Fetching the paper…
Reading the bibliography…
In the quest for Explainable Artificial Intelligence (XAI) one of the questions that frequently arises given a decision made by an AI system is, ``why was the decision made in this way?'' Formal approaches to explainability build a formal model of the AI system and use this to reason about the properties of the system.
An almost optimal algorithm for unbounded searching
Jon Louis Bentley and Andrew Chi-Chih Yao · 1976
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Fabian Pedregosa and et al · 2011
Earlier work this paper cites.
Minimum satisfying assignments for SMT
Isil Dillig, Thomas Dillig, Kenneth L. McMillan, and Alex Aiken · 2012
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
Earlier work this paper cites.
”why should I trust you?”: Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
Earlier work this paper cites.
PMLB: a large benchmark suite for machine learning evaluation and comparison
Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore · 2017
Earlier work this paper cites.
On the glucose SAT solver
Gilles Audemard and Laurent Simon · 2018
Earlier work this paper cites.
PySAT: A python toolkit for prototyping with SAT oracles
Alexey Ignatiev, António Morgado, and Joao Marques-Silva · 2018
Earlier work this paper cites.
Anchors: High-precision model-agnostic explanations
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Earlier work this paper cites.
A symbolic approach to explaining bayesian network classifiers
Andy Shih, Arthur Choi, and Adnan Darwiche · 2018
Earlier work this paper cites.
Abduction-based explanations for machine learning models
Alexey Ignatiev, Nina Narodytska, and Joao Marques-Silva · 2019
Earlier work this paper cites.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
Earlier work this paper cites.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
Earlier work this paper cites.
Towards explainable artificial intelligence
Wojciech Samek and Klaus-Robert Müller · 2019
Earlier work this paper cites.
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus-Robert Müller, editors · 2019
Earlier work this paper cites.
On tractable XAI queries based on compiled representations
Gilles Audemard, Frédéric Koriche, and Pierre Marquis · 2020
Earlier work this paper cites.
A symbolic approach for counterfactual explanations
Ryma Boumazouza, Fahima Cheikh Alili, Bertrand Mazure, and Karim Tabia · 2020
Cited alongside, same era.
On symbolically encoding the behavior of random forests
Arthur Choi, Andy Shih, Anchal Goyanka, and Adnan Darwiche · 2020
Cited alongside, same era.
From contrastive to abductive explanations and back again
Alexey Ignatiev, Nina Narodytska, Nicholas Asher, and Joao Marques-Silva · 2020
Cited alongside, same era.
Towards trustable explainable AI
Alexey Ignatiev · 2020
Cited alongside, same era.
Interpretable Machine Learning
Christoph Molnar · 2020
Cited alongside, same era.
https://archive.ics.uci.edu/ml , 2020
UCI Machine Learning Repository · 2020
Cited alongside, same era.
Axiomatic foundations of explainability
Leila Amgoud and Jonathan Ben-Naim · 2022
Later among the works it cites.
On computing probabilistic explanations for decision trees
Marcelo Arenas, Pablo Barceló, Miguel Romero, and Bernardo Subercaseaux · 2022
Later among the works it cites.
On the explanatory power of boolean decision trees
Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, and Pierre Marquis · 2022
Later among the works it cites.
Sufficient reasons for classifier decisions in the presence of domain constraints
Niku Gorji and Sasha Rubin · 2022
Later among the works it cites.
On tackling explanation redundancy in decision trees
Yacine Izza, Alexey Ignatiev, and Joao Marques-Silva · 2022
Later among the works it cites.
A logic of ”black box” classifier systems
Xinghan Liu and Emiliano Lorini · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Non-monotonic explanation functions
Leila Amgoud · 2021
Cited alongside, same era.
On the computational intelligibility of boolean classifiers
Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, and Pierre Marquis · 2021
Cited alongside, same era.
ASTERYX: A model-agnostic sat-based approach for symbolic and score-based explanations
Ryma Boumazouza, Fahima Cheikh Alili, Bertrand Mazure, and Karim Tabia · 2021
Cited alongside, same era.
On the tractability of explaining decisions of classifiers
Martin C. Cooper and Joao Marques-Silva · 2021
Cited alongside, same era.
On efficiently explaining graph-based classifiers
Xuanxiang Huang, Yacine Izza, Alexey Ignatiev, and João Marques-Silva · 2021
Cited alongside, same era.
SAT-based rigorous explanations for decision lists
Alexey Ignatiev and Joao Marques-Silva · 2021
Cited alongside, same era.
Delivering trustworthy AI through formal XAI
Joao Marques-Silva and Alexey Ignatiev · 2022
Later among the works it cites.
Logic-based explainability in machine learning
João Marques-Silva · 2022
Later among the works it cites.
Interpretable machine learning: Fundamental principles and 10 grand challenges
Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong · 2022
Later among the works it cites.
Toward verified artificial intelligence
Sanjit A. Seshia, Dorsa Sadigh, and S. Shankar Sastry · 2022
Later among the works it cites.
Explaining black-box classifiers: Properties and functions
Leila Amgoud · 2023
Closest in time.
Towards formal XAI: formally approximate minimal explanations of neural networks
Shahaf Bassan and Guy Katz · 2023
Closest in time.
Tractability of explaining classifier decisions
Martin C. Cooper and Joao Marques-Silva · 2023
Closest in time.
On the (complete) reasons behind decisions
Adnan Darwiche and Auguste Hirth · 2023
Closest in time.
A new class of explanations for classifiers with non-binary features
Chunxi Ji and Adnan Darwiche · 2023
Closest in time.
A unified logical framework for explanations in classifier systems
Xinghan Liu and Emiliano Lorini · 2023
Closest in time.