Search Results for author:
Found 7 papers, 5 papers with code
When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors
Hence, there are often limits on how much-labeled data is available for training.
Fair-SSL: Building fair ML Software with less data
Semi-supervised learning is a machine learning technique where, incrementally, labeled data is used to generate pseudo-labels for the rest of the data (and then all that data is used for model training).
Fair Enough: Searching for Sufficient Measures of Fairness
In summary, to simplify the fairness testing problem, we recommend the following steps: (1)~determine what type of fairness is desirable (and we offer a handful of such types); then (2) lookup those types in our clusters; then (3) just test for one item per cluster.
FairMask: Better Fairness via Model-based Rebalancing of Protected Attributes
Our approach aims to offset the biased predictions of the classification model via rebalancing the distribution of protected attributes.
FairBalance: How to Achieve Equalized Odds With Data Pre-processing
We found that equalizing the class distribution in each demographic group with sample weights is a necessary condition for achieving equalized odds without modifying the normal training process.
Bias in Machine Learning Software: Why? How? What to do?
This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those examples.
Software Engineering for Fairness: A Case Study with Hyperparameter Optimization
To the best of our knowledge, this is the first application of hyperparameter optimization as a tool for software engineers to generate fairer software.
