Extension usage examples:
Here's how our browser extension sees the article:
Noise-tolerant fair classification
Source: proceedings.neurips.cc
Article summary:
1. This paper presents a method for learning fair classifiers when the sensitive feature available in one's training sample is subject to noise.
2. The mean-difference score is used to measure fairness, and the required tolerance can be estimated using existing noise-rate estimators from the label noise literature.
3. The proposed procedure has been empirically effective on two case studies involving sensitive feature censoring.
Article analysis:
The article appears to be reliable and trustworthy overall, as it provides a detailed description of the proposed method for learning fair classifiers when the sensitive feature available in one's training sample is subject to noise. The authors provide evidence for their claims by citing existing work from the label noise literature and demonstrating empirical effectiveness on two case studies involving sensitive feature censoring.
The article does not appear to have any major biases or unsupported claims, as all of its claims are backed up with evidence from existing research and empirical results. Additionally, there does not seem to be any promotional content or partiality present in the article, as it objectively presents both sides of the argument without favoring either side. Furthermore, possible risks associated with using this method are noted throughout the article, which further adds to its trustworthiness and reliability.
In conclusion, this article appears to be reliable and trustworthy overall, as it provides a detailed description of its proposed method while also providing evidence for its claims and noting potential risks associated with using this method.