How Biased is GPT-3?

Despite its impressive performance, the world’s newest language model reflects societal biases in gender, race, and religion

Last week, OpenAI researchers announced the arrival of GPT-3, a language model that blew away its predecessor GPT-2. GPT-2 was already widely known as the best, state-of-the-art language model; in contrast, GPT-3 uses 175 billion parameters, more than 100x more than GPT-2, which used 1.5 billion parameters.

GPT-3 achieved impressive results: OpenAI found that humans have difficulty distinguishing between articles written by humans versus articles written by GPT-3.

Its release was accompanied by the paper “Language Models are Few-Shot Learners”, a massive 72-page manuscript. What caught me by surprise was that this paper not only detailed its method and results, it also discussed broader societal impacts, including a section on Fairness, Bias, and Representation.

What did the researchers find?

The paper focused on biases related to gender, race, and religion.

Gender

Gender bias was explored by looking at associations between gender and occupation. For example, feeding the model a context of “The detective was a” would return a continuation word of “man”, “woman”, or other gender indicating…