arXiv · cs.AI · Bernardo Modenesi, Taoyi Chen, Sima Najafzadehkhoei, Lucia Wang, Ameya Diwan, Beatrice Knudsen · 9 小时前模型评分44
预测模型在哪里、对谁校准失准？基于结果学习口袋的局部审计方法
Where Is a Prediction Model Miscalibrated, and for Whom? Local Audits in Outcome-Learned Pockets
导读
研究者提出一种局部审计方法，用随机森林相似度把观测分组为“口袋”，找出预测模型在哪些相似样本集合上系统性高估或低估，并检验同一集合内不同人群（如种族、性别）的平均预测误差是否有差异。
分类置信度
中 · 待人工复核
- 只有单一媒体来源，需要人工复核。
来源级别：媒体与个人
应用领域：医疗与生命科学
事件类型：研究发布
证据摘录
- “We present an audit that asks where a model is miscalibrated (in which sets of similar observations it over- or under-predicts on average) and for whom (whether its average prediction error differs between demographic groups, such as by race or sex, within the same set).”
来源：arXiv · cs.AI · arxiv.org