English(EN) False positive bias in AI-powered speech-based cognitive screening for multilingual English speakers in the UK

AI认知筛查模型对多语种使用者存在显著偏见

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

arXiv上发表的一项新研究发现，用于基于语音的认知筛查的AI模型存在显著的假阳性偏差，尤其影响英国的多语种人群。研究发现，与单语英语使用者相比，包括Whisper、Wav2Vec 2.0和NeMo在内的模型将多语种使用者错误地标记为认知障碍的可能性高出约2.5倍。这种偏差在记忆、流畅性和阅读任务中更为明显，并且在模型使用DementiaBank等现有数据集进行训练时会加剧，这凸显了在医疗保健领域公平部署AI的迫切需求。 AI

影响强调了在医疗保健领域公平开发和部署AI的必要性，以避免对不同人群造成误诊。

排序理由 arXiv上发表的研究论文，详细介绍了AI模型的偏见。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Madhurananda Pahar, Caitlin Illingworth, Dorota Braun, Bahman Mirheidari, Lise Sproson, Daniel Blackburn, Heidi Christensen · 2026-09-10 04:00

英国多语种英语使用者AI语音认知筛查中的假阳性偏见

arXiv:2602.13047v2 Announce Type: replace Abstract: Conversational speech reveals early signs of cognitive decline, including dementia and mild cognitive impairment (MCI). AI models show promise for speech-based screening, yet most research focuses on monolingual groups. In the U…

本文如何被排名

报道来源 [1]

英国多语种英语使用者AI语音认知筛查中的假阳性偏见

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