Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Data

@article{Chaudhari2022SimultaneousIO,
  title={Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Data},
  author={B. Chaudhari and Akash Agarwal and Tanmoy Bhowmik},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.13182}
}
Machine learning models built on datasets containing discriminative instances attributed to various underlying factors result in biased and unfair outcomes. It’s a well founded and intuitive fact that existing bias mitigation strategies often sacrifice accuracy in order to ensure fairness. But when AI engine’s prediction is used for decision making which reflects on revenue or operational efficiency such as credit risk modeling, it would be desirable by the business if accuracy can be somehow… 
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