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Increasing a ML model accuracy is not enough, we must also increase itstrustworthiness. This is an important step for building resilient AI systemsfor safety-critical applications such as automotive, finance, and healthcare.For that purpose, we propose a multi-agent system that combines both machineand human agents. In this system, a checker agent calculates a trust score ofeach instance (which penalizes overconfidence and overcautiousness inpredictions) using an agreement-based method and ranks it; then an improveragent filters the anomalous instances based on a human rule-based procedure(which is considered safe), gets the human labels, applies geometric dataaugmentation, and retrains with the augmented data using transfer learning. Weevaluate the system on corrupted versions of the MNIST and FashionMNISTdatasets. We get an improvement in accuracy and trust score with just fewadditional labels compared to a baseline approach.

Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation