# Fairness and discrimination, PhD Course, #5 Models and Data

For the fifth course, we will discuss machine learning and standard techniques used to get predictive models, and to assess accuracy of those models.

#### GLM (possibly constrained)

Classically, we use a penalized version of least squares (but this can be adapted to GLMs, when penalizing the negative log-likelihood).  Because of Karush–Kuhn–Tucker conditions, having a constraint on the parameter is equivalent to the following penalized problem, when the constraint is on the $\ell_2$ norm of $\boldsymbol{\beta}$,

We can also consider the $\ell_1$ norm of $\boldsymbol{\beta}$,

Those two approaches can be see as a trade-off between accuracy (here the empirical risk on the left) and complexity of the model (on the right). And we can also consider a mixture of the two norms,

As we will see, it will also be possible to consider some penality related to fairness and discriminiation measures (in-processing).

#### Classifier and ROC Curves

We will also recall metrics used in the context of classification, such as the ROC curve

Each point of the curve can be related to two areas related to the distributions of the scores (in the two groups), for the same threshold – namely the false positive rate and true positive rate

Based on the ROC curve, we can define the AUC, the area under the ROC curve,

But for classifiers, the important challenge is to have calibrated scores, meaning that we want the score to be interpreted as the true underlying probability.

#### Calibration

Well-calibration is defined as follows

or (with different notations)

It is a well know properties in several applications.

The plot on the right is the calibration plot,

We can easily get that plot

This concept is related to the question “do probabilities returned by some model represent reals probabilities ?” For instance, below, we have pictures generated as some sort of geodesic between two pictures, with a woman on the top left, and a man in the bottom right, published in the New York Times. And below, “probabilities” given by  https://www.picpurify.com/demo-face-gender-age.html.

We could agree that it is rather strange that probabilities (to have a man) do not increase continuously, but on top, with extremely high confidence, the model predicts that the picture is the one of a woman, and below, also with extremely high confidence, that the person is a man…

#### Data, observations vs. experiments

Then, after concept and notations related to models, we will talk about data. More specifically, the distinction between observations and experimentations.

Another popular classification is the one discussed by Judea Pearl.

So we will talk about association, correlation, causal inference, and counterfactuals.

#### “Correlated variables” or proxies

One important issue, is that with massive data, one can easily get a (good) proxy of almost any sensitive variable.

The concept is related to comonotonicity, or perfect correlation.

But this is clearly too strong, so we will discuss depedence measures, too.

#### Independence properties

Recall that independence is defined as follows

and we can consider a weaker form, based on null-covariance

or null-correlation

(sidenote, this correlation measure is bounded, and those bounds are related to Hardy-Littlewood inequality and optimal transport)

An interesting measure is the maximal correlation

or we can consider a weaker version, without consider all possible transformation, but only a subset

Another important concept is the one of conditional independence

(the later will be used in the context of causal graphs).

#### Causality

Before talking about causality, recall that what non-independence mean…

We can then construct causal graphs, or “directed acyclic graphs”

where nodes are the variables used in the model, and the outcome (usually that the end of the causal graph). Then we define paths

and the concept of d-separation

This concept is related to the statistical property of conditional independence

More precisely, we have the following Markov property on causal graphs

For example, for such a graphical model,

the joint distribution is $$\mathbb{P}[x_1,x_2,x_3,x_4]=\mathbb{P}[x_1]\times \mathbb{P}[x_2|x_1]\times \mathbb{P}[x_3|x_2]\times \mathbb{P}[x_4|x_3]$$and for the graphical model below

we have$$\mathbb{P}[x_1,x_2,x_3,x_4]=\mathbb{P}[x_1]\times \mathbb{P}[x_2]\times \mathbb{P}[x_3|x_1,x_2]\times \mathbb{P}[x_4|x_3]$$Those graphs can be related to structural models (with idiosyncratic noise denoted $U$), since

#### Potential outome

Another important concept is the concept of counterfactuals, and potential outome. In an ideal world, we would have observed the outome in both cases, with and without the treatement

but in real life, it’s only one of them,

And the goal will be, somehow, to estimate what the non-observed outcome would be. And then, classical quantites we wish to estimate are the average treatement effect, and the conditional version, based on some covariates.

This concept will be related to counterfactual fairness actually, when the “treatement” will be the sensitive attribute.

#### Twin network representation of the counterfactual

Finally, we will consider a so-called “twin network representation”. Consider a DAG, associated with some simple structural model

Based on a structural model, we can get values of idiosyncratic noise component

Then, we use those values on the twin representation, when the treatement is not $0$, but $1$. Counterfactuals are created by using the same noises

The difference between the two outcomes is the treatement effect, or the disparate treatement

or more generally, we write

This is an idea used in Plecko & Meinshausen, 2019, in the context of fairness, but we will discuss this more, later on…

# Fairness and discrimination, PhD Course, #4 Wasserstein Distances and Optimal Transport

For the fourth course, we will discuss Wasserstein distance and Optimal Transport. Last week, we mentioned distances, dissimilarity and divergences. But before talking about Wasserstein, we should mention Cramer distance.

