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arXiv · 2105.06558

Bias, Fairness, and Accountability with AI and ML Algorithms

Nengfeng Zhou·Zach Zhang·Vijayan N. Nair·Harsh Singhal·Jie Chen·Agus Sudjianto
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Abstract

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.

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Nengfeng Zhou, Zach Zhang, Vijayan N. Nair, Harsh Singhal, Jie Chen, Agus Sudjianto. 2021-05-13. Bias, Fairness, and Accountability with AI and ML Algorithms. https://arxiv.org/abs/2105.06558

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