Many real-world tensors come with missing values. The task of estimation of such missing elements is called tensor completion (TC). It is a fundamental problem with a wide range of applications in data mining, machine learning, signal processing, and computer vision. In the last decade, several different algorithms have been developed, couple of them have shown high-quality performance in diverse domains. However, our investigation shows that even state-of-the-art TC algorithms sometimes make poor estimations for few cases that are not noticeable if we look at their overall performance. However, such wrong estimates might have a severe effect on some decisions. It becomes a crucial issue in applications where humans are involved. Making bad decisions based on such poor estimations can harm fairness. We propose the first algorithm for tensor completion post-correction, called TCPC, to identify some of such poor estimates from the output of any TC algorithm and refine them with more realistic estimations. Our initial experiments with five real-life tensor datasets show that TCPC is an effective post-correction method. © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG