A Julia package for modelling Algorithmic Recourse Dynamics.
Author JuliaTrustworthyAI
4 Stars
Updated Last
5 Months Ago
Started In
November 2021

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AlgorithmicRecourseDynamics.jl is a small package for modeling Algorithmic Recourse Dynamics. It builds on CounterfactualExplanations, a package for generating counterfactual explanations.

Basic Usage

Below we first generate some synthetic data for a binary classification task and instantiate an instance of CounterfactualData.

N = 1000
xmax = 2
X, ys = make_blobs(
    N, 2; 
    centers=2, as_table=false, center_box=(-xmax => xmax), cluster_std=0.1
ys .= ys.==2
X = X'
counterfactual_data = CounterfactualData(X,ys')

We then define a simple model for the task and prepare it for use with CounterfactualExplanations.jl:

n_epochs = 100
model = Chain(Dense(2,1))
mod = FluxModel(model)

To generate algorithmic recourse, we will use a simple generic generator:

generator = GenericGenerator()

Finally we train our model on a subset of the data. The chart below shows the results.

data_train, data_test = Data.train_test_split(counterfactual_data)
CounterfactualExplanations.Models.train(mod, data_train; n_epochs=n_epochs)
plt_original = plot(mod, counterfactual_data; zoom=0, colorbar=false)


To model the dynamics of algorithmic recourse, we use simulations, in which we repeatedly select as subset of individuals from the non-target class, generate and implement recourse for all of them and finally retrain the model. To set this experiment up, we can use the code below:

models = Dict(:mymodel => mod)
generators = Dict(:wachter => generator)
experiment = set_up_experiment(data_train, data_test, models, generators)

Finally, we just run the experiment using default parameter settings that specify the number of rounds, the proportion of individuals to select for recourse and related aspects:


The chart below shows the data and predictions at the end of the simulation:

new_data = experiment.recourse_systems[1][1].data
new_model = experiment.recourse_systems[1][1].model
plt_original = plot(new_model, new_data; zoom=0, colorbar=false)

Related Research Paper 📝

The package was developed for a research project that investigates the dynamics of various counterfactual generators. You can find the details here.