Implementation and evaluation of provably Rawlsian fair ML algorithms for contextual bandits.
Related Work/Citations:
- Rawlsian Fairness for Machine Learning (https://arxiv.org/abs/1610.09559)
- Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms (https://arxiv.org/abs/1003.5956)
Install Python 3 from package. This allows you to run python3 and pip3. Software is installed into /Library/Frameworks/Python.framework/Versions/3.x/bin/.
Install virtualenv for Python 3 for the user only (which is placed into ~/Library/Python/3.x/bin):
$ pip3 install --user virtualenv
Create the following alias in your ~/.bash_profile:
$ echo "alias virtualenv3='~/Library/Python/3.x/bin/virtualenv'" >> ~/.bash_profile
Create a local virtualenv and activate it:
$ virtualenv3 fairml
$ source fairml/bin/activate
With the virtualenv active, install the project requirements into your virtualenv:
$ pip install -r requirements.txt
Create a Python kernel for Jupyter that uses your virtualenv:
$ python -m ipykernel install --user --name=fairml
You can then launch Jupyter using jupyter notebook from inside the project directory and change the kernel to fairml.
You can install Docker and use a standard configuration such as all-spark-notebook to run the project files.