Learn Before
Avoiding Harms
It is important to avoid harms that may result from classifiers.
Censorship harms: like in toxicity detection, false-positive errors could lead to the censoring of discourse about certain groups, like gay people and blind people.
0
1
Tags
Data Science
Related
Bayes Theorem in Machine Learning
Bayes Formula
Article: Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection
Bayes Error Rate for (Naive) Bayes Classifier
Statistical Significance Tests for Naive Bayes Classifier
Naive Bayes for other text classification tasks
Types of Naive Bayes Classifiers
Naïve Bayes as a Language Model
Training the Naive Bayes Classifier
Advantages and Disadvantages of Naive Bayes Classifier
Related
Bayes Theorem in Machine Learning
Bayes Formula
Article: Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection
Bayes Error Rate for (Naive) Bayes Classifier
Statistical Significance Tests for Naive Bayes Classifier
Naive Bayes for other text classification tasks
Types of Naive Bayes Classifiers
Naïve Bayes as a Language Model
Training the Naive Bayes Classifier
Advantages and Disadvantages of Naive Bayes Classifier