Learn how this novel approach to explainability based on adversarial machine learning can be used to explain the predictions of DNNs.
AI explainability, or the ability to understand and interpret how AI systems make decisions, is becoming increasingly important as AI is used in more and more aspects of our lives. This is especially true in fields such as healthcare, finance, and criminal justice, where the consequences of AI decision-making can be significant. For example, consider an AI system that is used to predict which individuals are likely to default on a loan. An AXAI explanation for a specific loan application might show how the probability of default would change if the applicant's income or credit score were different.
One approach to AI explainability is called AXAI, which was originally published in a paper entitled "An Adversarial Approach for Explaining the Predictions of Deep Neural Networks." This research paper presents a method for generating explanations for the predictions of deep neural networks using an adversarial approach.
AXAI uses an adversarial neural network to generate explanations for the predictions of a deep neural network. The adversarial network is trained to generate explanations that are consistent with the predictions of the original network, but that also reveal the specific factors that influence the prediction. This is done by minimizing a loss function that measures the distance between the original network's predictions and the explanations generated by the adversarial network, subject to some constraints on the structure or complexity of the explanations.
The authors of the paper demonstrate the effectiveness of their approach on several benchmarks and show that it can generate explanations that are more faithful to the original network and that capture the relationships between input variables and predictions more accurately than other explanation methods. They also discuss several limitations and challenges of their approach, including the need for a large amount of training data and the potential for the adversarial network to generate explanations that are overly complex or hard to interpret.
"An Adversarial Approach for Explaining the Predictions of Deep Neural Networks" is a valuable contribution to the field of AI explainability that presents a novel method for generating explanations for deep neural networks. It provides insight into the potential and limitations of adversarial approaches for AI explainability and highlights the importance of carefully considering the trade-offs and limitations of different explanation methods.
Learn how this novel approach to explainability based on adversarial machine learning can be used to explain the predictions of deep neural networks and produce better results faster than LIME and SHAP. This talk covers our approach, which identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN and uses this information to produce the explanations.