Ground truth quality for machine learning models

A system comprising:

• a memory having computer readable instructions;
• and
• one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising:
• receiving a plurality of data inputs, wherein each of the plurality of data inputs has an associated label;
• training a model based on the plurality of data inputs;
• generating a plurality of vector representations based on the model, wherein each of the plurality of vector representations corresponds to a unique one of the plurality of data inputs;
• clustering the plurality of vector representations into one or more clusters;
• identifying, based on the one or more clusters, at least one anomalous data input, wherein the at least one anomalous data input comprises a data input of the plurality of data inputs that is mislabeled, contributes to an ambiguous class structure or is an outlier;
• and
• outputting a notification that provides an indication of the at least one anomalous data input.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer processor to cause the computer processor to perform a method comprising:

• receiving a plurality of data inputs, wherein each of the plurality of data inputs has an associated label;
• training a model based on the plurality of data inputs;
• generating a plurality of vector representations based on the model, wherein each of the plurality of vector representations corresponds to a unique one of the plurality of data inputs;
• clustering the plurality of vector representations into one or more clusters;
• identifying, based on the one or more clusters, at least one anomalous data input, wherein the at least one anomalous data input comprises a data input of the plurality of data inputs that is mislabeled, contributes to an ambiguous class structure or is an outlier;
• and
• outputting a notification that provides an indication of the at least one anomalous data input.