AI robustness: a human-centered perspective on technological challenges and opportunities
Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …
Neuronfair: Interpretable white-box fairness testing through biased neuron identification
Deep neural networks (DNNs) have demonstrated their outperformance in various domains.
However, it raises a social concern whether DNNs can produce reliable and fair decisions …
However, it raises a social concern whether DNNs can produce reliable and fair decisions …
" Is your explanation stable?" A Robustness Evaluation Framework for Feature Attribution
Neural networks have become increasingly popular. Nevertheless, understanding their
decision process turns out to be complicated. One vital method to explain a models' …
decision process turns out to be complicated. One vital method to explain a models' …
Certifying robustness of convolutional neural networks with tight linear approximation
The robustness of neural network classifiers is becoming important in the safety-critical
domain and can be quantified by robustness verification. However, at present, efficient and …
domain and can be quantified by robustness verification. However, at present, efficient and …