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Found 14 papers, 6 papers with code
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
In the field of algorithmic fairness, significant attention has been put on group fairness criteria, such as Demographic Parity and Equalized Odds.
Achieving Diversity in Counterfactual Explanations: a Review and Discussion
In the field of Explainable Artificial Intelligence (XAI), counterfactual examples explain to a user the predictions of a trained decision model by indicating the modifications to be made to the instance so as to change its associated prediction.
When Mitigating Bias is Unfair: A Comprehensive Study on the Impact of Bias Mitigation Algorithms
With comparable performances in fairness and accuracy, are the different bias mitigation approaches impacting a similar number of individuals?
Integrating Prior Knowledge in Post-hoc Explanations
In the field of eXplainable Artificial Intelligence (XAI), post-hoc interpretability methods aim at explaining to a user the predictions of a trained decision model.
How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice
Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness.
Understanding surrogate explanations: the interplay between complexity, fidelity and coverage
This paper analyses the fundamental ingredients behind surrogate explanations to provide a better understanding of their inner workings.
On the overlooked issue of defining explanation objectives for local-surrogate explainers
In this work we review the similarities and differences amongst multiple methods, with a particular focus on what information they extract from the model, as this has large impact on the output: the explanation.
Understanding Prediction Discrepancies in Machine Learning Classifiers
This paper proposes to address this question by analyzing the prediction discrepancies in a pool of best-performing models trained on the same data.
Imperceptible Adversarial Attacks on Tabular Data
Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions.
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model.
Issues with post-hoc counterfactual explanations: a discussion
Counterfactual post-hoc interpretability approaches have been proven to be useful tools to generate explanations for the predictions of a trained blackbox classifier.
Detecting Potential Local Adversarial Examples for Human-Interpretable Defense
Machine learning models are increasingly used in the industry to make decisions such as credit insurance approval.
Defining Locality for Surrogates in Post-hoc Interpretablity
Local surrogate models, to approximate the local decision boundary of a black-box classifier, constitute one approach to generate explanations for the rationale behind an individual prediction made by the back-box.
Inverse Classification for Comparison-based Interpretability in Machine Learning
In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data).
