A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). ACM Computing Surveys (CSUR).
Abstract Preview: This survey provides a taxonomy of various fairness definitions and bias types, providing researchers with a structured way to understand the landscape of algorithmic bias.
Weapons of Math Destruction
O'Neil, C. (2016). Crown Publishing Group.
Abstract Preview: Explores how Big Data and algorithms can reinforce discrimination and undermine democracy when they are opaque, unaccountable, and applied at scale.
European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation"
Goodman, B., & Flaxman, S. (2017). AI Magazine.
Abstract Preview: Discusses the implications of the GDPR on automated individual decision-making and the legal challenges of providing human-interpretable explanations.
Quick Citation
@article{mehrabi2021survey,
title={A survey on bias and fairness in machine learning},
author={Mehrabi, Ninareh and others},
journal={ACM Computing Surveys (CSUR)},
year={2021}
}
(Mehrabi et al., 2021)