Decisions made by machine learning models may have lasting impacts over ...
Despite recent progress in reinforcement learning (RL) from raw pixel da...
Auditing machine learning-based (ML) healthcare tools for bias is critic...
Large pre-trained language models have shown remarkable performance over...
A common limitation of diagnostic tests for detecting social biases in N...
Disinformation has become a serious problem on social media. In particul...
Natural Language Processing (NLP) models propagate social biases about
p...
Is it possible to use natural language to intervene in a model's behavio...
Robustness and counterfactual bias are usually evaluated on a test datas...
Machine learning techniques have been widely used in natural language
pr...
There has been growing attention on fairness considerations recently,
es...
Advanced machine learning techniques have boosted the performance of nat...
Multilingual representations embed words from many languages into a sing...
Humor plays an important role in human languages and it is essential to ...
Recent developments in Neural Relation Extraction (NRE) have made signif...
Recent studies have shown that word embeddings exhibit gender bias inher...
As Natural Language Processing (NLP) and Machine Learning (ML) tools ris...
In this paper, we quantify, analyze and mitigate gender bias exhibited i...
In this work we analyze visual recognition tasks such as object and acti...
Word embedding models have become a fundamental component in a wide rang...
We introduce a new benchmark, WinoBias, for coreference resolution focus...
Language is increasingly being used to define rich visual recognition
pr...