It is well-known in the video understanding community that human action recognition models suffer from background bias, i.e., over-relying on scene cues in making their predictions. However, it is difficult to quantify this effect using existing evaluation frameworks. We introduce the Human-centric Analysis Toolkit (HAT), which enables evaluation of learned background bias without the need for new manual video annotation. It does so by automatically generating synthetically manipulated videos and leveraging the recent advances in image segmentation and video inpainting. Using HAT we perform an extensive analysis of 74 action recognition models trained on the Kinetics dataset. We confirm that all these models focus more on the scene background than on the human motion; further, we demonstrate that certain model design decisions (such as training with fewer frames per video or using dense as opposed to uniform temporal sampling) appear to worsen the background bias. We open-source HAT to enable the community to design more robust and generalizable human action recognition models.