论文标题
通过信任分数和数据增强，人类在线多代理方法以提高ML模型中的可信赖性
Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation
论文作者
论文摘要
提高ML模型准确性是不够的，我们还必须提高其可信度。这是建立用于安全至关重要应用（例如汽车，金融和医疗保健）的弹性AI系统的重要步骤。为此，我们提出了一个将机器和人体代理结合在一起的多代理系统。在此系统中，Checker代理使用基于协议的方法计算每个实例的信任分数（在预测中惩罚过度自信和过度审判）并将其对其进行排名；然后，制止者因基于人类规则的程序（被认为是安全的），获得人类标签，应用几何数据增强并使用转移学习对增强数据进行重新训练。我们评估了MNIST和FashionMnist数据集的损坏版本的系统。与基线方法相比，我们只有很少的标签获得了准确性和信任评分的提高。
Increasing a ML model accuracy is not enough, we must also increase its trustworthiness. This is an important step for building resilient AI systems for safety-critical applications such as automotive, finance, and healthcare. For that purpose, we propose a multi-agent system that combines both machine and human agents. In this system, a checker agent calculates a trust score of each instance (which penalizes overconfidence and overcautiousness in predictions) using an agreement-based method and ranks it; then an improver agent filters the anomalous instances based on a human rule-based procedure (which is considered safe), gets the human labels, applies geometric data augmentation, and retrains with the augmented data using transfer learning. We evaluate the system on corrupted versions of the MNIST and FashionMNIST datasets. We get an improvement in accuracy and trust score with just few additional labels compared to a baseline approach.
