Develops a fair relational model learning algorithm.
problem Fairness in machine learning models for relational data.
method Fair-A3SL, a fairness-aware structure learning algorithm for relational structures.
result Demonstrates effectiveness in learning fair, interpretable, and expressive structures.
New concept of within-group fairness improves AI fairness without sacrificing accuracy.
problem Fairness issues in AI models treating individuals in the same sensitive group unfairly.
method Introducing within-group fairness, proposing mathematical definitions, and developing learning algorithms.
result Improves within-group fairness without sacrificing accuracy and between-group fairness.
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…
Study develops a new method for creating fair models.
problem Ensuring equal outcomes for different protected groups.
method Introduces a new group-fair constraint based on transport maps.
result Develops a novel algorithm FTM for training group-fair models.
Algorithm ensures fair ranking by minority groups alongside majority groups.
problem Ensuring fair ranking of items from minority groups alongside majority groups.
method Optimal transport-based regularizer for individual fairness and efficient optimization algorithm.
result Certifiably individually fair LTR models are achieved.
How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most research on fairness focuses on improving fairness in a single model. Further, recent research on classification fairness has shown that combin…
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.
A new method SLIDE ensures fairness in AI models.
problem Ensuring fairness in AI models while maintaining computational feasibility.
method Proposes a new surrogate fairness constraint SLIDE.
result SLIDE ensures fairness in AI models asymptotically and converges fast.
Study examines fairness in machine learning for credit scoring.
problem Bias in machine learning models for credit scoring.
method Comprehensive experimental study of fairness-aware machine learning models.
result Fairness-aware models improve fairness while maintaining accuracy.
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.
Bayesian optimization framework for fair machine learning models.
problem Bias in machine learning models and lack of adaptability of fairness techniques.
method General constrained Bayesian optimization framework.
result BO can optimize ML models for fairness without model-specific constraints.
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.
Revises individual fairness by finding a fair metric for a model.
problem Difficulties in specifying a suitable fairness metric a priori.
method Introduces minimal metrics and applies randomized smoothing from adversarial robustness.
result Adapting minimal metrics to complex models yields interpretable fairness guarantees.
SenSeI ensures fair models by enforcing invariance on sensitive groups.
problem Ensuring fair machine learning models that respect sensitive groups.
method Designing a transport-based regularizer to enforce invariance on sensitive sets.
result Certifiably fair ML models trained using SenSeI achieve improved fairness metrics.
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.
We identify and optimize the fairness-accuracy tradeoff through TAF Curves and FAUC metrics.
problem Balancing fairness and accuracy in machine learning models for high-stakes decisions.
method Developed TAF Curves and FAUC metric to quantify the tradeoff, and introduced FairStacks framework to expand the Pareto frontier.
result FairStacks framework expands the empirical Pareto frontier and improves the FAUC for model ensembles.
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 a framework for partially fair machine learning models.
problem Achieving full fairness across all score ranges compromises predictive performance.
method Formulates model training as constrained optimization with difference-of-convex constraints, solvable by IDCA.
result Demonstrates high predictive performance while enforcing partial fairness in specific percentile intervals.
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.
Algorithm learns similarity metrics for individual fairness.
problem Difficulty in learning similarity metrics for individual fairness.
method Gradient descent and Bradley-Terry model for pairwise comparisons.
result Algorithm converges to ground truth metric for individual fairness.
Proposes FairRR to improve fairness in machine learning models through randomized response.
problem Achieving group fairness in machine learning models.
method Formulates group fairness as optimizing a design matrix in Randomized Response, proposing FairRR.
result Demonstrates FairRR yields excellent model utility and fairness.
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.
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.
FADE framework improves fairness and accuracy in ensemble learning.
problem Improving fairness in existing models without sacrificing accuracy.
method Flexible fair ensemble learning framework targeting multiple fairness criteria.
result Multiple unfairness measures can be minimized simultaneously with little impact on accuracy.
New approach to explain fairness in machine learning models.
problem Detect, understand, and mitigate unfairness in machine learning models.
method Shapley value paradigm and meta algorithm for training-time fairness interventions.
result Meta algorithm provides insight into accuracy-fairness trade-off.
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 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.
A new fairness metric for decision-making algorithms, conditioning on known fair variables.
problem Fairness issues in decision-making systems.
method Conditional fairness metric, Derivable Conditional Fairness Regularizer (DCFR), adversarial representation.
result Traditional fairness notations are special cases of the new conditional fairness notation.
