Fair HAC algorithms ensure clustering fairness across protected groups.
problem Ensuring clustering fairness in HAC algorithms when datasets contain biases.
method Proposes fair algorithms for HAC that enforce fairness constraints regardless of distance linkage criteria.
result Our fair HAC algorithms find fairer clusterings compared to vanilla HAC and other fair clustering approaches.
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.
Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairne…
FairLangProc simplifies fairness in NLP models for Python users.
problem Addressing bias in NLP models for decision-making contexts.
method Develops a Python package for implementing fairness metrics and algorithms.
result Promotes the use of bias mitigation techniques in NLP.
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.
New methods for fairness in regression using probabilistic classification.
problem Estimating fairness in continuous regression problems.
method Tractable approximations of fairness criteria using conditional probabilities from distinct classifiers.
result Model agnostic, tractable approximations of fairness criteria.
New fairness measures account for prediction uncertainties to detect bias.
problem Fairness of ML models is not well-defined and measures are limited.
method Introduce new fairness measures based on aleatoric and epistemic uncertainties.
result Uncertainty-based measures reveal bias not captured by existing measures.
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.
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.
A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, one common challenge of all causality-based fairness notions is identifiability, i.e., whether they can be uniquely measured from observationa…
Paper resolves apparent conflict between group and individual fairness.
problem Apparent conflict between group and individual fairness in machine learning.
method Theoretical discussions from fair machine learning, political, and legal philosophy.
result Individual and group fairness are not fundamentally in conflict.
Methodology explores fairness limits in decision tree classifiers.
problem Understanding statistical limits of bias mitigation in machine learning.
method Multi-objective framework optimizing accuracy and fairness.
result Decision tree models can be optimized for fairness with minimal accuracy loss.
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.
The paper tackles fairness in scoring functions for binary classification.
problem Fairness in scoring functions for binary classification tasks.
method Introduces ROC-based fairness constraints and learning algorithms.
result Generalization bounds and practical learning algorithms for fair scoring functions.
New method improves fairness of facial recognition systems.
problem Facial recognition systems exhibit bias across different demographic groups.
method Optimizes centroid-based scores to reduce bias in pre-trained models.
result Demonstrates significant improvement in fairness with minimal loss in accuracy.
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.
Study online learning with individual fairness without known similarity measure.
problem Online learning with individual fairness constraints without a known similarity measure.
method Reduction to standard online classification, leveraging auditor feedback.
result Achieves sub-linear regret and fairness violations with stochastic data.
Develops methods for fair classification under linear disparity constraints.
problem Disparate impacts of machine learning algorithms on protected groups.
method Bayes-optimal fair classification methods via pre-, in-, and post-processing.
result Explicit forms of Bayes-optimal fair classifiers under linear disparity measures.
The paper tackles fair classification with multiple sensitive features.
problem Existing fair classification methods often consider a single sensitive feature, but in practice, individuals are defined by multiple sensitive features.
method Characterizes Bayes-optimal fair classifiers for multiple sensitive features under various fairness measures, proposing in-processing and post-processing algorithms.
result Bayes-optimal fair classifiers for multiple sensitive features are instance-dependent thresholding rules that rely on a weighted sum of group membership probabilities.
Paper addresses fairness issues in error-prone outcomes.
problem Fairness in error-prone outcomes.
method Combining fair ML methods and measurement models.
result Using a latent variable model removes detected unfairness.
New causal approach resolves fairness and accuracy trade-offs.
problem Fairness and predictive performance are often at odds.
method Causal pre-processing methods to approximate the FiND world.
result Pre-processing resolves both fairness and accuracy trade-offs.
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.
In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different? Toxicity classifiers demonstrate a counterfactual fairness issue by predicting that "Some people are gay" is toxic while "…
Fairness measures fail in predictive settings that intentionally shift outcomes.
problem Fairness measures fail in performative prediction settings.
method Formalized concept shift and counterfactual outcomes.
result Predictors that are fair during training become unfair during deployment.
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.
Formulates LGFO to measure fair ML systems using legal signals.
problem Formally incompatible measures of unfairness in ML systems.
method Uses legal signals to measure social cost of unfairness.
result LGFO aligns with societal view of unfairness.
The paper proposes a happiness-based fairness framework using linear programming.
problem Fairness in decision-making outcomes.
method Linear programming to compute optimal, fair post-processing strategies.
result Unified and extended fairness definitions with practical strengths across diverse scenarios.
The paper explores the tradeoffs between fairness measures in machine learning.
problem The challenge of achieving all three fairness notions simultaneously in machine learning models.
method The approach uses partial information decomposition (PID) to analyze the relationships between fairness measures.
result Identifies the regions where fairness measures overlap and disagree, revealing potential tradeoffs.
New fairness criterion for risk-sensitive decisions in regulated industries.
problem Ensuring equitable outcomes in risk-sensitive decision-making.
method Marginal fairness for generalized distortion risk measures, two-step decision-making process.
result Ensures fairness in decision-making under risk measures, regardless of protected attributes.
Proposes measuring fairness through multiple stakeholder-curated stress tests.
problem Limited power of rigid fairness metrics and lack of stakeholder involvement in fairness discussions.
method Shift focus from fairness metrics to stress tests curated by stakeholders.
result Machine's performance under multiple stress tests reflects fairness.
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.
Proposes a new metric for financial risk based on volatility's local deviations.
problem Inefficiencies in classical risk metrics like volatility.
method Introduces pointwise regularity via the Hurst-Holder exponent.
result A more nuanced assessment of market inefficiencies and mechanisms for restoring equilibrium.
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.
This paper introduces efficient approximations for fairness criteria in regression models.
problem Measuring fairness in real-valued outcomes (regression settings) is computationally challenging.
method Fast approximations of mutual information for independence, separation, and sufficiency fairness criteria.
result The method achieves state-of-the-art accuracy/fairness tradeoffs in real-world datasets.
We map the recently proposed notions of algorithmic fairness to economic models of Equality of opportunity (EOP)---an extensively studied ideal of fairness in political philosophy. We formally show that through our conceptual mapping, many existing definition of algorithmic fairness, such as predictive value parity and…
One often finds in the literature connections between measures of fairness and measures of feature importance employed to interpret trained classifiers. However, there seems to be no study that compares fairness measures and feature importance measures. In this paper we propose ways to evaluate and compare such measure…
Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization. However, recent studies have noted the occurrence of bias (under- or over-representation of a certain gender or race) in such data summarization methods. In this paper we initiate a s…
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).
PAC-Bayesian framework for fairness in stochastic and deterministic classifiers.
problem Theoretical guarantees on fairness for balancing predictive risk and fairness constraints.
method PAC-Bayesian framework for both stochastic and deterministic classifiers, covering a broad class of fairness measures.
result Derives generalization bounds for fairness, demonstrating tightness with empirical evaluation.
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.
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.
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.
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.
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.
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…
Develops methods to measure and reduce fairness in datasets with limited protected attribute labels.
problem Measuring and reducing fairness in datasets with limited protected attribute labels.
method Proposes methods to estimate fairness metrics and train models to limit fairness violations using probabilistic protected attribute labels.
result Our methods provide tighter bounds on true disparity and effectively reduce fairness violations with lesser fairness-accuracy trade-offs.
Machine learning practitioners are often ambivalent about the ethical aspects of their products. We believe anything that gets us from that current state to one in which our systems are achieving some degree of fairness is an improvement that should be welcomed. This is true even when that progress does not get us 100%…
Study examines auditing fairness in evolving models, identifying strategic updates that preserve audit properties.
problem Auditing fairness in machine learning models that adapt to changing environments.
method Characterizes strategic updates that preserve audit properties, proposes a generic PAC auditing framework.
result Establishes distribution-free auditing bounds for statistical parity using the SP dimension.