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System and a Method for Bias Estimation in Artificial Intelligence (AI) Models Using Deep Neural Network

机译:基于深度神经网络的人工智能(AI)模型偏差估计系统与方法

摘要

A system for bias estimation in Artificial Intelligence (AI) models using a pre-trained unsupervised deep neural network, comprising a bias vector generator implemented by at least one processor that executes an unsupervised DNN with a predetermined loss function. The bias vector generator is adapted to store a given ML model to be examined, with predetermined features; store a test-set of one or more test data samples being input data samples; receive a feature vector consisting of one or more input samples; output a bias vector indicating the degree of bias for each feature, according to said one or more input samples. The system also comprises a post-processor which is adapted to receive a set of bias vectors generated by said bias vector generator; process said bias vectors; calculate a bias estimation for every feature of said ML model, based on predictions of said ML model; provide a final bias estimation for each examined feature.
机译:一种在人工智能(AI)模型中使用预先训练的无监督深度神经网络进行偏差估计的系统,包括由至少一个处理器实现的偏差向量发生器,该处理器执行具有预定损失函数的无监督DNN。偏置向量发生器适于存储具有预定特征的待检查的给定ML模型;存储作为输入数据样本的一个或多个测试数据样本的测试集;接收由一个或多个输入样本组成的特征向量;根据所述一个或多个输入样本,输出指示每个特征的偏差程度的偏差向量。该系统还包括后处理器,其适于接收由所述偏置向量发生器生成的一组偏置向量;处理所述偏置向量;基于所述ML模型的预测,计算所述ML模型的每个特征的偏差估计;为每个检查的特征提供最终偏差估计。

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