When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Deep neural networks often learn unintended bias during training, which might have harmful
effects when deployed in realworld settings. This work surveys 214 papers related to …

We investigate the potential for nationality biases in natural language processing (NLP)
models using human evaluation methods. Biased NLP models can perpetuate stereotypes …

Inappropriate design and deployment of machine learning (ML) systems lead to negative
downstream social and ethical impacts–described here as social and ethical risks–for users …

Identifying potential social and ethical risks in emerging machine learning (ML) models and
their applications remains challenging. In this work, we applied two well-established safety …

Recent dataset deduplication techniques have demonstrated that content-aware dataset
pruning can dramatically reduce the cost of training Vision-Language Pretrained (VLP) …

Machine Learning (ML) systems are capable of reproducing and often amplifying undesired
biases. This puts emphasis on the importance of operating under practices that enable the …

Artificial Intelligence (AI), particularly through the advent of large-scale generative AI (GenAI)
models such as Large Language Models (LLMs), has become a transformative element in …

Reference classes in healthcare establish healthy norms, such as pediatric growth charts of
height and weight, and are used to chart deviations from these norms which represent …

Federated Learning presents a way to revolutionize AI applications by eliminating the
necessity for data sharing. Yet, research has shown that information can still be extracted …