38 citations · 73 across the 10 of their papers we have counts for
4 papers · 1 filter
ExplAIner: A Declarative Query Language for Explaining Classification Models
Marcelo Arenas, Pablo Barceló, Diego Bustamante +3
The XAI community has studied a wide range of queries and scores for explaining predictions of ML models. From a data management perspective, this proliferation of explanation noti…
Probabilistic Explanations for Linear Models
Bernardo Subercaseaux, Marcelo Arenas, Kuldeep S Meel
Formal XAI is an emerging field that focuses on providing explanations with mathematical guarantees for the decisions made by machine learning models. A significant amount of work…
Foundations of Symbolic Languages for Model Interpretability
Marcelo Arenas, Daniel Baez, Pablo Barceló +2
Several queries and scores have recently been proposed to explain individual predictions over ML models. Given the need for flexible, reliable, and easy-to-apply interpretability m…
Model Interpretability through the Lens of Computational Complexity
Pablo Barceló, Mikaël Monet, Jorge Pérez +1
In spite of several claims stating that some models are more interpretable than others -- e.g., "linear models are more interpretable than deep neural networks" -- we still lack a…