Data Bias According to Bipol: Men are Naturally Right and It is the Role
  of Women to Follow Their Lead
Irene Pagliai Goya van Boven Tosin Adewumi Lama Alkhaled Namrata Gurung Isabella Södergren Elisa Barney
We introduce new large labeled datasets on bias in 3 languages and show in
experiments that bias exists in all 10 datasets of 5 languages evaluated,
including benchmark datasets on the English GLUE/SuperGLUE leaderboards. The 3
new languages give a total of almost 6 million labeled samples and we benchmark
on these datasets using SotA multilingual pretrained models: mT5 and mBERT. The
challenge of social bias, based on prejudice, is ubiquitous, as recent events
with AI and large language models (LLMs) have shown. Motivated by this
challenge, we set out to estimate bias in multiple datasets. We compare some
recent bias metrics and use bipol, which has explainability in the metric. We
also confirm the unverified assumption that bias exists in toxic comments by
randomly sampling 200 samples from a toxic dataset population using the
confidence level of 95% and error margin of 7%. Thirty gold samples were
randomly distributed in the 200 samples to secure the quality of the
annotation. Our findings confirm that many of the datasets have male bias
(prejudice against women), besides other types of bias. We publicly release our
new datasets, lexica, models, and codes.