智能体
arxiv.org ·
研究揭示 AI 语音认知筛查对英国多语使用者的假阳性偏倚
研究招募 1,395 名参与者（英语单语者及来自谢菲尔德/布拉德福德的多语者），通过 CognoMemory agent 采集超过 263 小时语音，多语者使用英语及索马里语、中文或南亚语言（印地语、乌尔都语、旁遮普语、米尔普里语、阿拉伯语）。评估 Whisper、Wav2Vec 2.0、NeMo 等 ASR 及下游认知分类与 MMSE 回归模型后发现：ASR 准确率组间无显著差异，但下游模型对多语者误判为认知受损的比例更高，假阳性率为 28%–37%，而单语者为 12%–16%，多语者被错误标记的可能性约为 2.5 倍；使用 DementiaBank 训练会加剧该偏差。作者称这是首个针对英国多语少数族裔语音 AI 认知筛查假阳性偏倚的大规模分析，强调需解决偏差以实现安全、公平的医疗部署。
AI推荐理由 arXiv 论文（v2，2026-09-09 修订），样本规模明确：1,395 名参与者、263 小时语音，使用 CognoMemory agent 采集，覆盖英语单语者与索马里语、中文、南亚语言（印地语、乌尔都语、旁遮普语、米尔普里语、阿拉伯语）多语者。关键发现具体：ASR（Whisper、Wav2Vec 2.0、NeMo）准确率组间无显著差异，但下游认知分类模型对多语者假阳性率 28%–37%，显著高于单语者 12%–16%，约 2.5 倍；在 DementiaBank 上训练会加剧偏差。新信息在于首次针对英国多语少数族裔群体量化语音认知筛查的假阳性偏倚，对医疗 AI 公平部署有实际意义；局限是研究限于英国特定人群与语言组合，未给出偏差修正方法。
76
正文
Computer Science > Computation and Language
arXiv:2602.13047 (cs)
[Submitted on 13 Feb 2026 (v1), last revised 9 Sep 2026 (this version, v2)]
Title:False positive bias in AI-powered speech-based cognitive screening for multilingual English speakers in the UK
Authors:Madhurananda Pahar, Caitlin Illingworth, Dorota Braun, Bahman Mirheidari, Lise Sproson, Daniel Blackburn, Heidi Christensen
View a PDF of the paper titled False positive bias in AI-powered speech-based cognitive screening for multilingual English speakers in the UK, by Madhurananda Pahar and 6 other authors
View PDF
HTML (experimental)
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 UK, dementia is projected to rise fastest among Black and Asian communities, where multilingualism is common, making equity assessment critical. We recruited 1,395 participants (monolingual English speakers and multilingual speakers from Sheffield/Bradford) and collected over 263 hours of speech via the CognoMemory agent. Multilingual participants spoke English alongside Somali, Chinese, or South Asian languages (Hindi, Urdu, Punjabi, Mirpuri, Arabic). We evaluated ASR (Whisper, Wav2Vec 2.0, NeMo) and downstream AI models for cognitive classification and MMSE regression. ASR accuracy showed no significant differences across groups. However, downstream models exhibited systematic disparities: multilingual speakers were more often misclassified as impaired, especially in memory, fluency, and reading tasks. False-positive rates were substantially higher for multilingual (28 to 37%) than monolingual (12 to 16%) speakers, meaning multilingual individuals were approximately 2.5 times more likely to receive incorrect impairment labels. These biases worsened when models were trained on DementiaBank. This is the first large-scale analysis of false-positive bias in speech-based AI cognitive screening for UK multilingual ethnic minorities. Despite strong overall performance, current models show measurable disparities affecting multilingual speakers. Addressing these biases is essential for safe, equitable deployment in diverse healthcare settings.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:
arXiv:2602.13047 [cs.CL]
(or
arXiv:2602.13047v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2602.13047
Focus to learn more
arXiv-issued DOI via DataCite
Submission history
From: Madhurananda Pahar [view email]
[v1]
Fri, 13 Feb 2026 16:03:37 UTC (2,596 KB)
[v2]
Wed, 9 Sep 2026 16:29:46 UTC (2,939 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled False positive bias in AI-powered speech-based cognitive screening for multilingual English speakers in the UK, by Madhurananda Pahar and 6 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CL
new
|
recent
| 2026-02
Change to browse by:
cs
cs.AI
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? |
Disable MathJax (What is MathJax?)