#### Cramer and Wasserstein distances

The definition of Cramér distance, for $k\geq1$, is

while Wasserstein will be (also for $k\geq1$)

If we consider cumulative distribution functions, for the first one (Cramer), we consider some sort of “vertical” distance, while for the second one (Wasserstein), we consider some “horizontal” one,

Obviously, when $k=1$, the two distances are identical

c1 = function(x) abs(pnorm(x,0,1)-pnorm(x,1,2)) w1 = function(x) abs(qnorm(x,0,1)-qnorm(x,1,2)) integrate(c1,-Inf,Inf)$value [1] 1.166631 integrate(w1,0,1)$value [1] 1.166636

But when $k>1$, it is no longer the case.

c2 = function(x) (pnorm(x,0,1)-pnorm(x,1,2))^2 w2 = function(u) (qnorm(u,0,1)-qnorm(u,1,2))^2 sqrt(integrate(c2,-Inf,Inf)$value) [1] 0.5167714 sqrt(integrate(w2,0,1)$value) [1] 1.414214

For instance, we can illustrate with a simple multinomial distribution, and the distance with some Binomial one, with some parametric inference based on distance minimization $$\theta^\star=\text{argmin}\{d(p,q_{\theta})\}$$(where here a multinomial distribution with parameters $\boldsymbol{p}=(.5,.1,.4)$, taking values respectively in $\{0,1,10\}$, while the binomial distribution has probabilities $\boldsymbol{q}_{\theta}=(1-\theta,\theta)$, taking values in $\{0,10\}$)

One can prove that

while

When $k=1$, observe that the distance is easy to compute when distributions are ordered

When $k=2$, the two distances are not equal

In the Gaussian (and the Bernoulli) case, we can get an expression for the distance (and much more as we will see later on)

There are several representations for $W_2$

And finally, we can also discuss $W_{\infty}$

#### Wasserstein distances, and optimal transport

Wasserstein distance can also we written using some sort of expected values, when considering random variables instead of distributions, and some best-case scenario, or cheapest transportation cost,

which lead to the so call Kantorovich problem

An alternative way to look at this problem is to consider a transport map, and a push-forward measure

This is simply

Of course such mapping exist

We can then consider Monge problem

And interestingly, those two problems are (somehow) equivalent

#### Discrete case

If $\boldsymbol{a}_{{A}}\in\mathbb{R}_+^{\color{red}{n_{{A}}}}$ and $\boldsymbol{a}_{{B}}\in\mathbb{R}_+^{\color{blue}{n_{{B}}}}$, define$$U(\boldsymbol{a}_{{A}},\boldsymbol{a}_{{B}})=\big\lbrace M\in\mathbb{R}_+^{\color{red}{n_{{A}}}\times\color{blue}{n_{{B}}}}:M\boldsymbol{1}_{\color{blue}{n_{{B}}}}=\boldsymbol{a}_{A}\text{ and }{M}^\top\boldsymbol{1}_{\color{red}{n_{{A}}}}=\boldsymbol{a}_{B}\big\rbrace$$For convenience, let $U_{\color{red}{n_{{A}}},\color{blue}{n_{{B}}}}$ denote $$\displaystyle{U\left(\boldsymbol{1}_{n_{{A}}},\frac{\color{red}{n_{{A}}}}{\color{blue}{n_{{B}}}}\boldsymbol{1}_{n_{{B}}}\right)}$$ (so that $U_{\color{red}{n},\color{blue}{n}}$ is the set of permutation matrices associated with $\mathcal{S}_n$). Let $C_{i,j}=d(x_i,y_{j})^k$so that $$W_k^k(\boldsymbol{x},\boldsymbol{y}) = \underset{P\in U_{\color{red}{n_{{A}}},\color{blue}{n_{{B}}}}}{\text{argmin}} \Big\lbrace \langle P,C\rangle \Big\rbrace$$where$$\langle P,C\rangle = \sum_{i=1}^{\color{red}{n_{{A}}}} \sum_{j=1}^{\color{blue}{n_{{B}}}} P_{i,j}C_{i,j}$$ then consider $$P^* \in \underset{P\in U_{\color{red}{n_A},\color{blue}{n_B}}}{\text{argmin}} \Big\lbrace \langle P,C\rangle \Big\rbrace$$For the slides, if we have the same sample sizes in the two groups, we have

we can illustrate below (with costs, or distances)

And with different group sizes,

i.e., if we consider real datasets

And as usual, we can consider some penalized version. Define $$\mathcal{E}(P) = -\sum_{i=1}^{\color{red}{n_{{A}}}} \sum_{j=1}^{\color{blue}{n_{{B}}}} P_{i,j}\log P_{i,j}$$or$$\mathcal{E}'(P) = -\sum_{i=1}^{\color{red}{n_{{A}}}} \sum_{j=1}^{\color{blue}{n_{{B}}}} P_{i,j}\big[\log P_{i,j}-1\big]$$ or $$\mathcal{E}'(P) = -\sum_{i=1}^{\color{red}{n_{{A}}}} \sum_{j=1}^{\color{blue}{n_{{B}}}} P_{i,j}\big[\log P_{i,j}-1\big]$$ Define $$P^*_\gamma = \underset{P\in U_{\color{red}{n_{{A}}},\color{blue}{n_{{B}}}}}{\text{argmin}} \Big\lbrace \langle P,C\rangle -\gamma \mathcal{E}(P) \Big\rbrace$$ The problem is strictly convex.

