外科医生是印度男性，言语治疗师是白人女性：审计面向医疗专业人员的视觉-语言模型中的偏见
Surgeons Are Indian Males and Speech Therapists Are White Females: Auditing Biases in Vision-Language Models for Healthcare Professionals
AI总结：
本研究提出评估协议，量化CLIP等视觉语言模型在医疗职业中的人口统计偏见，发现跨角色和模型存在一致偏差，强调AI招聘中的公平风险。
摘要：
视觉语言模型（VLMs），如CLIP和OpenCLIP，能够编码并反映从网络规模数据中学习到的医疗职业与人口统计属性之间的刻板关联。我们提出了一种面向医疗保健场景的评估协议，用于量化相关偏见并评估其操作风险。我们的方法包括：（i）定义了一个涵盖临床医生和联合医疗角色（如外科医生、心脏病专家、牙医、护士、药剂师、技师）的分类体系；（ii）策划了一套职业感知的提示套件以探测模型行为；（iii）针对平衡的人脸语料库基准测试人口统计偏差。实证中，我们观察到多种角色和视觉模型之间存在一致的人口统计偏见。我们的工作强调了在医疗保健等关键领域识别偏见的重要性，因为AI驱动的招聘和劳动力分析可能对公平性、合规性和患者信任产生下游影响。
英文摘要：
Vision language models (VLMs), such as CLIP and OpenCLIP, can encode and reflect stereotypical associations between medical professions and demographic attributes learned from web-scale data. We present an evaluation protocol for healthcare settings that quantifies associated biases and assesses their operational risk. Our methodology (i) defines a taxonomy spanning clinicians and allied healthcare roles (e.g., surgeon, cardiologist, dentist, nurse, pharmacist, technician), (ii) curates a profession-aware prompt suite to probe model behavior, and (iii) benchmarks demographic skew against a balanced face corpus. Empirically, we observe consistent demographic biases across multiple roles and vision models. Our work highlights the importance of bias identification in critical domains such as healthcare as AI-enabled hiring and workforce analytics can have downstream implications for equity, compliance, and patient trust.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
