Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms
Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms
Nengfeng Zhou,Zach Zhang,2 作者,Jie Chen
TLDR
The types and sources of data bias and the nature of algorithmic unfairness are described, and a review of fairness metrics in the literature is provided, to discuss their limitations and describe de‐biasing techniques in the model life cycle.
摘要
The advent of artificial intelligence (AI) and machine learning algorithms has led to opportunities as well as challenges in their use. In this overview paper, we begin with a discussion of bias and fairness issues that arise with the use of AI techniques, with a focus on supervised machine learning algorithms. We then describe the types and sources of data bias and discuss the nature of algorithmic unfairness. In addition, we provide a review of fairness metrics in the literature, discuss their limitations, and describe de‐biasing (or mitigation) techniques in the model life cycle.