Human Comprehension of Fairness in Machine Learning

@article{Saha2020HumanCO,
  title={Human Comprehension of Fairness in Machine Learning},
  author={Debjani Saha and Candice Schumann and Duncan C. McElfresh and John P. Dickerson and Michelle L. Mazurek and Michael Carl Tschantz},
  journal={Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
  year={2020}
}
Bias in machine learning has manifested injustice in several areas, with notable examples including gender bias in job-related ads [4], racial bias in evaluating names on resumes [3], and racial bias in predicting criminal recidivism [1]. In response, research into algorithmic fairness has grown in both importance and volume over the past few years. Different metrics and approaches to algorithmic fairness have been proposed, many of which are based on prior legal and philosophical concepts [2… 

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