#### Sinkhorn relaxation

This idea is related to the following theorem

Consider a simple optimal transportation problem between 6 points to 6 other points,

set.seed(123) x = (1:6)/7 y = runif(9) x [1] 0.14 0.29 0.43 0.57 0.71 0.86 y[1:6] [1] 0.29 0.79 0.41 0.88 0.94 0.05 library(T4transport) Wxy = wasserstein(x,y[1:6]) Wxy$plan that we can visualize below (the first observation of $\boldsymbol{x}$ is matched with the last one of $\boldsymbol{y}$, the second observation of $\boldsymbol{x}$ is matched with the first one of $\boldsymbol{y}$, etc) We observe that we simply match according to ranks. But we can also use a penalized version Sxy = sinkhorn(x, y[1:6], p = 2, lambda = 0.001) Sxy$plan

here with a very small pernalty

or a larger one

Sxy = sinkhorn(x, y[1:6], p = 2, lambda = 0.05) Sxy$plan In the discrete case, optimal transport can be related to Hardy-Littlewood-Polya inequality, that is related to the idea of matching based on ranks (corresponding to a monotone mapping function) We have then In the bivariate dicrete case, we have #### Optimal mapping We have mentioned that, in the univariate setting and clearly, $\mathcal{T}^\star$ is increasing. In the Gaussian case, for example$$x_{{B}}=\mathcal{T}^\star(x_{{A}})= \mu_{{B}}+\sigma_{{B}}\sigma_{{A}}^{-1} (x_A-\mu_{{A}}).$$In the multivariate case, we need a more general concept of increasingness to define an “increasing” mapping $\mathcal{T}^\star:\mathbb{R}^k\to\mathbb{R}^k$. In the Gaussian case, for example, we have a linear mapping,$$\boldsymbol{x}_{{B}} = \mathcal{T}^\star(\boldsymbol{x}_{{A}})=\boldsymbol{\mu}_{{B}} + \boldsymbol{A}(\boldsymbol{x}_{{A}}-\boldsymbol{\mu}_{{A}})$$where $\boldsymbol{A}$ is a symmetric positive matrix that satisfies $$\boldsymbol{A}\boldsymbol{\Sigma}_{{A}}\boldsymbol{A}=\boldsymbol{\Sigma}_{{B}},$$ which has a unique solution given by $$\boldsymbol{A}=\boldsymbol{\Sigma}_{{A}}^{-1/2}\big(\boldsymbol{\Sigma}_{{A}}^{1/2}\boldsymbol{\Sigma}_{{B}}\boldsymbol{\Sigma}_{{A}}^{1/2}\big)^{1/2}\boldsymbol{\Sigma}_{{A}}^{-1/2},$$ where $\boldsymbol{M}^{1/2}$ is the square root of the square (symmetric) positive matrix $\boldsymbol{M}$ based on the Schur decomposition ($\boldsymbol{M}^{1/2}$ is a positive symmetric matrix). In R, for example, use the expm package, M = expm::sqrtm(matrix(c(1,1.2,1.2,2),2,2)) M [,1] [,2] [1,] 0.8244771 0.5658953 [2,] 0.5658953 1.2960565 M %*% M [,1] [,2] [1,] 1.0 1.2 [2,] 1.2 2.0 #### Optimal mapping, on real data To illustrate, it is possible to consider the optimal matching, between the height of $n$ men and $n$ women, Another example (discussed in Optimal Transport for Counterfactual Estimation: A Method for Causal Inference – with a nice R notebook created by Ewen), consider Black and non-Black mothers in the U.S. or the joint mapping, in dimension 2 We will spend more time on those functions (and the related concept) in a few weeks, when discussing barycenters and geodesics… More details in the slides (online) and in the forthcoming textbook, # Fairness and discrimination, PhD Course, #3 Machine Learning, losses and distances For the third course, we will get back a little bit on machine learning (slides are still online on the github repository). The starting point will be loss functions and risk. #### Loss functions and risk A general definition for a loss is that it is positive, and null when we consider $\ell(y,y)$. As we will discuss further, it is neither a distance, nor a dissimilarity measure Then, define the empirical risk (and the associated empirical risk minimization principle, as coined in Vapnik (1991)) Given a loss $\ell$ and some probabilistic space, define the optimal decision, also called Bayes decision rule And instead of the risk of a model, define the excess of risk. A classical loss for a classifier is $\ell_{0/1}$, In that case, Bayes decision rule, is$$m^\star(\boldsymbol{x}) = \boldsymbol{1}(\mu(\boldsymbol{x})>1/2) =\begin{cases}1 \text{ if }\mu(\boldsymbol{x})>1/2\\0 \text{ if }\mu(\boldsymbol{x})\leq1/2\\\end{cases}$$where (of course), one needs to know $\mu$, otherwise, we can consider some plug-in estimator based on $\widehat\mu$. For continuous variables $y$, consider the quadratic loss $\ell_2$, In that case, Bayes decision rule (the optimal model) is the conditional expectation Observe that we can also define the quantile loss (or the expectile loss) Observe that this loss is not symmetric.. #### From loss functions to distances Let us discuss a bit more the fact that losses are not distances. As mentioned, it is neither necessarily symmetric nor seperable, But furthermore, it has no reason to satisfy the triangle inequality. Actually, if $d$ is the distance, it is very likely that $d^2$ is not (since exponentiating is not a subadditive transformation) Another related concept could be the concept of similarity, or dissimilarity. Another one is the concept of divergence, that we will use much more. For instance, Bregman divergence is which safisfies desirables properties. Interestingly it is possible to define “projections” even if we have neither an orthogonal projection (since there is no orthogonal concept since there is no inner-product), nor a distance. But still One can use a nice algorithm to estimate that quantity, if the convex set can we expressed simply When considering “distances” between distributions, instead of $y$‘s, among other interesting properties in statistics, we can mention the one of unbiased gradients, and Müller (1997) defined integral probability metrics #### Standard “distances” between distributions The first one will be Hellinger distance that can lead so simple expressions for standard parametric distributions, such as Beta distributions, or (multivariate) Gaussian ones We can also mention Pearson divergence More interesting (and popular in probability theory), total variation There are several ways to express that distance. If instead of general sets $\mathcal{A}$, we can consider half lines, (-\infty,t][\latex], and we obtain Kolmogorov distance (or Kolmogorov-Smirnov) Another important one in statistics is Kullback–Leibler divergence For instance, with Gaussian vectors Observe that the measure is actually a dissimilarity measure If we want a symmetric version, we can consider Jeffreys divergence Finally, we will mention f-divergence We will discuss a little bit more those "distances" (yes, I usually use that term, abusively) and next week, we will present the most interesting distance, that will be Wasserstein's. # Talk on Fairness and Discrimination in Insurance at the Thelem-ILB Chaire This morning, I will be giving a talk for the Thelem-ILB Chaire (Thelem – historically compagnie d’assurance mutuelle contre l’incendie dans le département du Loiret – founded in 1820, one of France’s oldest companies), on fairness and discrimination in insurance, and more specifically on counterfactual fairness, and causal graphs. It is based on recent work with Olivier Côté, our PhD student in Laval (Québec), co-supervised with Marie-Pier, Slides are available online. I can mention here that we obtained a grant from the Thelem-ILB Chaire to sponsor a trip in Europe for Olivier, to attend the Insurance Data Science conference in London, and the 26th International Congress on Insurance: Mathematics and Economics in Edinburgh. # Fairness and discrimination, PhD Course, #2 Insurance and risk classes For the second course, we will get back a little bit on insurance pricing in a context of heterogeneous portfolio, and risk classification (slides are still online on the github repository). The starting point will be the pure premium. See our online textbook, with Michel Denuit, Non Life Insurance Mathematics, for additional motivation. If we have some risk related variables $\boldsymbol{x}=(x_1,\cdots,x_k)$, the pure premium will be the conditional expectation, Here also, we have some law of numbers, for the conditional expected value, This relationship, which defines the conditional expected value using the limiting value of a conditional frequency cannot be used to define properly $\mathbb{P}[Y|\boldsymbol{X}=\boldsymbol{x}]$ and $\mathbb{E}[Y|\boldsymbol{X}=\boldsymbol{x}]$. One can consider a limit,$$\mathbb{P}\big(Y\in \mathcal{A}\big\vert X = x\big)=\lim_{\epsilon\to0}\frac{\mathbb{P}(\{Y\in \mathcal{A}\}\cap\{|X -x|\leq \epsilon\})}{\mathbb{P}(\{|X -x|\leq \epsilon\})}$$or$$\mathbb{P}\big(Y\in \mathcal{A}\big\vert X = x\big)=\lim_{\epsilon\to0}\mathbb{P}\big(Y\in \mathcal{A}\big\vert |X -x|\leq \epsilon\big)$$as in the law of the unconscious statistician or as Proschan and Presnell (1998) wrote it statisticians make liberal use of conditioning arguments to shorten what would otherwise be long proofs We can now compute conditional frequency, given some risk characteristics, for some quantity of interest $y$, such as the age of death, in life insurance contracts. #### Demographic risk and heterogeneity First, we will see some gender-based life tables, starting with the one obtained by Nicolaas Struyck (see e.g. Alberts et al. (2014)) More recently, in France, some wealth based life tables were obtained, with various quantiles And finally, we will see some life tables obtained 50 years ago in the US, with racial distinction #### Mean and variance decomposition About pure premiums, an important property is the law of total expectations, and a desirable property, that we will name “balance property” We will also mention variance and variance decomposition, depending if we take into heterogeneity, or not. With homogenous pricing, we have If we use the “true” underlying risk factor, $\Theta$, we have the standard variance decomposition, also called law of total variance i.e. And finally, if we do not observe $\Theta$, but we have a collection of covariates, $\boldsymbol{X}=(X_1,\cdots,X_k)$, #### Some historical perspectives In the textbook, Insurance: Biases, Discrimination and Fairness, I have several paragraph about an historical perspective, starting with insurance as clubs, without segmentation. Then segmentation started, with risk classes and groups. For example, according to Issues And Needed Improvements In State Regulation Of The Insurance Business, by Harry Havens, in 1979, The price which a person pays for automobile insurance depends on age, sex, marital status, place of residence and other factors. This risk classification system produces widely differing prices for the same coverage for different people. Questions have been raised about the fairness of this system, and especially about its reliability as a predictor of risk for a particular individual. While we have not tried to judge the propriety of these groupings, and the resulting price differences, we believe that the questions about them warrant careful consideration by the State insurance departments. In most States the authority to examine classification plans is based on the requirement that insurance rates are neither inadequate, excessive, nor unfairly discriminatory. The only criterion for approving classifications in