The paper tackles the trade-off between fairness and accuracy in machine learning models.
problem Ensuring fairness in machine learning often reduces model accuracy.
method The paper introduces formal tools for reconciling the fairness-accuracy tension using Pareto optimality from multi-objective optimization.
result The Chebyshev scalarization scheme is superior for finding Pareto optimal solutions compared to the linear scalarization scheme.
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.
Proposes Pareto efficient fairness for supervised learning models.
problem Ensuring fairness in machine learning models without sacrificing accuracy.
method Formulates a bilevel optimization problem to find Pareto efficient classifiers.
result Guaranteed solution on Pareto frontier for convex and non-convex objectives.
The potential for learned models to amplify existing societal biases has been broadly recognized. Fairness-aware classifier constraints, which apply equality metrics of performance across subgroups defined on sensitive attributes such as race and gender, seek to rectify inequity but can yield non-uniform degradation in…
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.
Paper finds a method to compute fair risk-sharing rules.
problem Finding a fair and understandable risk-sharing rule.
method Established a one-to-one correspondence with a fixed point approach.
result Fast numerical method for computing AFPO risk-sharing rules.
Pareto optimal centralized risk sharing with multiple agents
problem Centralized risk sharing with endogenous prices
method Inclusive and fair Pareto optimality
result Equivalence between inclusive and fair Pareto optimality and balanced sequential optimization
The study analyzes the conflict between group fairness and individual fairness in machine learning.
problem The conflict between group fairness (optimal statistical parity) and individual fairness in machine learning.
method Established sufficient conditions for the compatibility between optimal statistical parity and individual fairness requirements.
result Identified regions along the Pareto frontier that satisfy individual fairness requirements.
New method computes optimal fairness-performance trade-off without complex models.
problem Intrinsic trade-off between fairness and classifier performance.
method Computes optimal Pareto front without training complex models.
result Optimal fair representations have useful structural properties enabling efficient computation.
The paper tackles fair policy targeting by optimizing allocation rules to minimize unfairness.
problem Discrimination in individualized treatments of social welfare programs.
method Formulated as a mixed-integer linear program, solved using off-the-shelf algorithms, derived regret bounds and small sample guarantees.
result Designs fair and efficient treatment allocation rules within the Pareto frontier.
The paper proposes a method to ensure fairness in machine learning models.
problem Ensuring fairness in machine learning models powered by supervised learning.
method Optimal affine transport and Wasserstein-2 barycenter to characterize the Pareto frontier between prediction error and statistical disparity.
result The proposed method effectively balances prediction accuracy and fairness, as demonstrated by numerical simulations.
Proposes a fairness criterion for multi-objective optimization in classification.
problem Ensuring fairness in classification models across different groups.
method Formulates a minimax Pareto fairness criterion and provides an optimization algorithm.
result Demonstrates improved fairness compared to existing methods on various real-world datasets.
Common fairness definitions in machine learning focus on balancing notions of disparity and utility. In this work, we study fairness in the context of risk disparity among sub-populations. We are interested in learning models that minimize performance discrepancies across sensitive groups without causing unnecessary ha…
New approach to fairness in machine learning through stochastic optimization.
problem Fairness issues in machine learning predictions.
method Stochastic multi-objective optimization for accuracy-fairness trade-offs.
result Well-spread and accurate Pareto fronts for handling streaming data.
It has been shown that dimension reduction methods such as PCA may be inherently prone to unfairness and treat data from different sensitive groups such as race, color, sex, etc., unfairly. In pursuit of fairness-enhancing dimensionality reduction, using the notion of Pareto optimality, we propose an adaptive first-ord…
Study optimizes fairness in predictive models by balancing utility and separation.
problem Balancing fairness and utility in predictive models.
method Information-theoretic approach using conditional mutual information (CMI).
result Reduces separation violations while maintaining or improving utility.
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.
FanG-HPO optimizes machine learning models for fairness and low energy consumption.
problem Bias in machine learning models and high energy consumption in hyperparameter optimization.
method Combines multi-objective and multiple information source Bayesian optimization.
result FanG-HPO identifies fair and energy-efficient machine learning models.
New algorithm improves fairness and robustness in federated learning.
problem Ensuring fairness and robustness in federated learning.
method Formulated federated learning as multi-objective optimization and proposed FedMGDA+.
result FedMGDA+ converges to Pareto stationary solutions, improving performance.
The paper explores fair machine learning policies for balancing competing objectives in noisy data.
problem Balancing competing objectives in noisy data.
method Analyzes a class of policies that trace an empirical Pareto frontier based on learned scores.
result Characterizes optimal strategies and bounds Pareto errors due to score inaccuracies.
Simple greedy algorithms can excel in multi-objective bandits with multiple good arms.
problem Optimizing multiple objectives in bandits is traditionally harder.
method Introduced greedy algorithms that exploit multiple good arms for multiple objectives.
result Simple greedy algorithms achieve strong performance in multi-objective bandits.
Develops a fair classifier for deep learning models.
problem Ensuring fairness in classification models across different sub-populations.
method Applies Rawlsian principles to minimize error rate on the worst-off sub-population.
result Introduces a practical method to adapt any black-box deep learning model to be fair.
A new algorithm balances fairness in clustering to avoid discrimination.
problem Clustering data can unfairly discriminate against different demographic groups.
method Designing a stochastic alternating balance fair k-means algorithm (SAfairKM) that alternates between k-means updates and group swap updates.
result The algorithm efficiently constructs well-spread and high-quality Pareto fronts on synthetic and real datasets.
