16 research outputs found
Counterfactual Explanation via Search in Gaussian Mixture Distributed Latent Space
Counterfactual Explanations (CEs) are an important tool in Algorithmic
Recourse for addressing two questions: 1. What are the crucial factors that led
to an automated prediction/decision? 2. How can these factors be changed to
achieve a more favorable outcome from a user's perspective? Thus, guiding the
user's interaction with AI systems by proposing easy-to-understand explanations
and easy-to-attain feasible changes is essential for the trustworthy adoption
and long-term acceptance of AI systems. In the literature, various methods have
been proposed to generate CEs, and different quality measures have been
suggested to evaluate these methods. However, the generation of CEs is usually
computationally expensive, and the resulting suggestions are unrealistic and
thus non-actionable. In this paper, we introduce a new method to generate CEs
for a pre-trained binary classifier by first shaping the latent space of an
autoencoder to be a mixture of Gaussian distributions. CEs are then generated
in latent space by linear interpolation between the query sample and the
centroid of the target class. We show that our method maintains the
characteristics of the input sample during the counterfactual search. In
various experiments, we show that the proposed method is competitive based on
different quality measures on image and tabular datasets -- efficiently returns
results that are closer to the original data manifold compared to three
state-of-the-art methods, which are essential for realistic high-dimensional
machine learning applications.Comment: XAI workshop of IJCAI 202
Interpretable Distribution-Invariant Fairness Measures for Continuous Scores
Measures of algorithmic fairness are usually discussed in the context of
binary decisions. We extend the approach to continuous scores. So far,
ROC-based measures have mainly been suggested for this purpose. Other existing
methods depend heavily on the distribution of scores, are unsuitable for
ranking tasks, or their effect sizes are not interpretable. Here, we propose a
distributionally invariant version of fairness measures for continuous scores
with a reasonable interpretation based on the Wasserstein distance. Our
measures are easily computable and well suited for quantifying and interpreting
the strength of group disparities as well as for comparing biases across
different models, datasets, or time points. We derive a link between the
different families of existing fairness measures for scores and show that the
proposed distributionally invariant fairness measures outperform ROC-based
fairness measures because they are more explicit and can quantify significant
biases that ROC-based fairness measures miss. Finally, we demonstrate their
effectiveness through experiments on the most commonly used fairness benchmark
datasets
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Feature selection can be a crucial factor in obtaining robust and accurate
predictions. Online feature selection models, however, operate under
considerable restrictions; they need to efficiently extract salient input
features based on a bounded set of observations, while enabling robust and
accurate predictions. In this work, we introduce FIRES, a novel framework for
online feature selection. The proposed feature weighting mechanism leverages
the importance information inherent in the parameters of a predictive model. By
treating model parameters as random variables, we can penalize features with
high uncertainty and thus generate more stable feature sets. Our framework is
generic in that it leaves the choice of the underlying model to the user.
Strikingly, experiments suggest that the model complexity has only a minor
effect on the discriminative power and stability of the selected feature sets.
In fact, using a simple linear model, FIRES obtains feature sets that compete
with state-of-the-art methods, while dramatically reducing computation time. In
addition, experiments show that the proposed framework is clearly superior in
terms of feature selection stability.Comment: To be published in the Proceedings of the 26th ACM SIGKDD Conference
on Knowledge Discovery and Data Mining (KDD 2020
Causal Fairness-Guided Dataset Reweighting using Neural Networks
The importance of achieving fairness in machine learning models cannot be
overstated. Recent research has pointed out that fairness should be examined
from a causal perspective, and several fairness notions based on the on Pearl's
causal framework have been proposed. In this paper, we construct a reweighting
scheme of datasets to address causal fairness. Our approach aims at mitigating
bias by considering the causal relationships among variables and incorporating
them into the reweighting process. The proposed method adopts two neural
networks, whose structures are intentionally used to reflect the structures of
a causal graph and of an interventional graph. The two neural networks can
approximate the causal model of the data, and the causal model of
interventions. Furthermore, reweighting guided by a discriminator is applied to
achieve various fairness notions. Experiments on real-world datasets show that
our method can achieve causal fairness on the data while remaining close to the
original data for downstream tasks.Comment: To be published in the proceedings of 2023 IEEE International
Conference on Big Data (IEEE BigData 2023
Explanation Shift: Investigating Interactions between Models and Shifting Data Distributions
As input data distributions evolve, the predictive performance of machine
learning models tends to deteriorate. In practice, new input data tend to come
without target labels. Then, state-of-the-art techniques model input data
distributions or model prediction distributions and try to understand issues
regarding the interactions between learned models and shifting distributions.
We suggest a novel approach that models how explanation characteristics shift
when affected by distribution shifts. We find that the modeling of explanation
shifts can be a better indicator for detecting out-of-distribution model
behaviour than state-of-the-art techniques. We analyze different types of
distribution shifts using synthetic examples and real-world data sets. We
provide an algorithmic method that allows us to inspect the interaction between
data set features and learned models and compare them to the state-of-the-art.
We release our methods in an open-source Python package, as well as the code
used to reproduce our experiments.Comment: arXiv admin note: text overlap with arXiv:2210.1236
Bias in data-driven artificial intelligence systems - An introductory survey
Artificial Intelligence (AI)âbased systems are widely employed nowadays to make decisions that have farâreaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches wellâgrounded in a legal frame. In this survey, we focus on dataâdriven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth