And papers on bias amplification in ML predate LLMs. I remember this specific one which was a spotlight paper at EMNLP:
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints, Zhao et al.
The bias concerns in Gebru's paper cover pre-LLM systems. For all we know, modern frontier models might mitigate many of the concerns the paper brings up. It's hard to know. The logic used in summaries like the one we're commenting on is conclusory: centuries of prejudice are encoded in the total corpus of human language, language models are trained on that corpus, ergo language models must be biased.