The paper studies fairness in multi-stage selection problems and introduces a method to compute fair selections.
problem Fairness in multi-stage selection problems with additional features at each stage.
method Introducing fairness notions, proposing a linear program for fair selections, and defining the price of local fairness.
result It is possible to have a selection that has a small price of local fairness and is close to locally fair.
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.
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.
Model shows partial compliance can lead to less fair outcomes than expected.
problem How partial compliance affects fairness in competitive markets.
method Simple model of employment market, simulation to explore effects.
result Partial compliance can lead to less fair outcomes than expected.
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.
This work improves fairness in federated learning by using zero-shot data augmentation.
problem Statistical heterogeneity leads to biased and less uniform accuracy across clients in federated learning.
method Proposes a federated learning system with zero-shot data augmentation to mitigate statistical heterogeneity and improve fairness.
result Empirical results show improved test accuracy and fairness across clients.
Improves global counterfactual explanations for model recourse.
problem Inability to provide explanations beyond local instances.
method Investigates and improves Actionable Recourse Summaries (AReS) for global counterfactual explanations.
result Develops more efficient and interactive explainability tools.
A new federated learning framework ensures fairness and robustness.
problem Collaborative fairness and adversarial robustness in federated learning.
method RFFL framework with a reputation mechanism to identify and remove non-contributing or malicious participants.
result RFFL achieves high fairness and robustness to different types of adversaries.
Gradient boosting method enforced with individual fairness.
problem Enforcing fairness in machine learning models.
method Functional gradient descent on robust loss function.
result Algorithm converges globally and generalizes.
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.
Paper refutes EM convergence theory and introduces a new EM algorithm.
problem The convergence theory of the EM algorithm is incorrect and affects its performance.
method Proposes a new EM algorithm called the Channel Matching (CM) EM algorithm and provides an initialization map.
result The locally maximal Q can affect the convergent speed but not the global convergence.
In this paper we introduce the notion of cofrontal mappings, as the dual objects to frontal mappings, and study their basic local and global properties. Cofrontals are very special mappings and far from generic nor stable except for the case of submersions. It is observed that any smooth mapping can be C0-approximat…
Paper tackles fairness in CCA by minimizing correlation disparity error.
problem Fairness issues in CCA.
method Framework to minimize correlation disparity error in CCA.
result Reduces correlation disparity error without sacrificing CCA accuracy.
FairGP uses graph partitioning to make Graph Transformers fair and scalable.
problem Fairness issues in Graph Transformers, especially against sensitive features.
method Graph partitioning to minimize the influence of higher-order nodes and optimize attention mechanisms.
result FairGP improves fairness in Graph Transformers while reducing computational complexity.
Proposes adversarial learning for counterfactual fairness in machine learning.
problem Ensuring fairness at the individual level by simulating counterfactual samples.
method Adversarial neural learning approach to infer counterfactual samples.
result Significant improvements in counterfactual fairness for both discrete and continuous settings.
AAggFF improves federated learning fairness through sequential decision making.
problem Achieving client-level fairness in federated learning systems.
method Unified online convex optimization framework for adaptive aggregation strategies.
result AAggFF achieves better client-level fairness in federated learning.
Personalized federated learning improves model accuracy and fairness by leveraging shared representations and local memorization.
problem Sub-optimal performance of federated learning when client data distributions are heterogeneous.
method Proposes a personalization mechanism based on local memorization of shared representations from a collectively trained global model.
result Significantly higher accuracy and fairness compared to state-of-the-art methods.
FPCA optimizes fairness in target vectors' span.
problem Fairness in principal component analysis for multiple target vectors.
method Non-concave maximization of worst projected target norm using sub-gradient descent.
result Optimization landscape is benign with globally optimal local minima.
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.
FairPOT balances fairness and AUC performance by selectively transforming risk scores.
problem Balancing fairness and AUC performance in high-stakes domains.
method FairPOT uses proportional optimal transport to selectively transform risk scores.
result FairPOT consistently improves fairness with minimal AUC degradation or even positive gains.
A new method uses LLMs to discover causal pathways that affect fairness in machine learning.
problem Discovering fairness-relevant causal pathways in the presence of noise and confounding.
method Hybrid LLM-guided causal discovery framework combining active learning and dynamic scoring.
result LLM-guided methods, including the proposed active, dynamically scored variant, outperform baselines in recovering fairness-relevant structure under noisy conditions.
Paper revisits HVA to address model risk in banking.
problem Model risk and dynamic hedging frictions in banking.
method Reconciles global fair valuation with local bank models.
result Local models should be excluded rather than managed via reserves.
The paper explores various forms of calibration scores and their implications for fairness.
problem The evaluation of probabilistic predictions through calibration.
method The authors organize three grouping choices and one agglomeration of group errors, providing a framework for comparing and creating new calibration scores.
result The study demonstrates that appropriate choices of grouping can provide notions of (sub-)group or individual fairness.
FPPDL framework ensures fairness and privacy in federated deep learning.
problem Overfitting, low utility, single-point-of-failure, lack of fairness in federated learning.
method Local credibility mutual evaluation mechanism and three-layer onion-style encryption scheme.
result FPPDL balances fairness, privacy, and accuracy in federated deep learning.
