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Evidence record 17066 · automatically gathered

Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors

Automatic speaking assessment systems are increasingly deployed in high-stakes settings to mark second language (L2) learners' speaking tests, making it critical to show that their scores depend on speaking proficiency rather than irrelevant speaker attributes such as first language (L1) or age. Transformer-based foundation models have improved the accuracy of these L2 speaking graders, but their black-box representations make fairness and interpretability analysis more difficult. Building on pr

Record details

Published: 6 August 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 7 August 2026

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How to cite this record

ethics.ai (6 August 2026), “Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors,” evidence record 17066, https://ethics.ai/record/17066 (originally published by arXiv).

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