Add to ORKGInternational audienceComplex machine learning algorithms are used more and more often in critical tasks involving text data, leading to the development of interpretability methods. Among local methods, two families have emerged: those computing importance scores for each feature and those extracting simple logical rules. In this paper we show that using different methods can lead to unexpectedly different explanations, even when applied to simple models for which we would expect qualitative coincidence. To quantify this effect, we propose a new approach to compare explanations produced by different methods
Feature attribution a.k.a. input salience methods which assign an importance score to a feature are ...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
International audienceRule-based explanations are a popular method to understand the rationale behin...
Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) ...
We take inspiration from the study of human explanation to inform the design and evaluation of inter...
10+2 page paper, 15-page appendixAnchors [Ribeiro et al. (2018)] is a post-hoc, rule-based interpret...
Interpretability is becoming an active research topic as machine learning (ML) models are more widel...
Machine Learning (ML) is a rapidly growing field. There has been a surge of complex black-box models...
While a lot of research in explainable AI focuses on producing effective explanations, less work is ...
In this review, we examine the problem of designing interpretable and explainable machine learning m...
In the field of Explainable AI, multiples evaluation metrics have been proposed in order to assess t...
Recent years witnessed a number of proposals for the use of the so-called interpretable models in sp...
Feature importance is an approach that helps to explain machine learning model predictions. It works...
Widely used in a growing number of domains, Deep Learning predictors are achieving remarkable result...
Feature attribution a.k.a. input salience methods which assign an importance score to a feature are ...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
International audienceRule-based explanations are a popular method to understand the rationale behin...
Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) ...
We take inspiration from the study of human explanation to inform the design and evaluation of inter...
10+2 page paper, 15-page appendixAnchors [Ribeiro et al. (2018)] is a post-hoc, rule-based interpret...
Interpretability is becoming an active research topic as machine learning (ML) models are more widel...
Machine Learning (ML) is a rapidly growing field. There has been a surge of complex black-box models...
While a lot of research in explainable AI focuses on producing effective explanations, less work is ...
In this review, we examine the problem of designing interpretable and explainable machine learning m...
In the field of Explainable AI, multiples evaluation metrics have been proposed in order to assess t...
Recent years witnessed a number of proposals for the use of the so-called interpretable models in sp...
Feature importance is an approach that helps to explain machine learning model predictions. It works...
Widely used in a growing number of domains, Deep Learning predictors are achieving remarkable result...
Feature attribution a.k.a. input salience methods which assign an importance score to a feature are ...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...
Machine learning models often exhibit complex behavior that is difficult to understand. Recent resea...