公开(公告)号：US20250139474A1

公开(公告)日：2025-05-01

申请号：US18544899

申请日：2023-12-19

Applicant: Oracle International Corporation

Inventor： Yasha Pushak ,  Ehsan Soltan Aghai ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N7/01 ,  G06N5/04

Abstract: A computer obtains multipliers of a sensitive feature. From an input that contains a value of the feature, a probability of a class is inferred. Based on the value of the feature in the input, one of the multipliers of the feature is selected. The multiplier is specific to both of the feature and the value of the feature. The input is classified based on a multiplicative product of the probability of the class and the multiplier that is specific to both of the feature and the value of the feature. In an embodiment, a black-box tri-objective optimizer generates multipliers on a three-way Pareto frontier from which a user may interactively select a combination of multipliers that provides a best three-way tradeoff between fairness and accuracy. The optimizer has three objectives to respectively optimize three distinct validation metrics that may, for example, be accuracy, fairness, and favorable outcome rate decrease.

公开(公告)号：US20240303515A1

公开(公告)日：2024-09-12

申请号：US18512438

申请日：2023-11-17

Applicant: Oracle International Corporation

Inventor： Zahra Zohrevand ,  Ehsan Soltan Aghai ,  Yasha Pushak ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N5/04

CPC classification number: G06N5/04

Abstract: A computer stores a reference corpus that consists of many reference points that each has a respective class. Later, an expected class and a subject point (i.e. instance to explain) that does not have the expected class are received. Multiple reference points that have the expected class are selected as starting points. Based on the subject point and the starting points, multiple discrete interpolated points are generated that have the expected class. Based on the subject point and the discrete interpolated points, multiple continuous interpolated points are generated that have the expected class. A counterfactual explanation of why the subject point does not have the expected class is directly generated based on continuous interpolated point(s) and, thus, indirectly generated based on the discrete interpolated points. For acceleration, neither way of interpolation (i.e. counterfactual generation) is iterative. Generated interpolated points can be reused to amortize resources consumed while generating counterfactuals.