('tesseract')
> pic1="http://freakonometrics.hypotheses.org/files/2017/03/pic1.png"
> text_fr <- ocr(pic1, engine = tesseract("fra"))
> cat(text_fr)
Près de 14.400 décès

Si [épidémie de grippe est un phénomène récurrent. celle de
2016—2017 présente plusieurs spécificités. outre sa virulence :
une survenue plus précoce que d‘habitude. une activité
modérée en médecine ambulatoire. mais un impact fort en
milieu hospitalier.

It looks like we’ve be able to extract typed text from a picture ! I want to check. I have to admit, first of all that installation on a linux machine is tricky: one has to install first leptonica, and then follow some guidelines to install tesseract (see also Artem‘s advices). It took me some time, but I’ve been able to install the package.

The first important step, it to train the algorithm with some texts in French (because it is in French in my picture)

> library('tesseract')
> tesseract_download("fra")

Then, I did try with the picture that Fleur did send me (the picture was inserted in the core of the message)

> pic2="http://freakonometrics.hypotheses.org/files/2017/03/pic2.png"
> text_fr <- ocr(pic2, engine = tesseract("fra"))
> cat(text_fr)
Près de 14400 decès

s. mm….agw«… ………«…m ……
a……u…u Dhs—ur; ;pmum…. ;: u……
… W»: »… w…q…na… … ……
…… ………u…_ …… mm……
…… nwm/u

… … mm…—mg…»— sa…… su a.…….…
: :mmræwesdæ ; ; m…decnflwtülflws WWW…
un»… M on m…… . … … .. m...… wma:
.,… … V, …… … …;………yg…gn…
…… pe- le…samemeuuœwpwv m…
mum

Clearly, something went wrong here. When I got that output, I thought that I did not train properly the function. But it was not the answer. As described in that post (in French) it is necessary to have a clean picture, to read it properly

And actually, if we zoom in our picture – the first one, used by Fleur, to show me that package – we have

while for the second one – with a lower resolution – we have

It is necessary to have a scan of a typed text with high resolution… And you have to admit that it is awesome….

The good thing is that I have to work with a judge, in France, to assess quality of experts. And since most of the reports are typed, and then scanned, I am glad to have such a function. I just have to make sure that the resolution is high enough…