The advent of AI and ML algorithms has led to opportunities as well as challenges. In this paper, we provide an overview of bias and fairness issues that arise with the use of ML algorithms. We describe the types and sources of data bias, and discuss the nature of algorithmic unfairness. This is followed by a review of fairness metrics in the literature, discussion of their limitations, and a description of de-biasing (or mitigation) techniques in the model life cycle.
@article{arxiv.2105.06558,
title = {Bias, Fairness, and Accountability with AI and ML Algorithms},
author = {Nengfeng Zhou and Zach Zhang and Vijayan N. Nair and Harsh Singhal and Jie Chen and Agus Sudjianto},
journal= {arXiv preprint arXiv:2105.06558},
year = {2021}
}