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Found 33 papers, 13 papers with code
OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learning
The reverse-engineered nature of traditional models complicates the enforcement of fairness and can lead to biased outcomes.
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.
A Fair Pricing Model via Adversarial Learning
In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.
Fairness without the sensitive attribute via Causal Variational Autoencoder
In recent years, most fairness strategies in machine learning models focus on mitigating unwanted biases by assuming that the sensitive information is observed.
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.
Explaining how your AI system is fair
To implement fair machine learning in a sustainable way, choosing the right fairness objective is key.
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.
Implementing Fair Regression In The Real World
Most fair regression algorithms mitigate bias towards sensitive sub populations and therefore improve fairness at group level.
On the Granularity of Explanations in Model Agnostic NLP Interpretability
Current methods for Black-Box NLP interpretability, like LIME or SHAP, are based on altering the text to interpret by removing words and modeling the Black-Box response.
QUACKIE: A NLP Classification Task With Ground Truth Explanations
NLP Interpretability aims to increase trust in model predictions.
Active Fairness Instead of Unawareness
The possible risk that AI systems could promote discrimination by reproducing and enforcing unwanted bias in data has been broadly discussed in research and society.
Learning Unbiased Representations via Rényi Minimization
We leverage recent work which has been done to estimate this coefficient by learning deep neural network transformations and use it as a minmax game to penalize the intrinsic bias in a multi dimensional latent representation.
Adversarial Learning for Counterfactual Fairness
In recent years, fairness has become an important topic in the machine learning research community.
Vehicle Telematics Via Exteroceptive Sensors: A Survey
Whereas a very large number of sensors are available in the automotive field, currently just a few of them, mostly proprioceptive ones, are used in telematics, automotive insurance, and mobility safety research.
Getting Fairness Right: Towards a Toolbox for Practitioners
The potential risk of AI systems unintentionally embedding and reproducing bias has attracted the attention of machine learning practitioners and society at large.
Fairness-Aware Neural Réyni Minimization for Continuous Features
Second, by minimizing the HGR directly with an adversarial neural network architecture.
Imperceptible Adversarial Attacks on Tabular Data
Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions.
Contract Statements Knowledge Service for Chatbots
Towards conversational agents that are capable of handling more complex questions on contractual conditions, formalizing contract statements in a machine readable way is crucial.
How the Softmax Activation Hinders the Detection of Adversarial and Out-of-Distribution Examples in Neural Networks
Despite having excellent performances for a wide variety of tasks, modern neural networks are unable to provide a prediction with a reliable confidence estimate which would allow to detect misclassifications.
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.
Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees
Interpretable surrogates of black-box predictors trained on high-dimensional tabular datasets can struggle to generate comprehensible explanations in the presence of correlated variables.
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
Despite having excellent performances for a wide variety of tasks, modern neural networks are unable to provide a reliable confidence value allowing to detect misclassifications.
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).
