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TRUSTWORTHINESS OF ARTIFICIAL INTELLIGENCE MODELS IN PRESENCE OF ANOMALOUS DATA

机译：在异常数据存在下人工智能模型的可信度

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摘要

Methods, systems, and computer program products for improving trustworthiness of artificial intelligence models in presence of anomalous data are provided herein. A method includes obtaining a machine learning model and a set of training data; determining one or more anomalous data points in said set of training data; for a given one of said anomalous data points, identifying attributes that decrease confidence with respect to at least one output of said machine learning model; determining that a root cause of said decreased confidence corresponds to one of: a class imbalance issue related to said at least one attribute, a confused class issue related to said at least one attribute, a low density issue related to said at least one attribute, and an adversarial issue related to said at least one attribute; and performing step(s) to improve said confidence based at least in part on said determined root cause.
机译：本文提供了用于改善在存在异常数据存在的人工智能模型的可信度的方法，系统和计算机程序产品。一种方法包括获取机器学习模型和一组训练数据;确定所述培训数据集中的一个或多个异常数据点;对于给定的一个上述异常数据点，识别减少对所述机器学习模型的至少一个输出的置信度的属性;确定所述置信度下降的根本原因对应于以下一个：与所述至少一个属性相关的类别不平衡问题，与所述至少一个属性相关的困惑类问题，与所述至少一个属性相关的低密度问题，和与所述至少一个属性相关的对抗问题;并且执行步骤以至少部分地基于所述确定的根本原因来改善所述置信度。

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