The hype surrounding artificial intelligence (AI) has led to researchers in fields ranging from medicine to sociology using AI without a full understanding of the technology and its limitations. This has resulted in a wave of spurious AI-generated results. A number of publications have described astonishing results using machine learning, which is the foundation of modern AI. Machine learning involves feeding an algorithm with data from the past in order to attune it to future data that has not yet been seen. However, in several papers, researchers failed to cleanly separate the pools of data used for training vs. testing. This is a mistake that resulted in testing a system with data it has already seen.

Before using a new technique or software such as machine learning for data evaluation, one should critically examine its handling and limitations and, if necessary, also have the data evaluation checked by experts for the method used.