GLocalX - From Local to Global Explanations of Black Box AI Models
@article{Setzu2021GLocalXF, title={GLocalX - From Local to Global Explanations of Black Box AI Models}, author={Mattia Setzu and Riccardo Guidotti and Anna Monreale and Franco Turini and Dino Pedreschi and Fosca Giannotti}, journal={ArXiv}, year={2021}, volume={abs/2101.07685} }
22 Citations
Towards Knowledge-driven Distillation and Explanation of Black-box Models
- Computer ScienceDAO-XAI
- 2021
A knowledge-driven distillation approach to explaining black-box models by means of perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning.
Logic Programming for XAI: A technical perspective1
- Computer Science
- 2021
This work proposes using Constraint Logic Programming to construct explanations that incorporate prior knowledge, as well as Meta-Reasoning to track model and explanation changes over time.
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines
- Computer ScienceArXiv
- 2021
This paper proposes a technique for aggregating the feature attributions of different explanatory algorithms using Restricted Boltzmann Machines to achieve a more reliable and robust interpretation of deep neural networks.
A Quantitative Evaluation of Global, Rule-Based Explanations of Post-Hoc, Model Agnostic Methods
- Computer ScienceFrontiers in Artificial Intelligence
- 2021
This study proposes a novel comparative approach to evaluate and compare the rulesets produced by five model-agnostic, post-hoc rule extractors by employing eight quantitative metrics, using the Friedman test to check whether a method consistently performed better than the others, in terms of the selected metrics, and could be considered superior.
Model learning with personalized interpretability estimation (ML-PIE)
- Computer ScienceGECCO Companion
- 2021
This paper uses a bi-objective evolutionary algorithm to synthesize models with trade-offs between accuracy and a user-specific notion of interpretability, and finds that the users tend to prefer models found using the proposed approach overmodels found using non-personalized interpretability indices.
Classification of Explainable Artificial Intelligence Methods through Their Output Formats
- Computer ScienceMach. Learn. Knowl. Extr.
- 2021
This systematic review aimed to organise the existing XAI methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats.
An Empirical Investigation Into Deep and Shallow Rule Learning
- Computer ScienceFrontiers in Artificial Intelligence
- 2021
This paper empirically compare deep and shallow rule sets that have been optimized with a uniform general mini-batch based optimization algorithm and finds that deep rule networks outperformed their shallow counterparts, which is taken as an indication that it is worth-while to devote more efforts to learning deep rule structures from data.
Understanding Diversity in Human-AI Data: What Cognitive Style Disaggregation Reveals
- PsychologyArXiv
- 2021
It was found that participants’ cognitive styles not only clustered by their gender, but they also clustered across different age groups, and across all 5 cogniive style spectra, although there were instances where applying the guidelines closed inclusivity issues, there were also stubborn inclusiveness issues and inadvertent introductions of inclusivism issues.
Diagnosing AI Explanation Methods with Folk Concepts of Behavior
- PsychologyArXiv
- 2022
When explaining AI behavior to humans, how is the communicated information being comprehended by the human explainee, and does it match what the explanation attempted to communicate? When can we say…
Benchmarking and Survey of Explanation Methods for Black Box Models
- Computer ScienceArXiv
- 2021
A categorization of explanation methods based on the type of explanation returned is provided, and a visual comparison among explanations is shown and a quantitative benchmarking is shown.
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