Overcoming the Social Biases in Natural Language Processing Systems

How come sophisticated, automatically learned, state-of-the-art NLP models are so biased?

We train NLP models using ever-increasing datasets, which are often collected from the Internet, containing many toxic and biased viewpoints. For example, GPT-3 model released by Open AI is trained on 570 GB of text (ca. 400 billion tokens) crawled from the Internet. It might not be so surprising then to imagine some of those unfair discriminatory biases in the training data creep into the ML models, and even at times get amplified by the training algorithms we use to learn the ML models. Indeed, we have developed increasingly efficient and accurate learning algorithms that can pick even the slightest of the signals in the data, let it be desirable or harmful.

Eating and Having the Cake

However, we cannot simply decide not to use NLP systems because they are toxic, as we also want to enjoy the benefits of the systems developed using such models. We have got used to depending on NLP systems in our day-to-day lives to an extent such that we cannot think of a life without these systems. Therefore, we must find ways to somehow remove the social biases already learned by the NLP systems, or better, prevent NLP systems from learning such biases in the first place.



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