Systems and/or methods for automated repair of data used in machine learning models via counterfactual generation and mapping

A method for repairing a dataset to be used in a downstream machine learning (ML) model, the method comprising:

• extracting features from a dataset, which is stored in a data store, on a portion-by-portion basis;
• and
• for each portion:
• applying an error detection algorithm to identify erroneous data instances in the dataset using the extracted features associated with the respective portion;
• and
• for each identified erroneous data instance:
• determining a set of feature-space repair candidates including a plurality of feature-space repair candidates, each feature-space repair candidate in the set of feature-space repair candidates having at least one change with respect to the extracted features associated with the respective erroneous data instance;
• for each feature-space repair candidate, generating a data-space repair candidate by (a) finding a matching non-erroneous data instance within the respective portion, and (b) using one or more values from the matching non-erroneous data instance in place of one or more corresponding values in the respective erroneous data instance;
• generating a repaired instance based on one or more of the data-space repair candidates;
• and
• updating the portion to include the repaired instance rather than the respective erroneous data instance, in repairing the dataset in the data store.

A non-transitory computer readable storage medium tangibly storing instructions that, when executed by at least one processor, perform operations comprising:

• extracting features on a portion-by-portion basis from a dataset, which is stored in a data store, to be used in a downstream machine learning (ML) model;
• and
• for each portion:
• applying an error detection algorithm to identify erroneous data instances in the dataset using the extracted features associated with the respective portion;
• and
• for each identified erroneous data instance:
• determining a set of feature-space repair candidates including a plurality of feature-space repair candidates, each feature-space repair candidate in the set of feature-space repair candidates having at least one change with respect to the extracted features associated with the respective erroneous data instance;
• for each feature-space repair candidate, generating a data-space repair candidate by (a) finding a matching non-erroneous data instance within the respective portion, and (b) using one or more values from the matching non-erroneous data instance in place of in Hey-of-one or more corresponding values in the respective erroneous data instance;
• generating a repaired instance based on one or more of the data-space repair candidates;
• and
• updating the portion to include the repaired instance rather than the respective erroneous data instance, in repairing the dataset in the data store.