1,001 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
Is it ethical to avoid error analysis?
Machine learning algorithms tend to create more accurate models with the
availability of large datasets. In some cases, highly accurate models can hide
the presence of bias in the data. There are several studies published that
tackle the development of discriminatory-aware machine learning algorithms. We
center on the further evaluation of machine learning models by doing error
analysis, to understand under what conditions the model is not working as
expected. We focus on the ethical implications of avoiding error analysis, from
a falsification of results and discrimination perspective. Finally, we show
different ways to approach error analysis in non-interpretable machine learning
algorithms such as deep learning.Comment: Presented as a poster at the 2017 Workshop on Fairness,
Accountability, and Transparency in Machine Learning (FAT/ML 2017
Quantifying and Reducing Stereotypes in Word Embeddings
Machine learning algorithms are optimized to model statistical properties of
the training data. If the input data reflects stereotypes and biases of the
broader society, then the output of the learning algorithm also captures these
stereotypes. In this paper, we initiate the study of gender stereotypes in {\em
word embedding}, a popular framework to represent text data. As their use
becomes increasingly common, applications can inadvertently amplify unwanted
stereotypes. We show across multiple datasets that the embeddings contain
significant gender stereotypes, especially with regard to professions. We
created a novel gender analogy task and combined it with crowdsourcing to
systematically quantify the gender bias in a given embedding. We developed an
efficient algorithm that reduces gender stereotype using just a handful of
training examples while preserving the useful geometric properties of the
embedding. We evaluated our algorithm on several metrics. While we focus on
male/female stereotypes, our framework may be applicable to other types of
embedding biases.Comment: presented at 2016 ICML Workshop on #Data4Good: Machine Learning in
Social Good Applications, New York, N
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