most States is that the classifications be statistically justified — that is, that they reasonably reflect loss experience. Relative rates with respect to age, sex, and marital status are based on the analysis of national data. A youthful male driver, for example, is charged twice as much as an older driver all over the country} (…) t has also been claimed that insurance companies engage in redlining – the arbitrary denial of insurance to everyone living in a particular neighborhood. Community groups and others have complained that State regulators have not been diligent in preventing redlining and other forms of improper discrimination that make insurance unavailable in certain areas. In addition to outright refusals to insure, geographic discrimination can include such practices as: selective placement of agents to reduce business in some areas, terminating agents and not renewing their book of business, pricing insurance at un-affordable levels, and instructing agents to avoid certain areas. We reviewed what the State insurance departments were doing in response to these problem. To determine if redlining exists, it is necessary to collect data on a geographic oasis. Such data should include current insurance policies, new policies being written, cancellations, and non-renewals. It is also important to examine data on losses by neighborhoods within existing rating territories because marked discrepancies within territories would cast doubt on the validity of territorial boundaries. Yet, not even a fifth of the States collect anything other than loss data, and that data is gathered on a territory-wide basis. According to The Role of Risk Classification in Property and Casualty Insurance: A Study of the Risk Assessment Process : Final Report, by Barbara Casey, Jacques Pezier and Carl Spetzler, in 1976, On the other hand, the opinion that distinctions based on sex, or any other group variable, necessarily violate individual rights reflects ignorance of the basic rules of logical inference in that it would arbitrarily forbid the use of relevant information. It would be equally fallacious to reject a classification system based on socially acceptable variables because the results appear discriminatory. For example, a classification system may be built on use of car, mileage, merit rating, and other variables, excluding sex. However, when verifying the average rates according to sex one may discover significant differences between males and females. Refusing to allow such differences would be attempting to distort reality by choosing to be selectively blind. The use of rating territories is a case in point. Geographical divisions, however designed, are often correlated with socio-demographic factors such as income level and race because of natural aggregation or forced segregation according to these factors. Again we conclude that insurance companies should be free to delineate territories and assess territorial differences as well as they can. At the same time, insurance companies should recognize that it is in their best interest to be objective and use clearly relevant factors to define territories lest they be accused of invidious discrimination by the public. (…) One possible standard does exist for exception to the counsel that particular rating variables should not be proscribed. What we have called equal treatment’ standard of fairness may precipitate a societal decision that the process of differentiating among individuals on the basis of certain variables is discriminatory and intolerable. This type of decision should be made on a specific, statutory basis. Once taken, it must be adhered to in private and public transactions alike and enforced by the insurance regulator. This is, in effect, a standard for conduct that by design transcends and preempts economic considerations. Because it is not applied without economic cost, however, insurance regulators and the industry should participate in and inform legislative deliberations that would ban the, use of particular rating variables as discriminatory. And then, more recently, we started to talk about personalization, as in Barry and Charpentier (2020). And next week, we will start talking about predictive modeling, and machine learning. # Creating automatically dozens of calendar notifications (with R) In a few days, we will have our annual NSERC-CRSNG meeting for grant reviews. In a nutshell (the process will be the same as last year), we get an excel file that looks like a calendar, with about 45 slots of 20 minutes, from Monday 8 am till Friday 5 pm. This year, I wanted to create automatically notifications that could get directly into my agenda. And actually, that’s easy with calendar. First, we can extract information for an excel file, or from a pdf document (which is a printed version of an excel file). First let us read the excel document library("readxl") loc = "/Users/ac/Downloads/NSERC.xlsx" data_xls = read_excel(loc) Then, I use the structure of the document: each column is a day, so I start on Monday, and then I go down, row by row. Each time I have something which looks like “RGPIN-2024-12345”, I create an ics file, with the reference name, and the appropriate time library(stringr) library(calendar) library(lubridate) ext_RGPIN = function(chr) str_extract_all(chr, "RGPIN-2024-[0-9]{4}|R[0-9]{1}")[[1]] ext_time = function(chr)strsplit(as.character(chr)," - ")[[1]][1] for(j in 2:6){ for(i in 1:nrow(data_xls)){ read_RGPIN = ext_RGPIN(data_xls[i,j]) if(!is.na(read_RGPIN[1])) { dayhour = paste("2025-02-0",j," ",ext_time(data_xls[i,1]),sep="") s <- lubridate::ymd_hm(dayhour,tz = "EST") ic = ic_event( start = s,  end = s+20*60 ,  summary = paste(read_RGPIN[1]," (",read_RGPIN[2],")",sep=""), format = "%Y-%m-%d %H:%M") ic_write(ic, paste("ic_NSERC",read_RGPIN[1],".ics",sep="")) cat(read_RGPIN[1],"...",dayhour,"\n") }}} (to illustrate, I imported those in 2025). Finally, I can import all those notifications in my agenda. # Fairness and discrimination, PhD Course, #1 Motivation This week, we will start our MAT998P course, in Montréal, entitled “équité et discrimination des modèles prédictifs“. It will mainly be based on the forthcoming textbook, I can also mention the R package > library(devtools) > install_github("freakonometrics/InsurFair")` And because it is the first course, this week, I will start with some motivations this week… First of all, let me recall a definition, from Schauer (2006) To be an actuary is to be a specialist in generalization, and actuaries engage in a form of decision making that is sometimes called actuarial. Actuaries guide insurance companies in making decisions about large categories that have the effect of attributing to the entire category certain characteristics that are probabilistically indicated by membership in the category, but that still may not be possessed by a particular member of the category. #### Motivation #1 Redlining In 1937, the HOLC (Home Owners’ Loan Corporation) produced the following map of Philadelphia, related to “residential security” These maps were related to concept of “redlining”. According to Merriam Webster dictionary, to redline is (1) to withhold home-loan funds or insurance from neighborhoods considered poor economic risks; (2) to discriminate against in housing or insurance. On the (fictitious) maps below, we have three variables, ploted • some red and green areas (risky-non risky) • some unsanitary index (on a 0-100 scale) • the proportion of Black inhabitants per neiborhood In an insurance context, risky areas (with a higher premium) should be correlated with unsanitarity index (or any risk-related variable), and those variables are legitimate predictive variables. But they can also be related to less-legitimate variable, that could be racial, here. The challenge here is that a lot of variables are correlated… I could mention here that, for Glenn (2000), insurer’s risk selection process has two sides: • the one presented to regulators and policyholders (numbers, statistics and objectivity), • the other presented to underwriters (stories, character and subjective judgment). The rhetoric of insurance exclusion – numbers, objectivity and statistics – forms what Brian Glenn calls the myth of the actuary (…) a powerful rhetorical situation in which decisions appear to be based on objectively determined criteria when they are also largely based on subjective ones (…) or the subjective nature of a seemingly objective process. Glenn (2003) claimed that there are many ways to rate accurately. Insurers can rate risks in many different ways depending on the stories they tell on which characteristics are important and which are not. The fact that the selection of risk factors is subjective and contingent upon narratives of risk and responsibility has in the past played a far larger role than whether or not someone with a wood stove is charged higher premiums (…) virtually every aspect of the insurance industry is predicated on stories first and then numbers #### Motivation #2. “Gender directive”, 2004/113/EC From the Treaty on European Union (26.10.2012) Art. 2 The Union is founded on the values of respect for human dignity, freedom, democracy, equality, the rule of law and respect for human rights, including the rights of persons belonging to minorities. These values are common to the Member States in a society in which pluralism, non-discrimination, tolerance, justice, solidarity and equality between women and men prevail. Art. 3 (…) It shall combat social exclusion and discrimination, and shall promote social justice and protection, equality between women and men, solidarity between generations and protection of the rights of the child. from the Charter of Fundamental Rights of the European Union (18.12.2000) Art. 21 (Non discrimination): Any discrimination based on any ground such as sex, race, colour, ethnic or social origin, genetic features, language, religion or belief, political or any other opinion, membership of a national minority, property, birth, disability, age or sexual orientation shall be prohibited. Art. 23 (Equality between men and women) Equality between men and women must be ensured in all areas, including employment, work and pay. The principle of equality shall not prevent the maintenance or adoption of measures providing for specific advantages in favour of the under-represented sex. and from the EU Directive 2004/113/EC (2004 version) Art. 5 (Actuarial factors) 1. Member States shall ensure that in all new contracts concluded after 21 December 2007 at the latest, the use of sex as a factor in the calculation of premiums and benefits for the purposes of insurance and related financial services shall not result in differences in individuals’ premiums and benefits. 