US 12,481,916 B2
Query-driven challenge of AI bias
Nicholas Tsang, Saratoga, CA (US); Dana L Price, Surf City, NC (US); Diane Chalmers, Rochester, MN (US); and Andrew R. Freed, Cary, NC (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Feb. 28, 2022, as Appl. No. 17/652,754.
Prior Publication US 2023/0274179 A1, Aug. 31, 2023
Int. Cl. G06N 20/00 (2019.01); G06F 40/205 (2020.01); G06F 40/40 (2020.01)
CPC G06N 20/00 (2019.01) [G06F 40/205 (2020.01); G06F 40/40 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A computer system for processing challenges to potential artificial intelligence (AI) biases comprising:
one or more processing devices;
one or more memory devices communicatively and operably coupled to the one or more processing devices;
a bias evaluation engine, at least partially resident within the one or more memory devices, configured to:
inject one or more first values associated with one or more respective variables into an AI platform during runtime;
capture and list one or more features of a respective algorithm of the AI platform;
generate, through the one or more features of the respective algorithm of the AI platform, one or more first outcomes, further comprising:
altering the one or more first values which are used by an application machine learning (ML) scoring model to generate one or more revised outcomes;
producing details of the one or more revised outcomes; and
transmitting the details of the one or more revised outcomes via a summary metrics output module;
construct a bias feature-to-AI feature mapping between one or more bias features and the one or more features of the respective algorithm of the AI platform;
use the bias feature-to-AI feature mapping generated through a preparation portion to identify at least a portion of the one or more respective variables in each of one or more bias factor variables that are either directly or indirectly related to the one or more bias factors;
collect feedback with respect to the one or more first outcomes;
parse the feedback through a natural language understanding (NLU) application;
determine, subject to the parsing, one or more bias factors embedded within the AI platform; and
resolve any potential biases to mitigate any inadvertent effects.