Bias, Fairness, and Accountability with AI and ML Algorithms

Autor:	Zhou, Nengfeng, Zhang, Zach, Nair, Vijayan N., Singhal, Harsh, Chen, Jie, Sudjianto, Agus
Rok vydání:	2021
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 18 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.06558 Zobrazit plný text záznamu View this record from Arxiv