2. Notwithstanding paragraph 1, Member States may decide before 21 December 2007 to permit proportionate differences in individuals’ premiums and benefits where the use of sex is a determining factor in the assessment of risk based on relevant and accurate actuarial and statistical data. The Member States concerned shall inform the Commission and ensure that accurate data relevant to the use of sex as a determining actuarial factor are compiled, published and regularly updated. There was initially (2004) an opt-out clause (Article 5(2)), since, where gender is a determining factor in the assessment of risk based on relevant and accurate actuarial and statistical data then proportionate differences in individual premiums or benefits are allowed. In March 2011, the European Court of Justice issued its judgement into the “Test-Achats case”. The ECJ ruled Article 5(2) was invalid. Thus, insurers were no longer able to use gender as a risk factor when pricing policies. Other legal documents in Europe can be mentioned such that the “Ten Oever” judgement (Gerardus Cornelis Ten Oever v Stichting Bedrijfspensioenfonds voor het Glazenwassers — en Schoonmaakbedrijf). In April 1993, the Advocate General Vangerven argued that (see De Baere (2012)) the fact that women generally live longer than men has no significance at all for the life expectancy of a specific individual and it is not acceptable for an individual to be penalized on account of assumptions which are not certain to be true in his specific case, which could be related to the concept of “injustice by generalization”. #### Motivation #3. Colorado (September 27, 2023) In September 27, 2023, the Colorado Division of Insurance exposed a new proposed regulation entitled Concerning Quantitative Testing of External Consumer Data and Information Sources, Algorithms, and Predictive Models Used for Life Insurance Underwriting for Unfairly Discriminatory Outcomes. Section 4 (Definitions) Bayesian Improved First Name Surname Geocoding, or “BIFSG” means, for the purposes of this regulation, the statistical methodology developed by the RAND corporation for estimating race and ethnicity. External Consumer Data and Information Source, or “ECDIS” means, for the purposes of this regulation, a data source or an information source that is used by a life insurer to supplement or supplant traditional underwriting factors. This term includes credit scores, credit history, social media habits, purchasing habits, home ownership, educational attainment, licensures, civil judgments, court records, occupation that does not have a direct relationship to mortality, morbidity or longevity risk, consumer-generated Internet of Things data, biometric data, and any insurance risk scores derived by the insurer or third-party from the above listed or similar data and/or information source. Then we have different sections, where insurers are asked to “estimate” the race or ethnicity of policyholders Section 5 (Estimating Race and Ethnicity) : Insurers shall estimate the race or ethnicity of all proposed insureds that have applied for coverage on or after the insurer’s initial adoption of the use of ECDIS, or algorithms and predictive models that use ECDIS, including a third party acting on behalf of the insurer that used ECDIS, or algorithms and predictive models that used ECDIS, in the underwriting decision-making process, by utilizing: 1. BIFSG and the insureds’ or proposed insureds’ name and geolocation information included in the applications) for life insurance shall be used to estimate the race and ethnicity of each insured or proposed insured. 2. For the purposes of BIFSG, the following racial and ethnic categories shall be used: Hispanic, Black, Asian Pacific Islander (API), and White. Section 6 (Application Approval Decision Testing Requirements) : Using the BIFSG estimated race and ethnicity of proposed insureds and the following methodology, insurers shall calculate whether Hispanic, Black, and API proposed insureds are disapproved at a statistically significant different rate relative to White applicants for whom the insurer, or a third party acting on behalf of the insurer, used ECDIS, or an algorithm or predictive model that used ECDIS, in the underwriting decision-making process. 1. Logistic regression shall be used to model the binary underwriting outcome of either approved or denied. 2. The following factors may be accounted for as control variables in the regression model: policy type, face amount, age, gender, and tobacco use. 3. The estimated race or ethnicity of the proposed insureds shall be accounted for by including Hispanic, Black, and Asian Pacific Islander (API) as separate dummy variables in the regression model. 4. Determine if there is a statistically significant difference in approval rates for each BIFSG estimated race or ethnicity variable as indicated by a p-value of less than .05. a. If there is not a statistically significant difference in approval rates, no further testing is required. b. If there is a statistically significant difference in approval rates, the insurer shall determine whether the difference in approval rates is five (5) percentage points or greater as indicated by the marginal effects value of each BIFSG estimated race or ethnicity variable. (…) or Section 7 (Premium Rate Testing Requirements) : Using the insureds’ BIFSG estimated race and ethnicity, insurers shall determine if there is a statistically significant difference in the premium rate per$1,000 of face amount for policies issued to Hispanic, Black, and API insureds relative to White insureds for whom the insurer, or a third party acting on behalf of the insurer, used ECDIS, or an algorithm or predictive model that used ECDIS, in the underwriting decision-making process.

