PaperActive

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

quality89

Updated 2 h ago · first seen 11 Sept 2026

paper_01M294GPV20TA99D9QT6E0WD22

Published: 11 Sept 2026; T1 · 2 h ago
arXiv: 2602.13047; T1 · 2 h ago
Category: cs.CL; T1 · 2 h ago

Abstract

-cross 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.

Authors 7

Madhurananda Pahar, Caitlin Illingworth, Dorota Braun, Bahman Mirheidari, Lise Sproson, Daniel Blackburn, Heidi Christensen

Specification

Official page: arxiv.org/abs/2602.13047
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
arXiv id: 2602.13047
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Categories: cs.CL, cs.AI
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
PDF: arxiv.org/pdf/2602.13047
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Primary category: cs.CL
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh

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T19

Freshest observation

2 h ago

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9 claims · 9 properties

Official pageofficial_url1

Claim history for Official page
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/abs/2602.13047	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
-cross 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.	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Arxiv announce typearxiv_announce_type1

Claim history for Arxiv announce type
Value	Valid from → to	Status	Source	Confidence	Extractor
replace	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

arXiv idarxiv_id1

Claim history for arXiv id
Value	Valid from → to	Status	Source	Confidence	Extractor
2602.13047	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Authorsauthors1

Claim history for Authors
Value	Valid from → to	Status	Source	Confidence	Extractor
Madhurananda Pahar, Caitlin Illingworth, Dorota Braun, Bahman Mirheidari, Lise Sproson, Daniel Blackburn, Heidi Christensen	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Categoriescategories1

Claim history for Categories
Value	Valid from → to	Status	Source	Confidence	Extractor
cs.CL, cs.AI	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

PDFpdf_url1

Claim history for PDF
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/pdf/2602.13047	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Primary categoryprimary_category1

Claim history for Primary category
Value	Valid from → to	Status	Source	Confidence	Extractor
cs.CL	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Publishedpublished_at1

Claim history for Published
Value	Valid from → to	Status	Source	Confidence	Extractor
11 Sept 2026	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	40 min ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.