Due to the black box nature of deep neural networks (DNN), the continuous validation of DNN during operation is challenging with the absence of a human monitor. As a result this makes it difficult for developers and regulators to gain confidence in the deployment of autonomous systems employing DNN. It is critical for safety during operation to know when DNN's predictions are trustworthy or suspicious. With the absence of a human monitor, the basic approach is to use the model's output confidence score to assess if predictions are trustworthy or suspicious. However, the model's confidence score is a result of computations coming from a black box, therefore lacks transparency and makes it challenging to automatedly credit trustworthiness to predictions. We introduce the trustworthiness score (TS), a simple metric that provides a more transparent and effective way of providing confidence in DNN predictions compared to model's confidence score. The metric quantifies the trustworthiness in a prediction by checking for the existence of certain features in the predictions made by the DNN. We also use the underlying idea of the TS metric, to provide a suspiciousness score (SS) in the overall input frame to help in the detection of suspicious frames where false negatives exist. We conduct a case study using YOLOv5 on persons detection to demonstrate our method and usage of TS and SS. The case study shows that using our method consistently improves the precision of predictions compared to relying on model confidence score alone, for both 1) approving of trustworthy predictions (~20% improvement) and 2) detecting suspicious frames (~5% improvement).

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