Automates fairness and accuracy optimization in deep learning models for tabular data.
problem Improving fairness and accuracy in neural models for tabular data.
method Employed multi-objective Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO) to find new models.
result Jointly optimized architectures that consistently outperform single-objective fairness mitigation methods.
New algorithm mitigates bias in subset selection with noisy protected attributes.
problem Mitigating bias in subset selection when protected attributes are noisy.
method Formulated a denoised selection problem and developed a linear-programming based approximation algorithm.
result The approach can produce fairer subsets despite noisy protected attributes.
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.
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.
This work fills the gap in understanding multi-objective learning generalization.
problem Lack of statistical learning theory insights into multi-objective learning generalization.
method Established generalization bounds and excess bounds for multi-objective learning.
result Showed that all Pareto-optimal solutions can be approximated by empirically Pareto-optimal ones, but not vice versa.
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.
This work optimizes model performance while ensuring fairness through AUC constraints.
problem Ensuring fairness in machine learning models, especially for protected populations.
method Formulates fairness-aware machine learning model training as AUC optimization subject to fairness constraints, solves using stochastic first-order methods.
result Demonstrates effectiveness of the approach on real-world data under different fairness metrics.
As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences. How should we address this problem in a real-world system? How should we balance core performance and fairness me…
Researchers study fairness-accuracy tradeoffs in predictive models for multiple groups.
problem Understanding the tradeoff between fairness and accuracy in models serving multiple demographic groups.
method Characterizing the fairness-accuracy (FA) Pareto frontier, approximating it from limited data, and bounding the worst-case gap.
result Derivation of worst-case-optimal estimators and uniform finite-sample bounds for the entire FA frontier.
This work addresses local fairness in machine learning models.
problem Ensuring fairness within subregions of feature space, not just global averages.
method Introduces ROAD, a Distributionally Robust Optimization (DRO) approach with adversarial learning.
result Achieves Pareto dominance in local fairness and accuracy across datasets.
New method shows multi-objective bandits are not harder than single-objective ones.
problem Comparing multi-objective bandits to single-objective ones.
method Upper and lower confidence-bound estimators for every arm-objective pair, using top-two races and uncertainty-greedy rule.
result Achieves Pareto regret of \(O(
icefrac{\log T}{g^\dagger})\), matching lower bound of \(Ω(
icefrac{\log T}{g^\dagger})\).
The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisio…
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.
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.
New method for private learning with fairness constraints.
problem Rate-constrained optimization under differential privacy.
method RaCO-DP, a DP variant of SGDA solving Lagrangian formulation.
result Empirical results show RaCO-DP outperforms existing methods.
Paper uses conformal prediction sets to make criminal justice risk assessments fairer.
problem Fairness issues in criminal justice risk assessment algorithms.
method Adopting conformal prediction sets to remove unfairness from algorithms and covariates.
result Constructs confusion tables and measures fairness effectively free of racial differences.
This paper surveys gradient-based multi-objective deep learning methods.
problem Balancing multiple conflicting objectives in deep learning models.
method Gradient-based techniques adapted from Multi-Objective Optimization.
result Comprehensive survey of gradient-based multi-objective deep learning algorithms.
Generative model improved using Liouville PDE-based sliced-Wasserstein flow.
problem Improving generative models for fair regression.
method Transformed sliced-Wasserstein flow into Liouville PDE-based formalism, handling density estimation with normalizing flows of neural ODE.
result Outperforms in convergence and fairness with reduced variance.
Now that machine learning algorithms lie at the center of many resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. What is the relationship between fairness as defined by computer scientists and notions of soc…
Unified framework connects credit risk metrics with information theory.
problem Disconnection between industry-standard metrics and statistical theory.
method Unified information-theoretic framework, proving IV equals PSI, deriving standard errors, formalizing trade-off, automated binning with XGBoost.
result Unified framework connects IV and PSI, providing statistical foundation for metrics.
The paper explores how regularization can improve multi-objective learning with high-dimensional data.
problem Improving multi-objective learning with high-dimensional and costly data.
method A two-stage MOL framework that leverages low-dimensional structure.
result Vanilla regularization approaches often fail in multi-objective learning, and a two-stage framework can successfully exploit low-dimensional structure.
Optimizes machine learning models while controlling risks.
problem Finding a model configuration that balances multiple conflicting metrics.
method Combines Bayesian Optimization with rigorous risk-controlling procedures.
result Identifies and selects Pareto optimal configurations with guaranteed risk levels.
The widening inequality in income distribution in recent years, and the associated excessive pay packages of CEOs in the U.S. and elsewhere, is of growing concern among policy makers as well as the common person. However, there seems to be no satisfactory answer, in conventional economic theories and models, to the fun…
New algorithms optimize multiple machine learning metrics in real-world tasks.
problem Optimizing multiple conflicting performance criteria in real-world applications.
method Extends ASHA to multi-objective hyperparameter optimization.
result MO ASHA enables scalable multi-objective hyperparameter optimization.
Proposes a game-theoretic framework for ML trust regulation.
problem Lack of coordination between ML model builders and regulators.
method Formulates trustworthy ML as a multi-objective multi-agent optimization problem and introduces regulation games and ParetoPlay.
result Enables efficient enforcement of ML model specifications without discouraging participation.