Fairness of Automatic Speech Recognition: Looking Through a Philosophical Lens

2025-08-09

AI Summary

This paper demonstrates that systematic misrecognition of non-standard dialect speakers by automatic speech recognition (ASR) systems constitutes not merely a technical bias but an ethical harm. Method: Drawing on a philosophical analysis framework integrating linguistics and algorithmic fairness theory, the study identifies three ASR-specific ethical dimensions: temporal tax (excess interaction cost), conversational disruption (impairment of dialogue fluency), and identity disruption (erosion of cultural selfhood tied to vocal patterns). It critiques existing fairness metrics for neglecting power asymmetries and failing to capture such structural harms. Contribution/Results: The work reframes ASR bias from “neutral classification error” to “harmful discrimination,” proposing a systemic design paradigm grounded in the epistemic legitimacy of linguistic variation and respect for speaker agency. This advances fairness beyond statistical parity toward linguistic justice.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• Examining ASR bias as a form of disrespect and historical injustice
• Distinguishing between morally neutral classification and harmful discrimination in ASR
• Identifying unique ethical dimensions of ASR bias beyond technical metrics

Innovation

• Philosophical analysis of ASR bias
• Distinguishes harmful vs neutral misrecognition
• Proposes ethical dimensions for speech fairness