  Language models (LMs) must be both safe and equitable to be responsibly
deployed in practice. With safety in mind, numerous detoxification techniques
(e.g., Dathathri et al. 2020; Krause et al. 2020) have been proposed to
mitigate toxic LM generations. In this work, we show that current
detoxification techniques hurt equity: they decrease the utility of LMs on
language used by marginalized groups (e.g., African-American English and
minority identity mentions). In particular, we perform automatic and human
evaluations of text generation quality when LMs are conditioned on inputs with
different dialects and group identifiers. We find that detoxification makes LMs
more brittle to distribution shift, especially on language used by marginalized
groups. We identify that these failures stem from detoxification methods
exploiting spurious correlations in toxicity datasets. Overall, our results
highlight the tension between the controllability and distributional robustness
of LMs.
