This paper analyzes the issue of fairness in Automatic Speech Recognition (ASR) systems from a philosophical perspective. It argues that the systematic misrecognition of certain language variants goes beyond mere technical limitations and represents a form of disrespect that exacerbates historical injustices against marginalized speech communities. Distinguishing between morally neutral classifications (discriminate1) and harmful discrimination (discriminate2), it demonstrates that persistent misrecognition of nonstandard dialects can escalate from the former to the latter. Furthermore, it identifies three distinct ethical dimensions of ASR bias: the time burden imposed on speakers of nonstandard dialects ("temporal taxation"), which is not captured by existing technological fairness metrics; the disruption of conversational flow due to system misrecognition; and the fundamental link between speech patterns and personal/cultural identity. It analyzes the tension between language standardization and pluralism in ASR development, arguing that current approaches often embody and reinforce problematic linguistic ideologies. Ultimately, it emphasizes that addressing ASR bias requires more than technological intervention; it requires recognizing diverse language variants as legitimate modes of expression worthy of technological acceptance. This philosophical reframing suggests a new path for developing ASR systems that respect linguistic diversity and speaker autonomy.
