arXiv:2606.03198v1 Announce Type: new Abstract: Clinical AI evaluation increasingly delegates scoring to large language models (LLMs) acting as AI raters, yet their scoring behavior across evaluation conditions has not been quantitatively characterized. We address this gap through a factorial study of AI rater behavior in adult type 2 diabetes (T2D) pharmacotherapy at 12-month outpatient follow-up, a clinical task involving complex decision-making operationalized across seven evaluation questions. Four open-source LLMs served simultaneously as clinical decision support system (CDSS) models and
The increasing reliance on large language models for complex decision-making, particularly in critical fields like clinical evaluation, necessitates immediate scrutiny of their reliability and potential biases.
Understanding AI rater discrimination is crucial because biased AI evaluation could lead to inequitable or suboptimal outcomes, particularly in sensitive domains like healthcare.
This research provides quantitative characterization of AI rater behavior, potentially shifting how AI models are perceived and integrated into clinical and other high-stakes decision workflows.
- · AI ethics researchers
- · Healthcare providers prioritizing fairness
- · Patients receiving AI-assisted care
- · Open-source LLM developers improving fairness
- · Developers of unscrutinized AI evaluation systems
- · Healthcare systems relying on biased AI
- · Patients subjected to discriminatory AI decisions
AI rater protocols will be refined to mitigate discrimination and improve fairness in evaluation.
Increased regulatory and ethical oversight will be applied to AI systems used in critical decision-making.
Public trust in AI systems for sensitive applications like healthcare may either increase or decrease depending on the effectiveness of discrimination mitigation strategies.
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Read at arXiv cs.CL