CPC G06Q 40/03 (2023.01) | 16 Claims |
1. A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform:
training a machine-learning model, based on historical data, with a maximization problem and one or more minimization problems to improve one or more fairness metrics;
receiving real-time data; and
outputting a risk score generated based on the machine-learning model, as trained, and the real-time data,
wherein training the machine-learning model further comprises performing an estimation bundling of outputs of the maximization problem and the one or more minimization problems to generate a uniform predicted output; and
wherein performing the estimation bundling further comprises:
(a) estimating a convergence point of the outputs of the maximization problem and the one or more minimization problems;
(b) estimating a respective multiplier and a respective regularization item for each of the one or more minimization problems;
(c) solving a respective augmented minimization problem for each of the one or more minimization problems;
(d) determining whether outputs of the respective augmented minimization problems are within a predetermined tolerance threshold; and
(e) updating the convergence point and reiterating (b), (c), and (d) when the outputs of the respective augmented minimization problems are not within the predetermined tolerance threshold.
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