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.
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 fairness notion helps identify fair auditors for evaluating decision-support systems.
problem Identifying fair auditors to evaluate decision-support systems for bias.
method Introducing a non-comparative fairness notion based on desired system properties.
result The proposed fairness notion provides guarantees in terms of comparative fairness.
DFL framework improves action and outcome fairness in policy learning.
problem Fairness in policy learning, especially action and outcome fairness.
method Integrates action and outcome fairness into a multi-objective optimization problem using a lexicographic weighted Tchebyshev method.
result DFL framework improves both action and outcome fairness with minimal value reduction.
Introduces principal fairness for fair decision-making.
problem Discrimination among similarly affected individuals.
method Uses principal stratification from causal inference.
result Explicitly accounts for decision impacts, not just protected attributes.
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 explores fairness in credit scoring using machine learning.
problem The lack of research on fair machine learning in credit scoring.
method Revisits statistical fairness criteria, catalogs algorithmic options, and empirically compares fairness processors.
result Multiple fairness criteria can be approximately satisfied at once, and fair processors deliver a good balance between profit and fairness.
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.
Algorithm identifies intended fairness constraints from expert demonstrations for fair clustering.
problem Fair clustering challenges due to incomplete fairness constraints.
method Algorithm identifies fairness metric from expert demonstrations and generates clusters.
result Algorithm identifies and generates fair clusters from limited expert demonstrations.
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.
Unified approach for fair classification with overlapping groups.
problem Ensuring fairness across multiple overlapping groups in prediction problems.
method Probabilistic population analysis leading to Bayes-optimal classifier, unifying existing methods.
result Outperforms baselines in fairness-performance tradeoff on real datasets.
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.
Proposes FACT, a diagnostic for understanding group fairness trade-offs.
problem Group fairness notions often conflict with each other, requiring a cost in model performance.
method Characterizes trade-offs via the fairness-confusion tensor and optimizes accuracy and fairness objectives.
result Demonstrates the use of FACT on synthetic and real datasets to understand accuracy-fairness trade-offs.
Paper proposes a modified fairness constraint to address shortcomings of counterfactual fairness.
problem Counterfactual fairness is not a necessary condition for algorithmic fairness.
method Analyzed hypothetical scenario and explicated discrimination to develop causal relevance fairness.
result Causal relevance fairness is a modified constraint that circumvents shortcomings of counterfactual 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…
Algorithmic fairness, and in particular the fairness of scoring and classification algorithms, has become a topic of increasing social concern and has recently witnessed an explosion of research in theoretical computer science, machine learning, statistics, the social sciences, and law. Much of the literature considers…
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.
This paper improves fairness in recommendation systems by learning individual preferences across multiple dimensions.
problem Fairness in recommender systems, especially in areas with social impact.
method Opportunistic multi-aspect re-ranking approach that learns individual preferences and enhances provider fairness.
result Achieves a better trade-off between accuracy and fairness across multiple fairness dimensions.
This paper introduces individual fairness in clustering using f-divergence.
problem Ensuring fair clustering by treating similar individuals similarly.
method Uses f-divergence to measure statistical similarity and assigns individuals to probability distributions over cluster centers. result Provides an algorithm with provable approximation guarantee for clustering with individual fairness constraints.
The paper explores fair clustering, a niche area in machine learning.
problem Fairness in clustering remains underexplored despite its importance.
method Assesses existing work and proposes new directions for fair clustering.
result Widening normative principles and knowledge of downstream processes can enhance fair clustering research.
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. New fair PCA method using streaming algorithms with statistical guarantees.
problem Perform PCA while ensuring fair projected distributions.
method Formulated new notion PAFO learnability, proposed fair noisy power method (FNPM) for memory efficiency.
result First statistical guarantee for fair PCA in streaming setting.
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 clustering method considers causal fairness to avoid bias.
problem Clustering algorithms can unintentionally propagate unfair disparities.
method Integrates causal fairness metrics into clustering algorithms.
result Demonstrates efficacy on datasets with known unfair biases.
FCA improves fair clustering by optimizing utility and fairness.
problem Balancing fairness and utility in clustering.
method FCA alternates between aligning data and optimizing cluster centers in an aligned space.
result FCA achieves a superior trade-off between fairness and utility.
Fair MP-Boost improves fairness and interpretability in boosting methods.
problem Improving fairness and interpretability in boosting methods.
method Fair MP-Boost uses adaptive sampling of minipatches to balance accuracy and fairness.
result Fair MP-Boost enhances fairness and accuracy while providing interpretable feature importance.
We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of {\em Bayesian fairness} as a su…
New framework for fair ranking with noisy protected attributes.
problem Errors in socially-salient attributes undermine fairness guarantees.
method Modeling perturbations in protected attributes and incorporating probabilistic information.
result Framework provides provable guarantees on fairness and utility.
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.
The development of fair machine learning models that effectively avert bias and discrimination is an important problem that has garnered attention in recent years. The necessity of encoding complex relational dependencies among the features and variables for competent predictions require the development of fair, yet ex…
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…
Algorithm samples fair rankings to ensure individual fairness while maintaining group fairness.
problem Fair ranking tasks with group fairness constraints and uncertainty in item utilities.
method Efficient algorithm that samples rankings from an individually-fair distribution ensuring group fairness.
result Expected utility of output ranking is at least α times optimal fair solution, where α depends on utilities and constraints.
A new method for fair representation learning using PLS.
problem Fairness in representation learning for data reduction.
method Proposes Fair Partial Least Squares (PLS) components with fairness constraints.
result The new method outperforms standard fair PCA methods on various datasets.
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.
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.
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.
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.
Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness. Our novel framework includes an individual bias detector used to prioritize data samples in a bias mitigation a…
This work transfers fairness notions from binary classification to learning to rank.
problem Fairness concerns in automated ranking systems.
method Formalism to incorporate fairness objectives in learning to rank with provable guarantees.
result Improves ranking fairness substantially with minimal loss in model quality.
New approach to fairness in machine learning models using conformal prediction.
problem Fairness in machine learning models' downstream decision-making.
method Theoretical derivation and empirical evaluation of label-clustered conformal prediction.
result Label-clustered conformal prediction often provides a favorable balance between utility and substantive fairness.
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.
Standardized fairness measures for continuous risk scores using Wasserstein distance.
problem Quantifying and interpreting group disparities in continuous risk scores.
method Proposes standardized fairness measures based on Wasserstein distance for continuous scores.
result Proposed measures outperform ROC-based fairness measures by being more explicit and quantifying significant biases.
A new algorithm COVA-FC improves subgroup-fair clustering efficiency.
problem Challenges in making cluster assignments independent of sensitive attributes in subgroups.
method Defining a subgroup-fairness gap, deriving a covariance-based surrogate, and introducing a continuous relaxation for efficient optimization.
result COVA-FC achieves competitive cost-fairness trade-offs and improves computational efficiency.
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.
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.
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.
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.
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.