Proposes causal modeling for intersectional fairness in rankings.
problem Fairness in rankings, especially intersectional fairness.
method Causal modeling approach for intersectional fairness, flexible ranking computation.
result Experimental evaluation shows the approach's effectiveness under different assumptions.
A novel multi-objective optimization framework improves insurance pricing fairness.
problem Exacerbated trade-offs between competing fairness criteria in insurance pricing using machine learning.
method Proposes a novel multi-objective optimization framework using NSGA-II to jointly optimize accuracy and fairness criteria.
result Consistently achieves a balanced compromise between accuracy and fairness, outperforming single-model approaches.
The paper introduces a method to achieve fairness in machine learning models using graph models.
problem Theoretical properties and intuition behind fairness in machine learning models are poorly understood.
method Sheaf Diffusion framework to model fairness in a bias-free space.
result The proposed method achieves fair solutions and handles different fairness metrics.
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.
Fair GLASSO estimates fair GGMs by balancing statistical dependencies across groups.
problem Fairness in graphical models with biased data.
method Regularized graphical lasso with bias metrics, proximal gradient algorithm.
result Preserves statistical accuracy while promoting fairness across groups.
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.
Two new methods assess feature importance for fairness in machine learning models.
problem Understanding how features influence fairness in machine learning models.
method Two model-agnostic approaches: permutation and occlusion.
result Simple, scalable, and interpretable methods to quantify feature importance for fairness.
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…
Ditto improves fairness and robustness in federated learning.
problem Fairness and robustness in statistically heterogeneous federated learning networks.
method Personalized federated learning framework (Ditto) with a scalable solver.
result Ditto achieves competitive performance and superior fairness and robustness compared to existing methods.
A new method learns fair classifiers without sacrificing accuracy.
problem Designing fair classifiers that do not discriminate based on sensitive attributes.
method A model-agnostic multi-objective architecture using a differentiable relaxation of fairness notions.
result Our method achieves lower loss of accuracy compared to current debiasing algorithms.
FAIR method uses adversarial training to learn fair instance weights.
problem Reduces bias in machine learning predictions through fair instance weighting.
method Adversarial training to learn instance weighting function ensuring fair predictions.
result Demonstrates better trade-off between accuracy and fairness compared to other models.
Examines fairness in ML for health, highlighting its importance and challenges.
problem Ensuring fairness in ML models for health to prevent health disparities.
method Reviews fairness notions in ML for health, including group, individual, and causal-based approaches.
result Discusses the importance and challenges of fairness in health-focused ML applications.
Study fairness in intervention to maximize outcomes.
problem Fairness in intervention on a given node.
method Counterfactual estimation with partial causal model knowledge.
result Theoretical guarantees on error probability and effectiveness of algorithm.
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 model for fair clustering ensures balanced representation of protected attributes.
problem Ensuring fair representation in clustering for protected attributes.
method Model-based formulation of fair clustering, balancing protected attributes across clusters.
result Demonstrates improved fairness in clustering through a new model.
BADR framework optimizes fairness metrics efficiently.
problem Fairness-inefficient models in machine learning.
method Bilevel Adaptive Rescalarisation procedure.
result BADR framework recovers optimal Pareto-efficient models.
New fairness approach removes direct effects of unprivileged groups through causal regularization.
problem Ensuring fairness in machine learning models for unprivileged groups.
method Proposes a new fairness definition based on causal effects and develops regularizations to remove the impact of unprivileged groups on model outcomes.
result Demonstrates effectiveness of the approach on various datasets, reducing unfairness with minimal performance loss.
FairTrade uses variational inference to create fair predictions in causal models.
problem Creating fair predictions in machine learning models with causal reasoning.
method FairTrade uses variational inference to account for unobserved confounders and integrates fairness constraints on causal paths.
result Demonstrates the effectiveness of FairTrade in creating fair predictions in both simulated and real-world datasets.
Paper tackles fairness in insurance machine learning models using active learning.
problem Reducing labeling effort and promoting fairness in insurance machine learning.
method Introduces a fair active learning method to sample informative and fair instances.
result Achieves a balance between model performance and fairness in insurance datasets.
This work addresses building fair and calibrated models.
problem Building models that are both fair and calibrated.
method Developed a new definition of fairness and showed that group-wise calibration results in fairness. Proposed post-processing techniques and modifications of calibration losses.
result Demonstrated that ensuring group-wise calibration results in a fair model under the new definition of fairness.