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07/09/2021
How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice
Explainability is becoming an important requirement for organizations th...
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07/09/2021
Understanding surrogate explanations: the interplay between complexity, fidelity and coverage
This paper analyses the fundamental ingredients behind surrogate explana...
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06/10/2021
On the overlooked issue of defining explanation objectives for local-surrogate explainers
Local surrogate approaches for explaining machine learning model predict...
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04/12/2021
Understanding Prediction Discrepancies in Machine Learning Classifiers
A multitude of classifiers can be trained on the same data to achieve si...
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12/24/2020
QUACKIE: A NLP Classification Task With Ground Truth Explanations
NLP Interpretability aims to increase trust in model predictions. This m...
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12/24/2020
Sentence-Based Model Agnostic NLP Interpretability
Today, interpretability of Black-Box Natural Language Processing (NLP) m...
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11/08/2019
Imperceptible Adversarial Attacks on Tabular Data
Security of machine learning models is a concern as they may face advers...
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07/22/2019
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
Post-hoc interpretability approaches have been proven to be powerful too...
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06/04/2019
Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees
Interpretable surrogates of black-box predictors trained on high-dimensi...
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09/07/2018
Detecting Potential Local Adversarial Examples for Human-Interpretable Defense
Machine learning models are increasingly used in the industry to make de...
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06/19/2018
Defining Locality for Surrogates in Post-hoc Interpretablity
Local surrogate models, to approximate the local decision boundary of a ...
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12/22/2017
