  Machine Translation systems can produce different types of errors, some of
which are characterized as critical or catastrophic due to the specific
negative impact that they can have on users. In this paper we focus on one type
of critical error: added toxicity. We evaluate and analyze added toxicity when
translating a large evaluation dataset (HOLISTICBIAS, over 472k sentences,
covering 13 demographic axes) from English into 164 languages. An automatic
toxicity evaluation shows that added toxicity across languages varies from 0%
to 5%. The output languages with the most added toxicity tend to be
low-resource ones, and the demographic axes with the most added toxicity
include sexual orientation, gender and sex, and ability. We also perform human
evaluation on a subset of 8 translation directions, confirming the prevalence
of true added toxicity. We use a measurement of the amount of source
contribution to the translation, where a low source contribution implies
hallucination, to interpret what causes toxicity. Making use of the input
attributions allows us to explain toxicity, because the source contributions
significantly correlate with toxicity for 84% of languages studied. Given our
findings, our recommendations to reduce added toxicity are to curate training
data to avoid mistranslations, mitigate hallucination and check unstable
translations.