1. Linear regression shall be used to model the continuous numerical outcome of premium rate per $1,000 of face amount. 2. The following factors may be accounted for as control variables in the regression model: policy type, face amount, age, gender, and tobacco use. 3. The estimated race or ethnicity of the proposed insureds shall be accounted for by including Hispanic, Black, and Asian Pacific Islander (API) as separate dummy variables in the regression model. 4. Determine if there is a statistically significant difference in the premium rate per$1,000 of face amount for each BIFSG estimated race or ethnicity variable as indicated by a p-value of less than .05.

a. If there is not a statistically significant difference in premium rate per $1,000 of face amount, no further testing is required. b. If there is a statistically significant difference in premium rate per$1,000 of face amount, determine whether the premium rate per $1,000 of face amount is at least 5% more than the average premium rate per$1,000 for all policies.

i. If the difference in premium rate per $1,000 of face amount is less than 5%, no further testing is required. ii. If the difference in premium rate per$1,000 of face amount is 5% or greater, further testing is required as described in Section 8.

(etc). In order to illustrate, we can use some data, in the region of Atlanta

We can change the first and last name of people (and keep other relevant information, including the ZIP code) and compare “predictions” of race (white, black, hispanic, asian, etc)

#### Motivation #4. Motor Insurance in the U.S.

In the context of motor insurance in the U.S., recall that legal restrictions are per states, and we can observe some diversity about what “sensitive” could mean (via thezebra)

(etc). We will also discuss Avraham et al. (2013) that provides a long discussion accross US states.

Another motivation is the popular article, Bickel,  Hammel, and O’Connell (1975)

The dataset mentioned in the article is the following

the bias in the aggregated data stems not from any pattern of discrimination on the part of admissions committees, which seems quite fair on the whole, but apparently from prior screening at earlier levels of the educational system. Women are shunted by their socialization and education toward fields of graduate study that are generally more crowded, less productive of completed degrees, and less well funded, and that frequently offer poorer professional employment prospects

As we can see, if we formalize, we have (almost)

This is Simpson’s paradox. Another simple example is related to mortality : the (overall) mortality rate for women (picked at random in the entiere population) was 0.812% in Costa Rica, lower than 0.929% in Sweden. But as we can see on the left, below, at any age, mortality rates are lower in Sweden than in Costa Rica.

The paradox can easily be explained if we look at age structures in both countries. Long story short, in Costa Rica, picking someone randomly means that the person is very likely to be (very) young, with a low mortality rate; in Sweden, the person is more likely to be older, with a higher mortality rate.

#### Motivation #6. Propublica, Actuarial Justice

We will also mention actuarial justice, and et al (2016)

Hence, looking at the same data, with difference perspective, could lead to different conclusions. More robust conclusions can be obtained when look at distributions of scores (instead of simple binary predictions)

and we can also consider temporal process (again, instead of simply binary variables, with temporal censoring)

#### Motivation #7. Insurance in Québec

Two final motivations, in French this time. In Québec, there is the Charte des droits et libertés de la personne (C-12) with some very clear definition of what “discrimination” means,

Art. 10  Toute personne a droit à la reconnaissance et à l’exercice, en pleine égalité, des droits et libertés de la personne, sans distinction, exclusion ou préférence fondée sur la race, la couleur, le sexe, l’identité ou l’expression de genre, la grossesse, l’orientation sexuelle, l’état civil, l’âge sauf dans la mesure prévue par la loi, la religion, les convictions politiques, la langue, l’origine ethnique ou nationale, la condition sociale, le handicap ou l’utilisation d’un moyen pour pallier ce handicap.

Il y a discrimination lorsqu’une telle distinction, exclusion ou préférence a pour effet de détruire ou de compromettre ce droit.

But, interestingly, insurers can almost do anything they want,

Art 20.1 Dans un contrat d’assurance ou de rente, un régime d’avantages sociaux, de retraite, de rentes ou d’assurance ou un régime universel de rentes ou d’assurance, une distinction, exclusion ou préférence fondée sur l’âge, le sexe ou l’état civil est réputée non discriminatoire lorsque son utilisation est légitime et que le motif qui la fonde constitue un facteur de détermination de risque, basé sur des données actuarielles.

#### Motivation #8. Intention

And finally, I can mention that in many countries (such as France), “indirect discrimination” is considered as discriminatory, so “intention” has nothing to do with the problem… The Loi no 2008-496 du 27 mai 2008 states that

Art. 1 Constitue une discrimination indirecte une disposition, un critère ou une pratique neutre en apparence, mais susceptible d’entraîner, pour l’un des motifs mentionnés au premier alinéa, un désavantage particulier pour des personnes par rapport à d’autres personnes, à moins que cette disposition, ce critère ou cette pratique ne soit objectivement justifié par un but légitime et que les moyens pour réaliser ce but ne soient nécessaires et appropriés.

This law is an extension of Loi no. 72-546 du 1er juillet 1972, which abolished the requirement for specific intent.

Again, following Avraham (2017), keep in mind that insurance is very specific, regarding discrimination

What is unique about insurance is that even statistical discrimination which by definition is absent of any malicious intentions, poses significant moral and legal challenges. Why? Because on the one hand, policy makers would like insurers to treat their insureds equally, without discriminating based on race, gender, age, or other characteristics, even if it makes statistical sense to discriminate (…) On the other hand, at the core of insurance business lies discrimination between risky and non-risky insureds. But riskiness often statistically correlates with the same characteristics policy makers would like to prohibit insurers from taking into account.

That will be the topic of the course…

# Le Manuel d’Assurance arrive en format électronique !

Plusieurs mois après la sortie de la première édition, l’édition électronique du Manuel d’Assurance est enfin disponible !