Deep learning has provided the world of data science with highly effective tools that can address problems in virtually any domain, and using nearly any kind of data. However, the non-intuitive features deduced and used by deep learning algorithms require a very careful experimental design, and a failure to meet that requirement can lead to miserably flawed results, regardless of the quality of the data or the structure of the deep learning network. I first noticed such flaws almost ten years ago, when I applied algorithms that used non-intuitive features for the purpose of automatic face recognition. I noticed that when using the most common face recognition benchmarks at that time (FERET, ORL, YaleB, JAFFE, and others), the algorithms could identify the correct face even when using just a small seemingly blank part of the background, normally a small sub-image from the top-left corner of the original image, that does not contain any part of the face, hair, clothes, or anything else that could allow the recognition of a person (1). I ran the experiments like they were intended, but instead of using the full face images I used a very small part of the background taken from the top-left corner of each image. The algorithms were able to identify the faces in very high accuracy, sometimes as high as 100%, even though no faces were in the images that were analyzed.