Robust Explainability

A Tutorial on Gradient-Based Attribution Methods for Deep Neural ...

On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very ...

Robust Explainability: A Tutorial on Gradient-Based Attribution ...

Robust Explainability: A Tutorial on Gradient-Based Attribution. Methods for Deep Neural Networks. Ian E. Nielsen. Dimah Dera. The University of ...

A tutorial on gradient-based attribution methods for deep neural ...

Robustness is a popular topic for deep learning (DL) research; however, it has been hardly talked about in explainability until very recently.

[2306.03048] From Robustness to Explainability and Back Again

This paper addresses the limitation of scalability of formal explainability, and proposes novel algorithms for computing formal explanations.

Robustness and Explainability of Deep Neural Networks - UIC Indigo

Robustness and Explainability of Deep Neural Networks: Architectures and Applications.

Robust Explainable AI: the Case of Counterfactual Explanations

His research focuses on safe and explainable AI, with special emphasis on counterfactual explanations. Since 2022, he leads the project “ConTrust: Robust ...

Robust, explainable, and privacy-preserving deep learning

This special issue focuses on robust, explainable, and efficient next-generation deep learning algorithms with data privacy and theoretical ...

Robust Explanation Constraints for Neural Networks - OpenReview

We present a method for guaranteeing adversarial robustness of explanations that are based on the input gradient of a neural network.

Learning Robust Rationales for Model Explainability: A Guidance ...

We propose a Guidance-based Rationalization method (G-RAT) that effectively improves robustness against failure situations and the quality of rationales.

Verifiable, Robust, and Explainable AI - d3aconference.dk

Explainable AI (XAI) plays a crucial role in ensuring the verifiability and robustness of AI systems.

CVPR Tutorial Robustness at Inference: Towards Explainability ...

In this tutorial, we provide a human-centric approach to understanding robustness in neural networks that allow AI systems to function in society.

Towards Robust, Explainable and Fair Machine Learning Models ...

We propose to learn a unified framework that is accurate, robust, explainable and fair, via casual learning by capturing the causal features.

Towards robust explanations for deep neural networks - ScienceDirect

In this paper, we develop methods to make explanations provably more robust against attacks that manipulate the input.

A tutorial on gradient-based attribution methods for deep neural ...

On the other hand, robustness is a popular topic for deep learning (DL) research; however, it has been hardly talked about in explainability until very recently ...

AI Explainability Requires Robustness | by Klas Leino

Models that are trained to be robust to adversarial input perturbations exhibit higher-quality explanations.

Why Model Validation Can End the AI “Explainability Crisis”

Robust Intelligence's RIME offers a solution to model validation. The RI Model Engine (RIME) performs stress-testing on models before they ...

How explainable are adversarially-robust CNNs? - Anh Totti Nguyen

Three important criteria of existing convolutional neural networks (CNNs) are (1) test-set accuracy; (2) out-of-distribution accuracy; ...

Explainable and Robust Artificial Intelligence for Trustworthy ...

This article sheds light on the importance and means of achieving explainability and robustness toward trustworthy AI-based RRM solutions for 6G networks.

Machine Learning Explainability and Robustness - ACM Digital Library

Abstract. This tutorial examines the synergistic relationship between explainability methods for machine learning and a significant problem ...

Towards Robust Interpretability with Self-Explaining Neural Networks

Most recent work on interpretability of complex machine learning models has focused on estimating a posteriori explanations for previously trained models.