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Responsible NLP Blog

Advances in Ethics, Privacy, Security, Fairness, Reliability, Transparency, and Accountability in Natural Language Processing.

Healthcare organizations can face numerous challenges when developing high-quality machine learning models. Data is often noisy and unstructured, and developing successful models involves experimenting with numerous parameter configurations, datasets, and...

I’ll start the talk with an overview of cleanlab 2.0, a powerful open-source package that lets you find and fix label errors and data quality issues in *any* labeled dataset…...

Last year, I wrote about my top four predictions for natural language processing (NLP) in 2021. As we approach 2022, a lot has happened in the world of artificial intelligence (AI) and machine learning,...

The world is facing a global AI talent shortage, so while there’s a great demand for NLP implementations, the supply of data scientists needed to bring these projects to life...

Marco Tulio Ribeiro will present Adaptive Testing (AdaTest), a new process and tool that leverages the complementary strengths of humans and language models (GPT-3 in our case) in order to...