> What were they thinking?! [...] In addition to their statement, linked in a thread below [0], what they were thinking is pretty out in the open in the abstra

> What were they thinking?! [...] In addition to their statement, linked in a thread below [0], what they were thinking is pretty out in the open in the abstract of the original paper: "We introduce here a new database called “ImageNet”, a largescale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. " [1] That paper is now 10 years old, WordNet itself had it's first release somewhere in the 1980s. A lot happens in research a decade down the road and lots of research towards biases of datasets are relatively recent in that sense. It can hardly be blamed on the researchers that people use their datasets in production merely because "they're large and there in the open". As their statement indicates they're actively working on the issues that arose during the last few years. Most research datasets are fit for a very specific purpose, to me the larger problem here is two-fold: "AI" fearmongering on one side and startups/corporations on the other side lacking the necessary skills to select and filter their training data. Hopefully that'll be a matter of the past once the hyped data science field reaches maturity. edit: Wordnet seems to include said terms to this day, [2] for example still lists "mantrap" associated to "beaty" in the synset for "a very attractive or seductive looking woman". Although I'm unsure how they should handle these cases, both are words and that's one definition used in practice, even if we find it reprehensible. They seem to have removed the n-word but that's about the only removed instance I can find in a cursory search of sensitive or insulting English words. Maybe they should clarify sensitive annotations like they do with vulgarity. [0] https://news.ycombinator.com/item?id=21054770 https://news.ycombinator.com/item?id=21054770 [1] http://www.image-net.org/papers/imagenet_cvpr09.pdf http://www.image-net.org/papers/imagenet_cvpr09.pdf [2] http://wordnetweb.princeton.edu/perl/webwn?s=beauty http://wordnetweb.princeton.edu/perl/webwn?s=beauty