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05/10/2023
Achieving Diversity in Counterfactual Explanations: a Review and Discussion
In the field of Explainable Artificial Intelligence (XAI), counterfactua...
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02/14/2023
When Mitigating Bias is Unfair: A Comprehensive Study on the Impact of Bias Mitigation Algorithms
Most works on the fairness of machine learning systems focus on the blin...
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04/25/2022
Integrating Prior Knowledge in Post-hoc Explanations
In the field of eXplainable Artificial Intelligence (XAI), post-hoc inte...
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02/24/2022
A fair pricing model via adversarial learning
At the core of insurance business lies classification between risky and ...
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09/10/2021
Fairness without the sensitive attribute via Causal Variational Autoencoder
In recent years, most fairness strategies in machine learning models foc...
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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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05/03/2021
Explaining how your AI system is fair
To implement fair machine learning in a sustainable way, choosing the ri...
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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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04/09/2021
Implementing Fair Regression In The Real World
Most fair regression algorithms mitigate bias towards sensitive sub popu...
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02/16/2021
Towards the Right Kind of Fairness in AI
Fairness is a concept of justice. Various definitions exist, some of the...
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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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09/14/2020
Active Fairness Instead of Unawareness
The possible risk that AI systems could promote discrimination by reprod...
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09/07/2020
Learning Unbiased Representations via Rényi Minimization
In recent years, significant work has been done to include fairness cons...
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08/30/2020
Adversarial Learning for Counterfactual Fairness
In recent years, fairness has become an important topic in the machine l...
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08/27/2020
Vehicle Telematics Via Exteroceptive Sensors: A Survey
Whereas a very large number of sensors are available in the automotive f...
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03/15/2020
Getting Fairness Right: Towards a Toolbox for Practitioners
The potential risk of AI systems unintentionally embedding and reproduci...
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11/13/2019
Fair Adversarial Gradient Tree Boosting
Fair classification has become an important topic in machine learning re...
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11/12/2019
Fairness-Aware Neural Réyni Minimization for Continuous Features
The past few years have seen a dramatic rise of academic and societal in...
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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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10/10/2019
Contract Statements Knowledge Service for Chatbots
Towards conversational agents that are capable of handling more complex ...
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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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06/11/2019
Issues with post-hoc counterfactual explanations: a discussion
Counterfactual post-hoc interpretability approaches have been proven to ...
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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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05/22/2019
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
Despite having excellent performances for a wide variety of tasks, moder...
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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