Two-Layer Approach to Combine Artificial and Human Intelligence When Labeled Data Is Scarce
Montag, 16. November 2020
Building an AI solution, if the data is unlabeled and the labeling of the full data set is too expensive, is a more then complex task. In order to overcome this challenge, GfK uses a two-layer approach similar to active learning. In the first step we build a model to propose a relatively small subset of the data that should be annotated by the market experts that will work with the solution. Then, to further reduce the needed involvement we build a second model on the annotations to minimize their involvement for the future. The presentation will showcase how two-layer approach helped GfK to increase data quality while minimizing the needed human labelling effort. Furthermore, we will discuss the challenges and benefits of this approach. Finally, there will be a deep dive into the code, the architecture and continuous evolution pipeline for the model.