1. A computer implemented method for generating labels for machine learning algorithms, the method comprising: retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data; generating a graph that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets; determining weights for the plurality of edges based upon a consistency between the data points classified by two labels connected by the edges; applying an algorithm that creates grouped labels from the multiple label sets and based upon the weights for the plurality of edges; identifying data points from the corpus of data that represent conflicts within the grouped labels; transmitting an electronic message for presenting the identified data points to entities for further classification; and generating a new label set based upon the grouped labels and the further classification received from the entities.
1. A computer implemented method for generating labels for machine learning algorithms, the method comprising: retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data; generating a graph that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets; determining weights for the plurality of edges based upon a consistency between the data points classified by two labels connected by the edges; applying an algorithm that creates grouped labels from the multiple label sets and based upon the weights for the plurality of edges; identifying data points from the corpus of data that represent conflicts within the grouped labels; transmitting an electronic message for presenting the identified data points to entities for further classification; and generating a new label set based upon the grouped labels and the further classification received from the entities. 7. The method of claim 1 , further comprising identifying conflicts in the new label set.