Fairness and discrimination, PhD Course, #8 Individual fairness

After our post on “group fairness“, it’s time to discuss so-called “individual fairness“.

Similarity

The first idea is discussed in Dwork et al. (2012)

our approach is centered around the notion of a task-specific similarity metric describing the extent to which pairs of individuals should be regarded as similar for the classification task at hand. The similarity metric expresses ground truth. When ground truth is unavailable, the metric may reflect the “best” available approximation as agreed upon by society. Following established tradition – Rawls (1971) – the metric is assumed to be public and open to discussion and continual refinement. Indeed, we envision that, typically, the distance metric would be externally imposed, for example, by a regulatory body or externally proposed by a civil rights organization

or

Counterfactual fairness

The second one is related to causal inference. Ensuring fairness using causal methods will produce “counterfactual fairness” (to use the term introduced in Kusner et al. (2017)), based on the idea a decision is fair towards an individual if the outcome is the same in reality as it would be in a ‘counterfactual’ world, in which the individual belongs to the other group (with respect to the sensitive attribute).

Quite naturally, we should compare potential outcomes, either globally (average treatement effect) or a local version, conditional on characteristics $\boldsymbol{x}$ of an individual.

Based on causal graphs (discussed previously) we can define several notions of individual fairness.

Hence, it is possible to use Plečko et al. (2021), based on transport, and quantile regressions,

To illustrate, we can consider some causal graph on our toy dataset

and then, on some specific individuals in the dataset

Here, we can also get a counterfactual version of all individuals with one-to-one matching, and optimal transport

i.e.

and we can get a counterfactual version, and possibly, a different prediction, using the fairadapt R package

We can also consider the German credit dataset

or the causal graph used in Watson et al. (2021),

Then, those techniques can be used to see compare the predictions of 6 fictious individuals,