Corpus ID: 201666566

A Survey on Bias and Fairness in Machine Learning

@article{Mehrabi2019ASO,
  title={A Survey on Bias and Fairness in Machine Learning},
  author={Ninareh Mehrabi and Fred Morstatter and Nripsuta Saxena and Kristina Lerman and A. Galstyan},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.09635}
}
  • Ninareh Mehrabi, Fred Morstatter, +2 authors A. Galstyan
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
  • With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. Such systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that the decisions do not reflect discriminatory behavior toward certain groups or populations. We have recently seen work in machine learning, natural language processing… CONTINUE READING
    Robust Fairness under Covariate Shift
    Data, Power and Bias in Artificial Intelligence
    On the Applicability of ML Fairness Notions
    Fair Classification with Counterfactual Learning
    On Adversarial Bias and the Robustness of Fair Machine Learning
    Towards Threshold Invariant Fair Classification

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 155 REFERENCES
    Fairness in Relational Domains
    • 7
    • PDF
    The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making
    • 46
    • PDF
    Learning Optimal Fair Policies
    • 17
    • PDF
    Fairness-Aware Classifier with Prejudice Remover Regularizer
    • 278
    • Highly Influential
    • PDF
    Delayed Impact of Fair Machine Learning
    • 143
    • PDF
    50 Years of Test (Un)fairness: Lessons for Machine Learning
    • 54
    • PDF
    Fairness Constraints: Mechanisms for Fair Classification
    • 396
    • PDF
    Fairness without Harm: Decoupled Classifiers with Preference Guarantees
    • 21
    • PDF
    Aequitas: A Bias and Fairness Audit Toolkit
    • 31
    • PDF