A Survey on Bias and Fairness in Machine Learning.¶
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 1-35.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Bias
- Mehrabi and colleagues (2021) survey how such biases enter learning pipelines at data collection, labeling, and modeling stages and propagate into systematically skewed outputs.
This sourceSurvey of how model and dataset bias enter learning pipelines at data collection, labeling, and modeling stages and propagate into reproducibly skewed outputs, including institutional and algorithmic bias as directional displacement relative to a fairness or representational target.
- Mehrabi and colleagues (2021) survey how such biases enter learning pipelines at data collection, labeling, and modeling stages and propagate into systematically skewed outputs.
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