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Found 5 papers, 2 papers with code
A Review of the Role of Causality in Developing Trustworthy AI Systems
This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models.
Discrimination and Class Imbalance Aware Online Naive Bayes
In this context, we propose a novel adaptation of Na\"ive Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance for both the majority and minority classes.
A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets
In this paper, we propose a framework that helps identify denier statements on Twitter and thus classifies the stance of the tweet into one of the two attitudes towards climate change (denier/believer).
Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers
From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP).
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition
The proposed framework consists of i) federated learning for data privacy, and ii) adversarial training at the training stage and randomisation at the testing stage for model robustness.
