US 11,928,730 B1
Training machine learning models with fairness improvement
Yonghan Feng, New York, NY (US); Sijia Liu, Fremont, CA (US); Ratinder Bedi, Danville, CA (US); and Aaron Webster, Isle of Palms, SC (US)
Assigned to Social Finance, Inc., San Francisco, CA (US)
Filed by Social Finance, Inc., San Francisco, CA (US)
Filed on May 30, 2023, as Appl. No. 18/203,514.
Int. Cl. G06Q 40/00 (2023.01); G06Q 40/03 (2023.01)
CPC G06Q 40/03 (2023.01) 16 Claims
OG exemplary drawing
 
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.