While advanced classifiers have been increasingly used in real-world safety-critical applications, how to properly evaluate the black-box models given specific human values remains a concern in the community. Such human values include punishing error cases of different severity in varying degrees and making compromises in general performance to reduce specific dangerous cases. In this paper, we propose a novel evaluation measure named Meta Pattern Concern Score based on the abstract representation of probabilistic prediction and the adjustable threshold for the concession in prediction confidence, to introduce the human values into multi-classifiers. Technically, we learn from the advantages and disadvantages of two kinds of common metrics, namely the confusion matrix-based evaluation measures and the loss values, so that our measure is effective as them even under general tasks, and the cross entropy loss becomes a special case of our measure in the limit. Besides, our measure can also be used to refine the model training by dynamically adjusting the learning rate. The experiments on four kinds of models and six datasets confirm the effectiveness and efficiency of our measure. And a case study shows it can not only find the ideal model reducing 0.53% of dangerous cases by only sacrificing 0.04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1.62% lower value of itself and 0.36% fewer number of dangerous cases.

翻译：尽管先进的分类器越来越多地被应用于现实世界中的安全关键型应用，但如何根据特定人类价值观正确评估黑箱模型仍是学界关注的问题。此类人类价值观包括：对不同严重程度的错误案例实施差异化惩罚，以及通过牺牲整体性能来减少特定危险案例。本文基于概率预测的抽象表示和预测置信度让步的可调阈值，提出了一种名为"元模式关注分数"的新型评估指标，旨在将人类价值观引入多分类器评估。在技术层面，我们借鉴了混淆矩阵评估指标与损失函数两类常用指标的优缺点，使得本指标在通用任务中同样有效，且交叉熵损失函数在极限情况下成为本指标的特例。此外，该指标还可通过动态调整学习率优化模型训练过程。在四类模型和六个数据集上的实验验证了本指标的有效性和效率。案例研究表明，该指标不仅能通过仅牺牲0.04%的训练准确率找到减少0.53%危险案例的理想模型，还能优化学习率训练出新模型，该模型在自身指标值降低1.62%的同时，危险案例数量平均减少0.36%。