Fairness of Automatic Speech Recognition: Looking Through a Philosophical Lens

TL;DR

This paper critically examines the fairness of Automatic Speech Recognition systems through a philosophical perspective, highlighting ethical issues related to linguistic diversity, bias, and social justice.

Contribution

It introduces a philosophical framework to understand ASR bias, emphasizing ethical dimensions and proposing that addressing bias requires recognizing diverse speech varieties as legitimate.

Findings

ASR bias involves ethical dimensions like temporal taxation and cultural identity.

Current fairness metrics fail to capture power asymmetries caused by ASR misrecognition.

Addressing bias requires recognition of linguistic diversity beyond technical fixes.

Abstract

Automatic Speech Recognition (ASR) systems now mediate countless human-technology interactions, yet research on their fairness implications remains surprisingly limited. This paper examines ASR bias through a philosophical lens, arguing that systematic misrecognition of certain speech varieties constitutes more than a technical limitation -- it represents a form of disrespect that compounds historical injustices against marginalized linguistic communities. We distinguish between morally neutral classification (discriminate1) and harmful discrimination (discriminate2), demonstrating how ASR systems can inadvertently transform the former into the latter when they consistently misrecognize non-standard dialects. We identify three unique ethical dimensions of speech technologies that differentiate ASR bias from other algorithmic fairness concerns: the temporal burden placed on speakers of non-standard varieties ("temporal taxation"), the disruption of conversational flow when systems misrecognize speech, and the fundamental connection between speech patterns and personal/cultural identity. These factors create asymmetric power relationships that existing technical fairness metrics fail to capture. The paper analyzes the tension between linguistic standardization and pluralism in ASR development, arguing that current approaches often embed and reinforce problematic language ideologies. We conclude that addressing ASR bias requires more than technical interventions; it demands recognition of diverse speech varieties as legitimate forms of expression worthy of technological accommodation. This philosophical reframing offers new pathways for developing ASR systems that respect linguistic diversity and speaker autonomy.