2 research outputs found
Fair Kernel Learning
New social and economic activities massively exploit big data and machine
learning algorithms to do inference on people's lives. Applications include
automatic curricula evaluation, wage determination, and risk assessment for
credits and loans. Recently, many governments and institutions have raised
concerns about the lack of fairness, equity and ethics in machine learning to
treat these problems. It has been shown that not including sensitive features
that bias fairness, such as gender or race, is not enough to mitigate the
discrimination when other related features are included. Instead, including
fairness in the objective function has been shown to be more efficient.
We present novel fair regression and dimensionality reduction methods built
on a previously proposed fair classification framework. Both methods rely on
using the Hilbert Schmidt independence criterion as the fairness term. Unlike
previous approaches, this allows us to simplify the problem and to use multiple
sensitive variables simultaneously. Replacing the linear formulation by kernel
functions allows the methods to deal with nonlinear problems. For both linear
and nonlinear formulations the solution reduces to solving simple matrix
inversions or generalized eigenvalue problems. This simplifies the evaluation
of the solutions for different trade-off values between the predictive error
and fairness terms. We illustrate the usefulness of the proposed methods in toy
examples, and evaluate their performance on real world datasets to predict
income using gender and/or race discrimination as sensitive variables, and
contraceptive method prediction under demographic and socio-economic sensitive
descriptors.Comment: Work published on ECML'17,
http://ecmlpkdd2017.ijs.si/papers/paperID275.pd
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness
Current adoption of machine learning in industrial, societal and economical
activities has raised concerns about the fairness, equity and ethics of
automated decisions. Predictive models are often developed using biased
datasets and thus retain or even exacerbate biases in their decisions and
recommendations. Removing the sensitive covariates, such as gender or race, is
insufficient to remedy this issue since the biases may be retained due to other
related covariates. We present a regularization approach to this problem that
trades off predictive accuracy of the learned models (with respect to biased
labels) for the fairness in terms of statistical parity, i.e. independence of
the decisions from the sensitive covariates. In particular, we consider a
general framework of regularized empirical risk minimization over reproducing
kernel Hilbert spaces and impose an additional regularizer of dependence
between predictors and sensitive covariates using kernel-based measures of
dependence, namely the Hilbert-Schmidt Independence Criterion (HSIC) and its
normalized version. This approach leads to a closed-form solution in the case
of squared loss, i.e. ridge regression. Moreover, we show that the dependence
regularizer has an interpretation as modifying the corresponding Gaussian
process (GP) prior. As a consequence, a GP model with a prior that encourages
fairness to sensitive variables can be derived, allowing principled
hyperparameter selection and studying of the relative relevance of covariates
under fairness constraints. Experimental results in synthetic examples and in
real problems of income and crime prediction illustrate the potential of the
approach to improve fairness of automated decisions