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Found 10 papers, 2 papers with code
FRAPPÉ: A Group Fairness Framework for Post-Processing Everything
Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited computation resources or no access to the training pipeline of the prediction model.
Causally motivated Shortcut Removal Using Auxiliary Labels
Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning.
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches.
Can We Improve Model Robustness through Secondary Attribute Counterfactuals?
By implementing RDI in the context of toxicity detection, we find that accounting for secondary attributes can significantly improve robustness, with improvements in sliced accuracy on the original dataset up to 7% compared to existing robustness methods.
Flexible text generation for counterfactual fairness probing
A common approach for testing fairness issues in text-based classifiers is through the use of counterfactuals: does the classifier output change if a sensitive attribute in the input is changed?
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
We reconcile these notions and show that the tension is due to differences in distributions of users where items are relevant, and break down the important factors of the user's recommendations.
Striving for data-model efficiency: Identifying data externalities on group performance
Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predictive performance.
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals
We demonstrate that with a small amount of human-annotated counterfactual data (10%), we can generate a counterfactual augmentation dataset with learned labels, that provides an 18-20% improvement in robustness and a 14-21% reduction in errors on 6 out-of-domain datasets, comparable to that of a fully human-annotated counterfactual dataset for both sentiment classification and question paraphrase tasks.
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification
Despite the rich literature on machine learning fairness, relatively little attention has been paid to remediating complex systems, where the final prediction is the combination of multiple classifiers and where multiple groups are present.
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting
A crucial challenge for generative large language models (LLMs) is diversity: when a user's prompt is under-specified, models may follow implicit assumptions while generating a response, which may result in homogenization of the responses, as well as certain demographic groups being under-represented or even erased from the generated responses.
