10 papers · 1 filter
Towards Rigorous Explainability by Feature Attribution
Olivier Létoffé, Xuanxiang Huang, Joao Marques-Silva
For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such methods lack rigor and can mislea…
Interval Certifications for Multilayered Perceptrons via Lattice Traversal
Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris +1
In this work we present a rigorous theoretical framework to a foundational problem of AI safety, namely adversarial robustness. In particular, we show that the adversarial robustne…
Uncovering Bugs in Formal Explainers: A Case Study with PyXAI
Xuanxiang Huang, Yacine Izza, Alexey Ignatiev +1
Formal explainable artificial intelligence (XAI) offers unique theoretical guarantees of rigor when compared to other non-formal methods of explainability. However, little attentio…
Efficient & Correct Predictive Equivalence for Decision Trees
Joao Marques-Silva, Alexey Ignatiev
The Rashomon set of decision trees (DTs) finds importance uses. Recent work showed that DTs computing the same classification function, i.e. predictive equivalent DTs, can represen…
Rigorous Feature Importance Scores based on Shapley Value and Banzhaf Index
Xuanxiang Huang, Olivier Létoffé, Joao Marques-Silva
Feature attribution methods based on game theory are ubiquitous in the field of eXplainable Artificial Intelligence (XAI). Recent works proposed rigorous feature attribution using…
On Trustworthy Rule-Based Models and Explanations
Mohamed Siala, Jordi Planes, Joao Marques-Silva
A task of interest in machine learning (ML) is that of ascribing explanations to the predictions made by ML models. Furthermore, in domains deemed high risk, the rigor of explanati…