Machine translation raises many of the same ethical issues that we've discussed in earlier chapters. For example, consider MT systems translating from Hungarian (which has the gender neutral pronounő) or Spanish (which often drops pronouns) into English (in which pronouns are obligatory, and they have grammatical gender). When translating a reference to a person described without specified gender, MT systems often default to male gender (Schiebinger 2014 , Prates et al. 2019 . And MT systems often assign gender according to culture stereotypes of the sort we saw in Section 6.11. Fig. 10 .19 shows examples from Prates et al. 2019, in which Hungarian gender-neutralő is a nurse is translated with she, but gender-neutralő is a CEO is translated with he. Prates et al. (2019) find that these stereotypes can't completely be accounted for by gender bias in US labor statistics, because the biases are amplified by MT systems, with pronouns being mapped to male or female gender with a probability higher than if the mapping was based on actual labor employment statistics.