https://www.technologyreview.com/2021/04/01/1021619/ai-data-errors-warp-machine-learning-progress
The 10 most cited AI data sets are riddled with label errors, according to a new study out of MIT, and it’s distorting our understanding of the field’s progress.
Data backbone: Data
sets are the backbone of AI research, but some are more critical than
others. There are a core set of them that researchers use to evaluate
machine-learning models as a way to track how AI capabilities are
advancing over time. One of the best-known is the canonical
image-recognition data set ImageNet, which kicked off the modern AI
revolution. There’s also MNIST, which compiles images of handwritten
numbers between 0 and 9. Other data sets test models trained to
recognize audio, text, and hand drawings.
Yes, but: In recent years, studies have found that these data sets can contain serious flaws. ImageNet, for example, contains racist and sexist labels as well as photos of people’s faces obtained without consent.
The latest study now looks at another problem: many of the labels are
just flat-out wrong. A mushroom is labeled a spoon, a frog is labeled a
cat, and a high note from Ariana Grande is labeled a whistle. The
ImageNet test set has an estimated label error rate of 5.8%. Meanwhile,
the test set for QuickDraw, a compilation of hand drawings, has an
estimated error rate of 10.1%.
How was it measured? Each
of the 10 data sets used for evaluating models has a corresponding data
set used for training them. The researchers, MIT graduate students
Curtis G. Northcutt and Anish Athalye and alum Jonas Mueller, used the
training data sets to develop a machine-learning model and then used it
to predict the labels in the testing data. If the model disagreed with
the original label, the data point was flagged up for manual review.
Five human reviewers on Amazon Mechanical Turk were asked to vote on
which label—the model’s or the original—they thought was correct. If the
majority of the human reviewers agreed with the model, the original
label was tallied as an error and then corrected.
Does this matter? Yes.
The researchers looked at 34 models whose performance had previously
been measured against the ImageNet test set. Then they remeasured each
model against the roughly 1,500 examples where the data labels were
found to be wrong. They found that the models that didn’t perform so
well on the original incorrect labels were some of the best
performers after the labels were corrected. In particular, the simpler
models seemed to fare better on the corrected data than the more
complicated models that are used by tech giants like Google for image
recognition and assumed to be the best in the field. In other words, we
may have an inflated sense of how great these complicated models are
because of flawed testing data.
Now what? Northcutt
encourages the AI field to create cleaner data sets for evaluating
models and tracking the field’s progress. He also recommends that
researchers improve their data hygiene when working with their own data.
Otherwise, he says, “if you have a noisy data set and a bunch of models
you’re trying out, and you’re going to deploy them in the real world,”
you could end up selecting the wrong model. To this end, he open-sourced
the code he used in his study for correcting label errors, which he says is already in use at a few major tech companies.
