Meta-theorems validate fair regression algorithms under demographic parity constraints.
problem Regression under demographic parity constraints.
method Meta-theorems and post-processing methods.
result Fair minimax optimal regression can be achieved through post-processing.
Paper introduces methods to create fair and accurate regression models.
problem Creating fair and accurate regression models.
method Mixed-integer optimization methods, exact formulations, branch-and-bound algorithm, coordinate descent algorithm.
result Developed methods produce fair and accurate models with reduced training times.
Proposes a method for fair regression using RKHS.
problem Ensuring fairness in regression models with multiple sensitive attributes.
method Uses reproducing kernel Hilbert space (RKHS) to construct a functional space that satisfies MP fairness.
result Derives a closed-form solution for fair regression that is efficient and interpretable.
The paper explores fair regression and classification under demographic parity constraints.
problem Ensuring fairness in regression and classification models under demographic parity constraints.
method Characterizes the optimal fair regression function using a barycenter problem with optimal transport costs and studies the connection between fair classification and regression.
result The optimal fair regression function is derived from the solution to a barycenter problem with optimal transport costs, and the optimal fair cost-sensitive classifiers can be derived by applying thresholds to this function.
Study fairness in ordinal regression using threshold models.
problem Fairness in ordinal regression predictions.
method Adapted fairness notions from fair ranking; use threshold model with scoring function and thresholds; apply binary classification for scoring function and local search for thresholds.
result Generalization guarantees on predictor error and fairness violation; effectiveness demonstrated in experiments.
New fair regression method improves fairness in chronic kidney disease classification.
problem Mitigating societal bias in health care for multiple groups.
method Penalized fair regression framework for multiple groups, with penalties for true positive rate disparity.
result Achieves fairness-accuracy frontier beyond existing methods in simulations and real-world data.
Unified framework for fair regression under demographic parity.
problem Ensuring fairness in regression tasks subject to demographic parity constraints.
method Proposes a unified framework applicable to various regression tasks with a broad spectrum of loss functions, derived a novel characterization of the fair risk minimizer, and established theoretical consistency and convergence rates.
result Effective minimization of risk while satisfying fairness constraints across various regression settings.
In this paper, we study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a protected attribute such as gender or race. We call this class of problems \emph{fair regression}. We propose general schemes for fair regressio…
The paper develops a method to achieve fairness in predictions using Wasserstein barycenters.
problem Learning a fair real-valued function independent of sensitive attributes.
method Establishing a connection between fair regression and optimal transport theory, deriving a close form expression for the optimal fair predictor as the Wasserstein barycenter of sensitive groups.
result The optimal fair predictor's distribution is the Wasserstein barycenter of sensitive groups' distributions, offering an intuitive interpretation and a simple post-processing algorithm.
Proposes a method to achieve quantile fairness in predictions.
problem Lack of research on quantile fairness in socially sensitive domains.
method Introduces a framework to learn a real-valued quantile function under Demographic Parity fairness.
result Demonstrates superior empirical performance and uncovering fairness-accuracy trade-offs.
In recent years, there have been significant efforts on mitigating unethical demographic biases in machine learning methods. However, very little is done for kernel methods. In this paper, we propose a new fair kernel regression method via fair feature embedding (FKR-F2E) in kernel space. Motivated by prior works on…
The paper explores the tradeoff between fairness and accuracy in regression models.
problem Characterizing the tradeoff between fairness and accuracy in regression models.
method Provided a lower bound on the error of any fair regressor and extended the result to joint error using Wasserstein distance.
result Lower bounds on the error of fair regressors and their connection to Wasserstein distance.
Project fair estimators while maintaining accuracy.
problem Making estimators fair without sacrificing accuracy.
method Optimal transport tools to find closest fair estimator.
result Efficiently constructs fair estimators with quantified cost.
New method for fair regression using optimal transport.
problem Learning fair regression models under counterfactual fairness constraints.
method Causal uncertainty view, optimal transport, post-processing method.
result High-probability fairness guarantees with O(n−1/3) decay. We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the rang from notions of group fairness to strong individual fairness. By varying the weight on the fairness regularizer, we can comp…
New framework enforces demographic parity on distribution tails.
problem Enforcing demographic parity on entire distribution can degrade accuracy.
method Optimal transport theory, focusing on distribution tails.
result More nuanced and context-sensitive fairness interventions.
New algorithm tackles subgroup fairness in AI with multiple sensitive attributes.
problem Heavy computational burdens and data sparsity in subgroup fairness for multiple sensitive attributes.
method Doubly Regressing Adversarial learning (DRAF) for subgroup fairness, focusing on subgroups with sufficient sample sizes and marginal fairness.
result DRAF algorithm reduces a surrogate fairness gap for supIPM with less computation than directly reducing supIPM.
Unified framework for fair regression in aware and unaware settings.
problem Lack of principled methods for fair regression in unawareness settings.
method Formulated as an optimal transport problem, unifying aware and unaware settings.
result Characterizes optimal prediction functions via optimal transport maps under different penalties.
Most work in algorithmic fairness to date has focused on discrete outcomes, such as deciding whether to grant someone a loan or not. In these classification settings, group fairness criteria such as independence, separation and sufficiency can be measured directly by comparing rates of outcomes between subpopulations. …
We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the res…
Selective regression allows abstention to improve fairness criteria.
problem Selective regression can exacerbate disparities between subgroups.
method Proposes new fairness criteria and two approaches to mitigate performance disparity.
result Proposed fairness criteria ensures performance improvement for every subgroup with reduced coverage.
The study connects fairness constraints with optimal transport to derive new insights in classification.
problem Ensuring fairness in classification models without sacrificing performance.
method Using Wasserstein barycenters and optimal transport, the study characterizes optimal classification functions under fairness constraints.
result Maximizing fairness under demographic parity is equivalent to solving a regression problem.
Unified framework for fair pricing in long-term insurance products.
problem Unclear generalization of fair pricing methods to long-term products.
method Reformulate multi-state transition models as Poisson regression problems.
result Direct application of existing fair pricing methods to long-term insurance products.
Paper introduces fair GLMs with convex penalty for equalizing GLM outcomes.
problem Achieving fairness in GLMs for practical use.
method Two fairness criteria based on GLM outcomes/log-likelihoods, achieved via a convex penalty on linear components.
result The fair GLM estimator is efficient and can handle various response variables.
Study achieves fairness without demographic info, improving regression tasks.
problem Achieve fairness in models without prior demographic info.
method VFair method to minimize training loss variance, dynamic update approach.
result Regression tasks can achieve significant fairness improvement without prior demographics.
Fair ML systems can be safe ML systems by considering uncertainty.
problem Safety of data-driven decision systems is often neglected.
method Viewing ML systems as socio-technical, uncertainty-aware modeling.
result Fair models should be uncertainty-aware, e.g. through distributional regression.
Study identifies and measures biases in legal case data.
problem Addressing representation biases and sentencing disparities in legal case data.
method Utilizes two regression models: a baseline and a fair judge model.
result Quantifies biases across demographic groups in criminal data from Cook County (Illinois).
We relax demographic parity in regression by enforcing parity at quantile levels and score thresholds.
problem Enforcing full distributional fairness in regression can lead to substantial accuracy loss.
method Introduce (ℓ, Z)-fair predictor, derive closed-form solutions, and develop post-processing algorithm. result The risk gap to the continuous optimum vanishes as the grid is refined, and we enable targeted fairness corrections.
The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk a…
Develops a framework for fair semi-supervised learning.
problem Balancing fairness and accuracy in semi-supervised learning.
method Formulates a framework as an optimization problem, incorporating classifier loss, label propagation loss, and fairness constraints.
result Achieves fair semi-supervised learning with better accuracy-fairness trade-off than fair supervised learning.
Develops a new fairness learning approach for multi-task regression models.
problem Fairness in multi-task regression models with biased datasets.
method Uses rank-based non-parametric independence test (Mann Whitney U statistic) and reformulates as non-convex optimization problem.
result Outperforms state-of-the-art methods on fairness metrics.
Proposes active sampling for improving fairness in machine learning.
problem Improving fairness in machine learning models, especially for disadvantaged groups.
method Simple active sampling and reweighting strategies for min-max fairness.
result Proves the rate of convergence to a min-max fair solution for convex problems.
Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints in kernel regression methods, applicable to Gaussian processes, support vector machines, neural network regression and decision t…
Securely trains fair models using homomorphic encryption.
problem Protecting sensitive features while testing model fairness.
method Fully homomorphic encryption for training and testing.
result Practical application to adult income data set.
We tackle the problem of algorithmic fairness, where the goal is to avoid the unfairly influence of sensitive information, in the general context of regression with possible continuous sensitive attributes. We extend the framework of fair empirical risk minimization to this general scenario, covering in this way the wh…
Enhances fairness in predictions without sacrificing accuracy.
problem Balancing fairness and predictive performance in machine learning.
method Model ensemble-based post-processing framework.
result Framework effectively enhances fairness while maintaining predictive accuracy.
Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming …
fairml simplifies fair machine learning models for interpretable results.
problem Ensuring fairness in machine learning models without sacrificing interpretability.
method Uses classical statistical models and penalized regression to enforce fairness constraints.
result Interpretable fair models that are well-known in statistical properties.
Abstract reviews mathematical fairness in machine learning.
problem Ensuring fairness in machine learning models.
method Independence-based approach to fairness definitions and methodologies.
result Optimal fair classifiers and predictors under equality of odds are derived.
FAIR-NN finds invariant variables for causal inference across diverse environments.
problem Nonparametric invariance and causal learning in regression models with varying joint distributions.
method FAIR-NN framework using adversarial optimization and neural networks.
result FAIR-NN identifies invariant variables and quasi-causal variables under minimal conditions.
Group fairness is an important concern for machine learning researchers, developers, and regulators. However, the strictness to which models must be constrained to be considered fair is still under debate. The focus of this work is on constraining the expected outcome of subpopulations in kernel regression and, in part…
Proposes a fair classification model using robust optimization.
problem Preventing discrimination in classification models.
method Distributionally robust logistic regression with Wasserstein ball and convex unfairness measure.
result Improves fairness with minimal loss in predictive accuracy.
The paper introduces a model to measure ASR fairness, addressing key issues.
problem Measuring fairness in ASR systems for different subgroups.
method Mixed-effects Poisson regression to control nuisance factors and handle unobserved heterogeneity.
result The method effectively addresses WER gaps among subgroups and is flexible for practical analyses.
A non-trivial predictor satisfies demographic parity and equalizes group risks in regression.
problem Achieving fairness in regression models while maintaining equal risks across groups.
method Provided an explicit example of a non-constant predictor satisfying Demographic Parity and Equal Group-Wise Risks.
result First explicit construction of a non-constant predictor satisfying both fairness notions.
Quantile regression is a tool for learning conditional distributions. In this paper we study quantile regression in the setting where a protected attribute is unavailable when fitting the model. This can lead to "unfair'' quantile estimators for which the effective quantiles are very different for the subpopulations de…
Proposes a new method for fairness in machine learning with multiple protected attributes.
problem Ensuring fairness in machine learning models with continuous and multiple protected attributes.
method Distance covariance regularisation framework to mitigate association between model predictions and protected attributes.
result Demonstrates effectiveness in mitigating fairness gerrymandering in regression tasks.
The potential lack of fairness in the outputs of machine learning algorithms has recently gained attention both within the research community as well as in society more broadly. Surprisingly, there is no prior work developing tree-induction algorithms for building fair decision trees or fair random forests. These metho…
Extends Demographic Parity for fairer wage predictions with expert knowledge.
problem Inadequate fairness metrics limit user domain knowledge and ignore intersectional fairness.
method Develops a parametric method to incorporate expert knowledge in fair predictions.
result Offers a robust solution for real-life applications with limited data and spending constraints.