  Advances in large language models (LLMs) have driven an explosion of interest
about their societal impacts. Much of the discourse around how they will impact
social equity has been cautionary or negative, focusing on questions like "how
might LLMs be biased and how would we mitigate those biases?" This is a vital
discussion: the ways in which AI generally, and LLMs specifically, can entrench
biases have been well-documented. But equally vital, and much less discussed,
is the more opportunity-focused counterpoint: "what promising applications do
LLMs enable that could promote equity?" If LLMs are to enable a more equitable
world, it is not enough just to play defense against their biases and failure
modes. We must also go on offense, applying them positively to equity-enhancing
use cases to increase opportunities for underserved groups and reduce societal
discrimination. There are many choices which determine the impact of AI, and a
fundamental choice very early in the pipeline is the problems we choose to
apply it to. If we focus only later in the pipeline -- making LLMs marginally
more fair as they facilitate use cases which intrinsically entrench power -- we
will miss an important opportunity to guide them to equitable impacts. Here, we
highlight the emerging potential of LLMs to promote equity by presenting four
newly possible, promising research directions, while keeping risks and
cautionary points in clear view.
