Project 5: Fairness in Machine Learning

One of the most crucial aspects to consider while working on Machine Learning models is how bias and fairness in various stages can affect outcomes for different user groups. This especially becomes more important in the context of AI in healthcare, where patients’ privacy should be treated with utmost care. In this project, we intend to develop interpretable machine learning models while identifying and reducing sources of bias in the data and using features through methods such as aggregation and proper evaluation among different demographic groups.

We prioritize equity, diversity, and inclusion to develop trustworthy ML algorithms for medical imaging applications. We expect to present reliable and non-biased results for equity deserving groups, male and female participants of any race, all age ranges in the adult lifespan, and people with varying educational backgrounds. Our work can be translated to datasets other than the one we used during development, such as multi-site heterogeneous data, simulating larger clinical trials. We intend to make code available, making our findings reproducible and providing straightforward benchmarking.