The Dataset Nutrition Label Project (DNLP), which was created during the 2018 Assembly program hosted by the Berkman Klein Center and MIT Media Lab, seeks to tackle this blindspot in our understanding of the health and quality of data.
The project’s premise is simple. The integrity of a machine learning model is fundamentally predicated on the data used to train it — as the saying goes, “garbage in, garbage out.” Instead of waiting to assess models after they’ve been created, the DNLP aims to make it easier to quickly assess the viability and fitness of a dataset, before it is used to train a model, by giving it a “nutrition” label.