On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

• https://dl.acm.org/doi/10.1145/3442188.3445922
• Emily Bender, Timnit Gebru et al.

Ethics, Ethics of artificial intelligence, LLMs, AI

When we perform risk/benefit analyses of language technology, we must keep in mind how the risks and benefits are distributed, because they do not accrue to the same people. … Is it fair or just to ask, for example, that the residents of the Maldives (likely to be underwater by 2100) or the 800,000 people in Sudan affected by drastic floods pay the environmental price of training and deploying ever larger English LMs, when similar large-scale models aren’t being produced for Dhivehi or Sudanese Arabic?

“Size doesn’t guarantee diversity”

In all cases, the voices of people most likely to hew to a hegemonic viewpoint are also more likely to be retained. In the case of US and UK English, this means that white supremacist and misogynistic, ageist, etc. views are overrepresented in the training data, not only exceeding their prevalence in the general population but also setting up models trained on these datasets to further amplify biases and harms.

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For instance, GPT-2’s training data is sourced by scraping out- bound links from Reddit, and Pew Internet Research’s 2016 survey reveals 67% of Reddit users in the United States are men, and 64% between ages 18 and 29.13 Similarly, recent surveys of Wikipedians find that only 8.8–15% are women or girls