Training data quality
The layer this wiki named as its founding gap and then could not find a literature for: how the data a model learns from, and the data it is judged against, get collected, labelled and checked.
Two sources, two halves
The practice half. data-cascades interviewed 53 practitioners in high-stakes domains and found that 92% had hit at least one compounding data failure, 45.3% more than one in a single project. The failures share a shape — a decision made early about a label, a sample or a hand-off, whose consequence surfaces much later and far downstream.
The measurement half. pervasive-label-errors checked the labels themselves: at least 3.3% wrong on average across ten of the field’s standard test sets, at least 6% on the ImageNet validation set. Correcting them can reverse a model ranking, so the errors are not noise that averages out — they change conclusions.
Put together: the data is wrong at a measured rate, and the process that produces it fails at a measured rate. Neither source needed a model to establish its claim.
Why the field looks away
Both sources say the same thing about incentives, from different directions. Sambasivan et al. put it plainly — “data is the most under-valued and de-glamorised aspect of AI” — and their title is a practitioner’s own words about who wants which job. Northcutt et al. show the cost of that preference landing somewhere specific: in the test sets everyone competes on, unexamined for years while model architectures turned over several times.
The wiki has its own version of this. Every source it holds on the middle layer arrived as teaching material about something else, and the reading was that no literature existed. What existed was a literature in HCI, studying practitioners, which a search of the ML side would never return.
What is still missing
None of the three says how to do the work well. Data Cascades is a diagnosis and calls for “incentivizing data excellence” without specifying the practice; the label-error paper supplies a detection method (confident learning) for one failure, on data that already exists. Nothing here covers annotation guidelines, annotator training, sampling design, or what a labelling operation actually costs. Inter-annotator agreement is the one exception, and it arrived as a metric rather than a practice — see below.
The instrument, added the same day
measuring-annotator-agreement is the third piece: not how often the practice fails or how wrong the labels are, but how you would check. Its finding is that inter-annotator agreement — the standard instrument — is well-founded for categorical labels and shaky for bounding boxes, sequence tags, ranked lists and free text, because each needs a distance function chosen before agreement means anything. So the gap above narrows by one: the corpus now has an instrument for checking a labelling process. It still has no account of how to run one.
Related
measuring-annotator-agreement · data-cascades · pervasive-label-errors · demo-to-production-gap · machine-learning · ml-system-design · synthesis