Patent ID: 11922435
Assignee: CEREBRI AI INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 19:
20. A method performed by one or more processors configured with operational instructions, the method comprising:
obtaining, with one or more processors, for a plurality of entities, datasets, wherein:
the datasets comprise event records involving the a plurality of entities;

obtaining, with one or more processors, a first training dataset, wherein:
the first training dataset comprises at least some of the event records;

training, with one or more processors, a first machine-learning model on the first training dataset by iteratively adjusting parameters of the first machine-learning model to optimize a first objective function that indicates an accuracy of the first machine-learning model in making a first decision; and
storing, with one or more processors, the adjusted parameters of the trained first machine-learning model in memory;
obtaining, with one or more processors, a second training dataset, wherein:
the second training dataset comprises a set of unbiased or pseudo-unbiased event records;

training, with one or more processors, a second machine-learning model on the second training dataset by iteratively adjusting parameters of the second machine- learning model to optimize a second objective function that indicates an accuracy of the second machine-learning model in making a second decision; and
storing, with one or more processors, the adjusted parameters of the trained second machine-learning model in memory; and
determining that the first decision includes bias based at least in part on the second decision, wherein the method further comprises:
detecting correlation of outputs of the first machine-learning model with a member of a second set of attributes and, in response, selecting a first rule to be used by one or more processors to make a decision about the first entity; and
detecting, responsive to a second set of inputs corresponding to a second entity, an absence of correlation of outputs of the first machine-learning model with any member of the second set of attributes and, in response, selecting a second rule to be used by the computer system to make a decision about the second entity, wherein:
the first rule is selected based on which member of the second set of attributes exhibits the correlation of outputs of the first machine-learning model; and
different members of the second set of attributes correspond to different rule selections.