About
Bias detection is the task of detecting and measuring racism, sexism and otherwise discriminatory behavior in a model (Source: https://stereoset.mit.edu/)
Benchmarks
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Subtasks
Datasets
Greatest papers with code
fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation
The package includes a series of methods for bias mitigation that aim to diminish the discrimination in the model.
StereoSet: Measuring stereotypical bias in pretrained language models
Since pretrained language models are trained on large real world data, they are known to capture stereotypical biases.
Ranked #1 on
Bias Detection
on StereoSet
Towards explainable classifiers using the counterfactual approach -- global explanations for discovering bias in data
The paper proposes summarized attribution-based post-hoc explanations for the detection and identification of bias in data.
LOGAN: Local Group Bias Detection by Clustering
Machine learning techniques have been widely used in natural language processing (NLP).
Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word Categories
We find that some domains are definitely more prone to gender bias than others, and that the categories of gender bias present also vary for each set of word embeddings.
BIAS DETECTION GENDER BIAS DETECTION TWO-SAMPLE TESTING WORD EMBEDDINGS
Towards Detection of Subjective Bias using Contextualized Word Embeddings
Subjective bias detection is critical for applications like propaganda detection, content recommendation, sentiment analysis, and bias neutralization.
Ranked #1 on
Bias Detection
on Wiki Neutrality Corpus
BIAS DETECTION PROPAGANDA DETECTION SENTIMENT ANALYSIS WORD EMBEDDINGS
Context in Informational Bias Detection
• vdenberg/context-in-informational-bias-detection
• 
We find that the best-performing context-inclusive model outperforms the baseline on longer sentences, and sentences from politically centrist articles.
Automated Dependence Plots
• davidinouye/automated-dependence-plots
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To address these drawbacks, we formalize a method for automating the selection of interesting PDPs and extend PDPs beyond showing single features to show the model response along arbitrary directions, for example in raw feature space or a latent space arising from some generative model.
Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information
Our analysis shows that the use of acoustic signal helped to improve bias detection by more than 6% absolute over using text and metadata only.
Multilingual sentence-level bias detection in Wikipedia
We propose a multilingual method for the extraction of biased sentences from Wikipedia, and use it to create corpora in Bulgarian, French and English.