Two simple methods learn fair metrics from data to improve fairness in ML tasks.
problem Lack of widely accepted fair metrics for many ML tasks hinders individual fairness adoption.
method Presented two simple ways to learn fair metrics from various data types.
result Fair training with learned metrics improves fairness on three ML tasks.
The paper tackles fairness in data and algorithms, expanding on prior work.
problem Discrimination and disparate treatment in data and algorithms.
method Targeted learning for nonparametric inference of fairness in the data generating process.
result Derivation and validation of estimators for fairness metrics like demographic parity and equal opportunity.
Fair Mixup improves fairness in classifiers by interpolating between groups.
problem Ensuring fairness in classifiers during training and evaluation.
method Fair Mixup uses interpolation of samples between groups to enforce fairness constraints.
result Fair Mixup ensures better generalization of fairness in various benchmarks.
Proposes individual fairness for clustering, making data points prefer their own cluster.
problem No fair clustering for clustering data points.
method Introduces a new fairness notion for clustering and studies its feasibility and heuristics.
result Individual fairness for clustering is NP-hard in general but feasible for one-dimensional data.
Paper creates fair synthetic data ensuring equal predictions across sensitive attributes.
problem Ensuring fair predictions across sensitive attributes in synthetic data.
method Equalizing target probability distributions across sensitive attributes in synthetic data generation.
result Synthetic data provides strong fair predictions, equal across all thresholds.
DECAF generates fair synthetic data by embedding causal relationships.
problem Generating fair synthetic data from biased training data.
method DECAF uses a GAN with a structural causal model to embed causal relationships and debias synthetic data.
result DECAF successfully removes bias and generates high-quality synthetic data.
Bayesian data selection framework ensures fairness in machine learning models.
problem High computational costs and limited scalability of fairness-aware methods.
method Bayesian data selection framework using generalized discrepancy measures.
result Consistently outperforms existing methods in fairness and accuracy.
New method certifies individual fairness in representations.
problem Ensuring fairness in data representations without sacrificing utility.
method Mapping similar individuals to close latent representations to certify individual fairness.
result Certifies individual fairness for existing and new data points.
Fairness-aware learning is increasingly important in data mining. Discrimination prevention aims to prevent discrimination in the training data before it is used to conduct predictive analysis. In this paper, we focus on fair data generation that ensures the generated data is discrimination free. Inspired by generative…
Paper examines fairness of data augmentation methods, finding vanilla Mixup outperforms Fair Mixup.
problem Improving fairness in classification models with limited minority data.
method Uses multicalibration to rigorously evaluate and improve data augmentation methods for classification fairness.
result Vanilla Mixup outperforms Fair Mixup and baseline methods in fairness and accuracy, especially with small minority groups.
Fair machine learning models can be vulnerable to adversarial attacks that reduce their accuracy and fairness.
problem Fairness constraints in machine learning models can compromise their robustness against adversarial attacks.
method Analysis of data poisoning attacks on group-based fair machine learning models, focusing on equalized odds.
result Adversaries can significantly reduce the test accuracy of fair machine learning models and widen their fairness gap.
Develops algorithm to make fair decisions from biased data.
problem Ethical concerns in machine learning fairness.
method Fair Learning through Data Preprocessing (FLAP) algorithm.
result Counterfactual fairness equivalent to conditional independence.
FBC clusters data fairly without needing cluster count.
problem Fairness in clustering groups of different sensitive groups.
method Developed a Bayesian model-based clustering method with a fair prior and efficient MCMC algorithm.
result Reasonably infers the number of clusters and achieves a fair utility trade-off.
Improves fairness in machine learning by adding underrepresented group data.
problem Machine learning biases across subgroups due to under-representation or societal biases.
method Data augmentation via pairwise mixup across subgroups to balance subpopulations.
result Achieves fair outcomes with robust if not improved accuracy.
The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can…
The paper connects counterfactual fairness to robust prediction and group fairness using causal context.
problem The challenge of ensuring fairness in AI systems when counterfactuals cannot be directly observed.
method Using causal context to bridge counterfactual fairness, robust prediction, and group fairness.
result Counterfactual fairness is equivalent to group fairness metrics in specific contexts.
New algorithms handle missing data to improve fairness in machine learning.
problem Missing values in data can lead to unfair outcomes in machine learning models.
method Developed scalable and adaptive algorithms to handle missing values while preserving predictive information.
result Our adaptive algorithms consistently achieve higher fairness and accuracy than standard impute-then-classify methods.
We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose f…
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.
Motivated by concerns surrounding the fairness effects of sharing and transferring fair machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The first is a model-agnostic algorithm that provides interpretable boundary conditions for when a fairly trained model may not behave fairly on sim…
The paper tackles fairness in forecasting and learning linear dynamical systems.
problem Under-representation bias in training data for multiple subgroups.
method Introducing subgroup-fair and instant-fair learning of LDS from multiple trajectories of varying lengths, using hierarchies of convexifications of non-commutative polynomial optimisation problems.
result Empirical results show both the beneficial impact of fairness considerations on statistical performance and encouraging effects of exploiting sparsity on run time.
Study highlights how lazy data practices in fair ML research can unfairly impact minority groups.
problem Lazy data practices in fair ML research can unfairly impact minority groups.
method Systematic study of 280 experiments across 142 publications on 142 datasets.
result Unreflective data practices lead to biased findings and unfair treatment of minorities.
Synthetic data mimics real-world demographics for fairness testing.
problem Lack of complete, representative datasets for fairness testing.
method Construct synthetic datasets using overlapping real and separate datasets.
result Synthetic data yields consistent fairness metrics with real data.
Boosting improves data fitting while maintaining fairness guarantees.
problem Ensuring fairness in data preprocessing.
method Boosting algorithm to learn sufficient statistics of exponential families.
result The learned distribution maintains fairness guarantees while fitting the data better.
Study on missing data mechanisms and simple imputation methods in fairness of machine learning algorithms.
problem Impact of missing data mechanisms and simple imputation methods on fairness of machine learning algorithms.
method Three popular datasets for classification fairness were used. Missing values were generated using three missing data mechanisms. Various missing data handling techniques (listwise deletion, mean imputation, mode imputation, multiple imputation) were applied to the datasets. Fairness was assessed using classification algorithms (random forests).
result Missing data mechanism does not significantly impact fairness; listwise deletion gives highest fairness on average.
FIFA improves fairness in imbalanced datasets by encouraging both classification and fairness generalization.
problem Imbalanced datasets lead to poor fairness generalization in classifiers.
method FIFA: Imbalance-Fairness-Aware approach that encourages both classification and fairness generalization.
result FIFA improves fairness generalization on real-world datasets.
FVNNs use graph convolutions on fair covariance estimates to improve fairness in machine learning.
problem Data-driven methods can encode biases in sample covariance matrices, leading to unfair treatment of different subpopulations.
method FVNNs perform graph convolutions on fair covariance estimates and use a fairness regularizer in the loss function.
result FVNNs provide a flexible model that is intrinsically fairer than PCA approaches and can handle low sample regimes.
New framework improves fairness in small data settings.
problem Ensuring fairness in low-data environments.
method Combines posterior sampling exploration with fair classification.
result Framework maximizes accuracy while meeting fairness constraints.
FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data
problem Ensuring fairness in machine learning
method Bayesian Experimental Design
result Improved fairness-accuracy trade-offs
Fairness in machine learning increases privacy risks, especially for underrepresented groups.
problem Privacy risks in fair machine learning models, particularly for underrepresented groups.
method Membership inference attacks to measure information leakage and analyze fairness vs. privacy trade-offs.
result Achieving fairness in machine learning models increases privacy risks, especially for underrepresented groups.
FaiREE provides fair classification with guarantees for small datasets.
problem Fairness in classification often requires large sample sizes and distributional assumptions.
method FaiREE offers finite-sample and distribution-free fairness guarantees.
result FaiREE achieves optimal accuracy and satisfies various fairness notions.
Bayesian framework uses unlabeled data to improve fairness assessment.
problem Reliable fairness assessment with limited labeled data.
method Hierarchical latent variable model with Bayesian inference.
result Significant reduction in estimation error for fairness metrics.
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).
The paper proposes a method to identify fair features in ML data integration.
problem Ensuring fairness in machine learning data integration.
method Causal interventional fairness, conditional independence tests, group testing.
result The proposed algorithm identifies fair features without biasing the dataset.
New research shows fair data representations are impossible for different tasks.
problem Achieving fairness in machine learning models trained on various tasks.
method Analyzing the limits of fair data representations.
result No representation can guarantee fairness for different tasks trained on it.
Fair machine learning has become a significant research topic with broad societal impact. However, most fair learning methods require direct access to personal demographic data, which is increasingly restricted to use for protecting user privacy (e.g. by the EU General Data Protection Regulation). In this paper, we pro…
Proposes a method to enforce fairness in machine learning models without sensitive data.
problem Bias in machine learning models from historical data.
method Infers sensitive attributes from auxiliary features and integrates fairness constraints into model training.
result Mitigates bias while preserving predictive accuracy.
Proposes CRA framework for certifying fair predictive models.
problem Certifying fairness of predictive models trained on biased data.
method Formulates CRA for fairness queries, uses background knowledge and limited target population stats.
result Builds certifiably fair predictive models on target population.
Propensity score matching improves fairness in machine learning models.
problem Bias in training data affects fairness metrics in machine learning models.
method Propensity score matching to evaluate and mitigate bias in test data.
result FairMatch significantly reduces bias in test data without sacrificing predictive performance.
Fair active learning selects data points to balance model accuracy and fairness.
problem Ensuring fairness in machine learning models used in high-stakes applications.
method Designing algorithms for fair active learning that select data points to balance model accuracy and fairness, focusing on demographic parity.
result Demonstrated the effectiveness of the proposed fair active learning approach over benchmark datasets.
FR-Train improves fair and robust AI training by detecting and reducing poisoned data.
problem Training AI models that are fair and robust in the presence of data bias and poisoning.
method Mutual information-based adversarial training with an additional discriminator.
result FR-Train maintains fairness and accuracy even in the presence of poisoned data.
Recent work in fairness in machine learning has proposed adjusting for fairness by equalizing accuracy metrics across groups and has also studied how datasets affected by historical prejudices may lead to unfair decision policies. We connect these lines of work and study the residual unfairness that arises when a fairn…
Proposes a method to learn fair predictors for multiple subgroups with limited data.
problem Fairness and accuracy issues in learning from multiple subgroups with limited data.
method Formulates a bilevel objective to learn subgroup-specific predictors and a fair predictor that is close to all of them.
result The method effectively controls group sufficiency and generalization error, improving fairness and accuracy.
Paper studies fair classification of functional data.
problem Mitigating disparities in functional data classification.
method Unified framework for fairness-aware functional classification.
result Established theoretical guarantees on fairness and excess risk controls.
Optimal LDP mechanisms reduce data unfairness in classification.
problem Reducing data unfairness in classification models.
method Developed a closed-form optimal mechanism for binary attributes and a tractable framework for multi-valued attributes.
result Optimal LDP mechanisms improve fairness in classification while maintaining accuracy close to non-private models.
New approach for fair predictions under changing data distributions.
problem Fairness in classification algorithms under covariate shift.
method Proposes a robust predictor that satisfies fairness and maintains statistical properties of source data.
result Demonstrates improved fairness and target performance on benchmark tasks.
This work proposes a new pre-processing method for supervised learning to improve fairness without sacrificing utility.
problem Improving fairness in supervised learning without compromising model performance.
method Task-tailored pre-processing approach that balances fairness and utility.
result The proposed method preserves consistent trade-offs among multiple downstream models and improves fairness in computer vision tasks.
WassFFed addresses fairness in Federated Learning by ensuring consistency between local and global models.
problem Achieving fairness in Federated Learning where data is distributed among diverse user groups.
method WassFFed employs a Wasserstein barycenter calculation to aggregate local models' outputs, ensuring consistency and fairness.
result WassFFed outperforms existing approaches in balancing accuracy and fairness.