270 citations · 624 across the 13 of their papers we have counts for
4 papers · 1 filter
Ground-Truth, Whose Truth? -- Examining the Challenges with Annotating Toxic Text Datasets
Kofi Arhin, Ioana Baldini, Dennis Wei +2
The use of machine learning (ML)-based language models (LMs) to monitor content online is on the rise. For toxic text identification, task-specific fine-tuning of these models are…
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness
Moninder Singh, Gevorg Ghalachyan, Kush R. Varshney +1
To ensure trust in AI models, it is becoming increasingly apparent that evaluation of models must be extended beyond traditional performance metrics, like accuracy, to other dimens…
AI Explainability 360: Impact and Design
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.…
Your fairness may vary: Pretrained language model fairness in toxic text classification
Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy +2
The popularity of pretrained language models in natural language processing systems calls for a careful evaluation of such models in down-stream tasks, which have a higher potentia…