MIT study finds labelling errors in datasets used to test AI

A team led by computer scientists from MIT examined ten of the most-cited datasets used to test machine learning systems. They found that around 3.4 percent of the data was inaccurate or mislabeled, which could cause problems in AI systems that use these datasets. The datasets, which have each been cited more than 100,000 times, include text-based ones from newsgroups, Amazon and IMDb. Errors emerged from issues like Amazon product reviews being mislabeled as positive when they were actually negative and vice versa. Some of the image-based errors result from mixing up animal species.