Title: Fair Kernel Learning,
Abstract: 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.