Social Implications of Large Language Models

Hal Daume · Kate Crawford


This tutorial will address the wider social and economic implications of large language models, such as ELMO (Peters et al., 2018), BERT (Devlin et al., 2019), GPT-2 and -3 (Radford et al., 2019; Brown et al., 2020), FlauBERT (Le et al., 2020), XLNet (Yang et al., 2019), CPM (Zhang et al., 2020), PALM (Bi et al., 2020), Switch C (Fedus et al., 2021) and others. Over the past few years the resources put into developing bigger language models trained on more data has been unparalleled. And yet, the full repercussions of this record concentration of resources has been little discussed. In this tutorial, we aim to address concerns around the economic, political, social, and legal impacts of the development of large language models.

Our tutorial includes guest presentations by:
Emily Bender
Su Lin Blodgett
Emma Strubell
Ari Waldman
Glen Weyl
Thanks to these five scholars for providing their expertise!

Chat is not available.

Mon 12:00 p.m. - 12:40 p.m.
Introduction (Remarks)   
Kate Crawford, Hal Daume
Mon 12:40 p.m. - 12:55 p.m.
Economic Implications (Talk)   
Hal Daume, Kate Crawford
Mon 12:55 p.m. - 1:35 p.m.
Social Aspects (Talk)   
Kate Crawford, Hal Daume
Mon 1:35 p.m. - 1:55 p.m.
Environmental Implications (Talk)   
Kate Crawford, Hal Daume
Mon 1:55 p.m. - 2:15 p.m.
Political and Legal Implications (Talk)   
Hal Daume, Kate Crawford
Mon 2:15 p.m. - 2:20 p.m.
Conclusions (Talk)
Kate Crawford, Hal Daume
Mon 2:20 p.m. - 2:50 p.m.