In this post, we're working towards proposing a qualitative definition that could be used by regulators that are trying to target the most dangerous AI systems in the EU, China and the US.

CounterGen is a framework for auditing and reducing bias in NLP models such as generative models (e.g GPT-J, T5, GPT-3 etc.) or classification models (e.g BERT). It does so by generating counterfactual datasets, evaluating NLP models, and doing direct model editing (à la ROME) to reduce bias. CounterGen is easy to use, even by those who don’t know how to code, it is applicable to any text data, including niche use cases, and it proposes concrete solutions to debias biased models.