This project addresses the bias present in Machine Learning algorithms. Existing algorithms using preprocessing, in-processing and post-processing have been used. These algorithms are also paired and tested in an emsemble architecture.
This project uses the following algorithms for experimentation:
- Disparate Impact Remover (pre-processing)
- Exponentiated Gradient Reduction (in-processing)
- Equalized odds (post-processing)
The data following datasets have been used for analysis: