Fair Kernel Learning

By A. Pérez-Suay, Valero Laparra, Gonzalo Mateo-García, Jordi Muñoz-Marí, Luis Gómez-Chova, Gustau Camps-Valls. 2017

97 citations recorded by the source corpus.

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.

Subfields: Safety Research, Sociology and Political Science

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Citations in the corpus

Fair Regression: Quantitative Definitions and Reduction-based Algorithms (2019, background): “P´erez-Suay et al. (2017) seek zero correlation in a reproducing kernel Hilbert space (RKHS), which can capture statistical independence, but it only yields predictors in the same RKHS and the loss is limited to least squares.”
Taking Advantage of Multitask Learning for Fair Classification (2018, background): “In many recent works [2–4 8 9 16 17 17 25 26 28 30 35–38] it has been shown how to enforce these constraints during the learning phase of a classifier.”
Empirical Risk Minimization under Fairness Constraints (2018, background): “Methods in the second family enforce fairness directly during the training step: [18, 19, 20, 3, 21, 22, 23, 24, 25, 26] are examples of this method which provide non-convex approaches to the solution of the problem or they derive consistency results just for the non-convex formulation resorting lat”
Fairness risk measures (2019, background): “One can replace the KL divergence in (6) with other measures of dissimilarity between distributions, e.g., an f -divergence [Komiyama and Shimao, 2017] or Hilbert-Schmidt criterion [P´erez-Suay et al., 2017].”
Pairwise Fairness for Ranking and Regression (2019, background): “[27] regularize to minimize the Hilbert-Schmidt independence between the protected features and model output.”
Rényi Fair Inference (2019, methodology): “Thus, research on fairness in machine learning encountered significant attention in recent years; see [11, 20, 25, 56, 52, 17, 21, 51, 53, 54, 37, 6].”
Fairness in Machine Learning (2020, methodology): “…21, 22, 23, 25, 28, 29, 31, 34, 44, 45, 46, 52, 53, 54, 55, 56, 59, 66, 69, 71, 72, 73, 74, 75, 82, 84, 87, 89, 90, 94, 95, 97, 98, 99, 103, 104, 106, 114, 115, 116, 121, 123, 126, 138, 139, 141, 148, 149, 153, 156, 159, 165, 174, 176, 177, 178, 179, 180, 182, 183, 184, 186, 187, 191, 192, 193].”
Fairness in Machine Learning: A Survey (2020, unknown): “[218] propose methods for fair regression as well as fair dimensionality reduction using a Hilbert Schmidt independence criterion and a projection-based methodology that is able to consider multiple sensitive attributes simultaneously.”