Large language models (LLMs) have shown powerful performance and development
prospect and are widely deployed in the real world. However, LLMs can capture social …

This position paper investigates the problem of automated text anonymisation, which is a
prerequisite for secure sharing of documents containing sensitive information about …

Human-like biases and undesired social stereotypes exist in large pretrained language
models. Given the wide adoption of these models in real-world applications, mitigating such …

Abstract Concept erasure aims to remove specified features from a representation. It can
improve fairness (eg preventing a classifier from using gender or race) and interpretability …

Commonsense reasoning remains a major challenge in AI, and yet, recent progresses on
benchmarks may seem to suggest otherwise. In particular, the recent neural language …

Large transformer-based language models (LMs) trained on huge text corpora have shown
unparalleled generation capabilities. However, controlling attributes of the generated …

Federated learning is a method of training models on private data distributed over multiple
devices. To keep device data private, the global model is trained by only communicating …

Differential privacy (DP) is a popular mechanism for training machine learning models with
bounded leakage about the presence of specific points in the training data. The cost of …

The ability to control for the kinds of information encoded in neural representation has a
variety of use cases, especially in light of the challenge of interpreting these models. We …

We present the first challenge set and evaluation protocol for the analysis of gender bias in
machine translation (MT). Our approach uses two recent coreference resolution datasets …