5,115 research outputs found
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
As machine learning is increasingly used to make real-world decisions, recent
research efforts aim to define and ensure fairness in algorithmic decision
making. Existing methods often assume a fixed set of observable features to
define individuals, but lack a discussion of certain features not being
observed at test time. In this paper, we study fairness of naive Bayes
classifiers, which allow partial observations. In particular, we introduce the
notion of a discrimination pattern, which refers to an individual receiving
different classifications depending on whether some sensitive attributes were
observed. Then a model is considered fair if it has no such pattern. We propose
an algorithm to discover and mine for discrimination patterns in a naive Bayes
classifier, and show how to learn maximum likelihood parameters subject to
these fairness constraints. Our approach iteratively discovers and eliminates
discrimination patterns until a fair model is learned. An empirical evaluation
on three real-world datasets demonstrates that we can remove exponentially many
discrimination patterns by only adding a small fraction of them as constraints
Discriminating word senses with tourist walks in complex networks
Patterns of topological arrangement are widely used for both animal and human
brains in the learning process. Nevertheless, automatic learning techniques
frequently overlook these patterns. In this paper, we apply a learning
technique based on the structural organization of the data in the attribute
space to the problem of discriminating the senses of 10 polysemous words. Using
two types of characterization of meanings, namely semantical and topological
approaches, we have observed significative accuracy rates in identifying the
suitable meanings in both techniques. Most importantly, we have found that the
characterization based on the deterministic tourist walk improves the
disambiguation process when one compares with the discrimination achieved with
traditional complex networks measurements such as assortativity and clustering
coefficient. To our knowledge, this is the first time that such deterministic
walk has been applied to such a kind of problem. Therefore, our finding
suggests that the tourist walk characterization may be useful in other related
applications
A Confidence-Based Approach for Balancing Fairness and Accuracy
We study three classical machine learning algorithms in the context of
algorithmic fairness: adaptive boosting, support vector machines, and logistic
regression. Our goal is to maintain the high accuracy of these learning
algorithms while reducing the degree to which they discriminate against
individuals because of their membership in a protected group.
Our first contribution is a method for achieving fairness by shifting the
decision boundary for the protected group. The method is based on the theory of
margins for boosting. Our method performs comparably to or outperforms previous
algorithms in the fairness literature in terms of accuracy and low
discrimination, while simultaneously allowing for a fast and transparent
quantification of the trade-off between bias and error.
Our second contribution addresses the shortcomings of the bias-error
trade-off studied in most of the algorithmic fairness literature. We
demonstrate that even hopelessly naive modifications of a biased algorithm,
which cannot be reasonably said to be fair, can still achieve low bias and high
accuracy. To help to distinguish between these naive algorithms and more
sensible algorithms we propose a new measure of fairness, called resilience to
random bias (RRB). We demonstrate that RRB distinguishes well between our naive
and sensible fairness algorithms. RRB together with bias and accuracy provides
a more complete picture of the fairness of an algorithm
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
Machine learning is being integrated into a growing number of critical
systems with far-reaching impacts on society. Unexpected behaviour and unfair
decision processes are coming under increasing scrutiny due to this widespread
use and its theoretical considerations. Individuals, as well as organisations,
notice, test, and criticize unfair results to hold model designers and
deployers accountable. We offer a framework that assists these groups in
mitigating unfair representations stemming from the training datasets. Our
framework relies on two inter-operating adversaries to improve fairness. First,
a model is trained with the goal of preventing the guessing of protected
attributes' values while limiting utility losses. This first step optimizes the
model's parameters for fairness. Second, the framework leverages evasion
attacks from adversarial machine learning to generate new examples that will be
misclassified. These new examples are then used to retrain and improve the
model in the first step. These two steps are iteratively applied until a
significant improvement in fairness is obtained. We evaluated our framework on
well-studied datasets in the fairness literature -- including COMPAS -- where
it can surpass other approaches concerning demographic parity, equality of
opportunity and also the model's utility. We also illustrate our findings on
the subtle difficulties when mitigating unfairness and highlight how our
framework can assist model designers.Comment: 15 pages, 3 figures, 1 tabl
Weka: A machine learning workbench for data mining
The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up large-scale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. The system is written in Java and distributed under the terms of the GNU General Public License
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