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Found 12 papers, 2 papers with code
Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models
We survey the literature on fairness metrics for pre-trained language models and experimentally evaluate compatibility, including both biases in language models and in their downstream tasks.
How to be fair? A study of label and selection bias
Recently, Wick et al. showed, with experiments on synthetic data, that there exist situations in which bias mitigation techniques lead to more accurate models when measured on unbiased data.
Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics
Artificial Intelligence (AI) finds widespread applications across various domains, sparking concerns about fairness in its deployment.
Model-based Counterfactual Generator for Gender Bias Mitigation
Counterfactual Data Augmentation (CDA) has been one of the preferred techniques for mitigating gender bias in natural language models.
How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classification
Considering that the end use of these language models is for downstream tasks like text classification, it is important to understand how these intrinsic bias mitigation strategies actually translate to fairness in downstream tasks and the extent of this.
Text Style Transfer for Bias Mitigation using Masked Language Modeling
Our style transfer model improves on the limitations of many existing style transfer techniques such as loss of content information.
Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Models
We survey the existing literature on fairness metrics for pretrained language models and experimentally evaluate compatibility, including both biases in language models as in their downstream tasks.
OSACT4 Shared Tasks: Ensembled Stacked Classification for Offensive and Hate Speech in Arabic Tweets
In this paper, we describe our submission for the OCAST4 2020 shared tasks on offensive language and hate speech detection in the Arabic language.
Finding Robust Itemsets Under Subsampling
We do this by measuring the robustness of a property of an itemset such as closedness or non-derivability.
Detecting and Explaining Drifts in Yearly Grant Applications
These scores can be used to detect outlying cases and concept drift.
Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs
Checking various log files from different processes can be a tedious task as these logs contain lots of events, each with a (possibly large) number of attributes.
