Similar to above, we compute the average senti-
ment of co-occurring words. We report the average
sentiment numbers in Table 13. We ﬁnd that “his-
panic/latino” narrowly edges out “asian” for the
highest sentiment, followed by “white”. On the
other hand, “black” had the lowest sentiment, at
-0.15.

We note that for all demographics, the average sen-
timent is negative. We hypothesize that this is due
to the speciﬁc context for which the phrases we use
to compute co-occurrences appear. For example, it
is often quite common for news articles to describe
suspects as an “asian man”.

6.5 Author Consent and Public Data
Another issue with the use of texts in natural lan-
guage processing research is consent. Although
one is typically not legally obligated to receive the
permission of an author to train a NLP algorithm on
their work15, many consider doing so a moral obli-

15Laws vary by country. For a discussion of US law, see

Section 7.1

13
The Pile- An 800GB Dataset of Diverse Text for Language Modeling