#dataset-bias News & Analysis

4 articles tagged with #dataset-bias. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 237/10

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SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis

Researchers introduce SAGE, a South Asian GI endoscopy dataset with 1,300 expert-annotated images designed to address geographic bias in medical AI models. Benchmarking reveals existing AI models suffer significant performance degradation on South Asian data, with task-specific classifiers dropping 58% in accuracy and multimodal models showing substantial accuracy losses in clinical detection tasks.

AIBearisharXiv – CS AI · Jun 197/10

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Before the Labels: How Dataset Construction Shapes Suicidality Detection in Clinical Text

Researchers demonstrate that clinical NLP datasets for suicidality detection, particularly the ScAN dataset built on MIMIC-III notes, embed specific operational choices that obscure how labels are constructed rather than representing objective ground truth. The study reveals that dataset design decisions—including single annotators, ICD-based cohort selection, and hospital-stay aggregation—shape what suicidality means in algorithmic systems, highlighting critical gaps between documented clinical judgments and actual suicidal intent.

AIBearisharXiv – CS AI · Jun 197/10

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Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language

Researchers reveal significant limitations in using English-centric persona-based methods to generate multilingual mental health datasets, finding that simply adding nationality and language parameters introduces clinical inconsistencies and causes LLM evaluators to perform poorly on non-English depression severity assessments. The study underscores the urgent need for culturally responsive data generation approaches to build equitable AI mental health systems globally.

AINeutralarXiv – CS AI · May 296/10

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Toward Ethical Facial Age Estimation: A Generalized Zero-Shot Benchmark Without Training on Children's Data

Researchers propose an ethical benchmark for facial age estimation that excludes children's data during training, addressing privacy and legal concerns in AI development. Testing nine state-of-the-art methods reveals severe performance degradation (46.4% average) when models encounter unseen age groups, exposing a critical gap between current practices and responsible data governance.