Gradient descent training for defensible artificial intelligence

Abstract

Defensible AI systems and methods may provide technical solutions for technical problems facing typical AI systems. An expert system may be used to address problems facing AI that systems operate without providing visibility into their internal decision-making processes. The expert system may be developed with meaning-assigned fact nodes. A gradient descent style training process may be used to improve the performance of expert system networks. In an example, a gradient descent training process identifies the contributions of rules and fact values to the outcome fact values, then distributes a portion (e.g., velocity value determined portion) of the error to each rule input weighting based on its proportion of overall contribution. These expert systems may use various approaches to training, such as various selected inputs used to calculate the difference value (e.g., error value), various network designs, various error and augmentation levels, and various different training levels.