A new framework promotes trustworthy user-generated datasets by ensuring no user benefits from misreporting.
problem Incentivizing data misreporting in user-generated datasets.
method Proposes Licchavi, a global and personalized learning framework with provable strategyproofness guarantees.
result Proves that no user can gain much by replying to Licchavi's queries with deviated answers.
The paper tackles fair correlation clustering with fairness constraints.
problem Minimizing disagreements while adhering to fairness constraints for clustering.
method Two variants of fairness constraints are considered: equal distribution and relative bounds. Approximation algorithms are developed for these constraints.
result Approximation algorithms for fair correlation clustering with theoretical guarantees and empirical validation.
DP-NCB algorithm ensures privacy and fairness in bandit decisions.
problem Achieving both privacy and fairness in bandit algorithms.
method Differentially Private Nash Confidence Bound (DP-NCB) framework.
result DP-NCB achieves optimal Nash regret while maintaining privacy.
Paper proposes OPF policy for fair resource allocation with sublinear regret.
problem Fair resource allocation in an online setting against an unrestricted adversary.
method Online Proportional Fair (OPF) policy achieving approximate sublinear regret.
result OPF policy achieves cα-approximate sublinear regret with cα≤1.445. The paper defines fair profit sharing ratios in Islamic PL contracts.
problem Determining fair profit sharing ratios in Islamic PL contracts.
method Introduces c-fair profit sharing ratios and uses econometrics models to compute or approximate them. result Elucidates the relation between profit sharing ratios and economic factors.
Study compares different scoring rules for machine-learned weather forecasts, finding scale-awareness improves forecast realism.
problem Improving the accuracy of machine-learned probabilistic weather forecasts.
method Comparison of scoring rules (CRPS, fair global energy score, graph energy score) and analysis of their impact on forecast field spectra.
result Scale-awareness improves forecast realism, particularly in the tropics.
We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment. Rather than present a black box model and explain it afterwards, we provide a globally interpretable model that is as accurate as other neural networks. O…
This paper improves privacy and fairness in federated learning by protecting sensitive data and ensuring group fairness.
problem Privacy and fairness issues in federated learning.
method Introduces group privacy through d-privacy, a localized form of differential privacy. result The method provides better group fairness than a global model in federated learning.
The paper analyzes the current state of the world economy and offers a short-term forecast of its development. Our analysis of log-periodic oscillations in the DJIA dynamics suggests that in the second half of 2017 the United States and other more developed countries could experience a new recession, due to the third p…
Federated learning can propagate bias from a few parties to all participants.
problem Bias from a few parties in federated learning can spread to all participants.
method Analysis of naturally partitioned real-world datasets.
result Bias in federated learning is higher than in centralized training.
Federated learning algorithm reduces global model size by combining local and global representations.
problem Scalability issues in training large models on private data distributed over multiple devices.
method Proposes a federated learning algorithm that jointly learns compact local representations and a global model.
result The global model can be smaller since it only operates on local representations, reducing the number of communicated parameters.
Introduces privilege scores to measure and interpret protected attribute-related privilege in machine learning models.
problem Lack of explicit formulation of non-neutrality in fairness-aware machine learning methods.
method Privilege scores (PS) and privilege score contributions (PSCs) to measure and interpret protected attribute-related privilege.
result Demonstrates the broad applicability of PS and PSCs in gender and racial privilege in mortgage and college admissions applications.
WAFFLe anonymizes federated learning weights to protect data privacy and fairness.
problem Federated learning exposes local models to attacks and underfits heterogeneous clients.
method Combines Indian Buffet Process with shared weight factors.
result Significant improvement in local test performance and fairness.
Local discovery method uncovers direct unfairness in complex systems.
problem Identifying causal pathways of unfairness in complex domains.
method Local discovery for direct discrimination (LD3) method.
result LD3 returns a valid adjustment set (VAS) for assessing unfairness.
GWHD dataset offers 4,700 high-res images of wheat heads.
problem Challenges in wheat head detection from high-resolution imagery.
method Large, diverse dataset with detailed metadata.
result Benchmark for wheat head detection methods.
This paper proposes a new framework for financial risk that considers predictability rather than volatility.
problem Volatility's limitations as a risk measure, especially in complex strategies and non-stationary markets.
method Developed a new paradigm based on stochastic processes and the Multifractional Process with Random Exponent (MPRE) framework.
result A formal definition of 'fair volatility' that aligns with market efficiency and provides a measure of market inefficiency.
A popular methodology for building binary decision-making classifiers in the presence of imperfect information is to first construct a non-binary "scoring" classifier that is calibrated over all protected groups, and then to post-process this score to obtain a binary decision. We study the feasibility of achieving vari…
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.
GLOBE-CE offers efficient global counterfactual explanations.
problem Lack of reliable and scalable global counterfactual explanations.
method Translation-based approach for global counterfactual explanations.
result GLOBE-CE performs significantly better than current methods across multiple metrics.
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.
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.
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.
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.
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.