  Despite the fact that variation is a fundamental characteristic of natural
language, automatic speech recognition systems perform systematically worse on
non-standardised and marginalised language varieties. In this paper we use the
lens of language policy to analyse how current practices in training and
testing ASR systems in industry lead to the data bias giving rise to these
systematic error differences. We believe that this is a useful perspective for
speech and language technology practitioners to understand the origins and
harms of algorithmic bias, and how they can mitigate it. We also propose a
re-framing of language resources as (public) infrastructure which should not
solely be designed for markets, but for, and with meaningful cooperation of,
speech communities.
