Methods and systems for mitigating bias from artificial intelligence models during post-processing

A server system, comprising:

• a communication interface;
• a memory comprising executable instructions;
• and
• a processor communicably coupled to the communication interface and the memory, the processor configured to cause the server system to at least:
• access a first predicted probability score and a second predicted probability score for each data sample of a plurality of data samples from an input dataset stored in a database associated with the server system,
• wherein each data sample is associated with a particular predicted class label from at least two predicted class labels and a predicted loss category label of at least two predicted loss category labels, wherein each predicted class label has a counterpart class label;
• segregate a first set of data samples and a second set of data samples from the plurality of data samples based, at least in part, on the first predicted probability score, the second predicted probability score, a predefined margin threshold, and a first predefined loss threshold;
• segregate a third set of data samples from the plurality of data samples based, at least in part, on the first set of data samples, the second set of data samples, and an overlap condition;
• and
• transition the predicted class label associated with each of the third set of data samples to the counterpart class label.

A non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by at least a processor of a server system, cause the server system to perform a method comprising:

• accessing a first predicted probability score and a second predicted probability score for each data sample of a plurality of data samples from an input dataset stored in a database associated with the server system,
• wherein each data sample is associated with a particular predicted class label from at least two predicted class labels and a predicted loss category label of at least two predicted loss category labels, wherein each predicted class label has a counterpart class label;
• segregating a first set of data samples and a second set of data samples from the plurality of data samples based, at least in part, on the first predicted probability score, the second predicted probability score, a predefined margin threshold, and a first predefined loss threshold;
• segregating a third set of data samples from the plurality of data samples based, at least in part, on the first set of data samples, the second set of data samples, and an overlap condition;
• and
• transitioning the predicted class label associated with each of the third set of data samples to the counterpart class label.