This study investigates gender fairness in personalized pain care recommendations using machine learning algorithms. Leveraging a contextual bandits framework, personalized recommendations are formulated and evaluated using LinUCB algorithm on a dataset comprising interactions with $164$ patients across $10$ sessions each. Results indicate that while adjustments to algorithm parameters influence the quality of pain care recommendations, this impact remains consistent across genders. However, when certain patient information, such as self-reported pain measurements, is absent, the quality of pain care recommendations for women is notably inferior to that for men.

翻译：本研究利用机器学习算法探讨个性化疼痛护理建议中的性别公平性问题。基于上下文老虎机框架，采用LinUCB算法在包含164名患者各10次诊疗 session 的数据集上制定并评估个性化建议。结果表明，虽然算法参数调整会影响疼痛护理建议的质量，但这种影响在性别间保持一致。然而，当某些患者信息（如自述疼痛测量值）缺失时，针对女性的疼痛护理建议质量显著低于男性。