Flexible tool for bias detection, visualization, and mitigation. Use models explained with DALEX and calculate fairness classification metrics based on confusion matrices using `fairness_check()`

or try newly developed module for regression models using `fairness_check_regression()`

. R package fairmodels allows to compare and gain information about various machine learning models. Mitigate bias with various pre-processing and post-processing techniques. *Make sure your models are classifying protected groups similarly*.

Install it from CRAN:

`install.packages("fairmodels")`

or developer version from GitHub:

`devtools::install_github("ModelOriented/fairmodels")`

Checking fairness is easy!

```
library(fairmodels)
library(ranger)
library(DALEX)
data("german")
# ------------ step 1 - create model(s) -----------------
lm_model <- glm(Risk~.,
data = german,
family=binomial(link="logit"))
rf_model <- ranger(Risk ~.,
data = german,
probability = TRUE,
num.trees = 200)
# ------------ step 2 - create explainer(s) ------------
# numeric y for explain function
y_numeric <- as.numeric(german$Risk) -1
explainer_lm <- explain(lm_model, data = german[,-1], y = y_numeric)
explainer_rf <- explain(rf_model, data = german[,-1], y = y_numeric)
# ------------ step 3 - fairness check -----------------
fobject <- fairness_check(explainer_lm, explainer_rf,
protected = german$Sex,
privileged = "male")
print(fobject)
plot(fobject)
```

Compas recidivism data use case: Basic tutorial

Bias mitigation techniques on Adult data: Advanced tutorial

`fairness_check`

parameters are

* x, … - `explainers`

and `fairness_objects`

(products of fairness_check).

* protected - factor with different subgroups as levels. Usually specific race, sex etc…

* privileged - subgroup, base on which to calculate parity loss metrics.

* cutoff - custom cutoff, might be single value - cutoff same for all subgroups or vector - for each subgroup individually. Affecting only explainers.

* label - character vector for every explainer.

Models might be trained on different data, even without protected variable. May have different cutoffs which gives different values of metrics. `fairness_check()`

is place where `explainers`

and `fairness_objects`

are checked for compatibility and then glued together.

So it is possible to to something like this:

```
fairness_object <- fairness_check(explainer1, explainer2, ...)
fairness_object <- fairness_check(explainer3, explainer4, fairness_object, ...)
```

even with more `fairness_objects`

!

If one is even more keen to know how `fairmodels`

works and what are relations between objects, please look at this diagram class diagram

There are 12 metrics based on confusion matrix :

Metric | Formula | Full name | fairness names while checking among subgroups |
---|---|---|---|

TPR | true positive rate | equal opportunity | |

TNR | true negative rate | ||

PPV | positive predictive value | predictive parity | |

NPV | negative predictive value | ||

FNR | false negative rate | ||

FPR | false positive rate | predictive equality | |

FDR | false discovery rate | ||

FOR | false omission rate | ||

TS | threat score | ||

STP | statistical parity | statistical parity | |

ACC | accuracy | Overall accuracy equality | |

F1 | F1 score |

*and their parity loss.*

How is *parity loss* calculated?

Where `i`

denotes the membership to unique subgroup from protected variable. Unprivileged subgroups are represented by small letters and privileged by simply “privileged”.

some fairness metrics like *Equalized odds* are satisfied if parity loss in both *TPR* and *FPR* is low

R package fairmodels has support for regression models. Check fairness using `fairness_check_regression()`

to approximate classification fairness metrics in regression setting. Plot object with `plot()`

to visualize *fairness check* or with `plot_density()`

to see model’s output.