Compensating for vulnerabilities in machine learning algorithms

A non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor, cause the processing system to perform operations, the operations comprising:

• obtaining an output of a machine learning algorithm;
• identifying a vulnerability in the output of the machine learning algorithm, wherein the vulnerability relates to a bias in the output;
• integrating auxiliary data from an auxiliary data source of a plurality of auxiliary data sources into the machine learning algorithm to try to compensate for the vulnerability;
• determining whether the integrating has compensated for the vulnerability;
• and
• generating a runtime output using the machine learning algorithm when the processing system determines that the integrating has compensated for the vulnerability.

A device comprising:

• a processing system including at least one processor;
• and
• a non-transitory computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising:
• obtaining an output of a machine learning algorithm;
• identifying a vulnerability in the output of the machine learning algorithm, wherein the vulnerability relates to a bias in the output;
• integrating auxiliary data from an auxiliary data source of a plurality of auxiliary data sources into the machine learning algorithm to try to compensate for the vulnerability;
• determining whether the integrating has compensated for the vulnerability;
• and
• generating a runtime output using the machine learning algorithm when the processing system determines that the integrating has compensated for the vulnerability.