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.
Paper proposes GEG to enhance fairness in binary and multi-class classification.
problem Fairness in multi-class classification tasks is under-explored.
method Formulates multi-objective problem between effectiveness and fairness constraints, proposes GEG algorithm.
result GEG improves fairness up to 92% and decreases accuracy up to 14%.
CrossWalk enhances fairness in graph algorithms by biasing random walks.
problem Fairness in machine learning systems applied to graphs.
method Bias random walks to cross group boundaries by upweighting edges.
result Enhances fairness in various graph algorithms with minimal performance loss.
Computers are increasingly used to make decisions that have significant impact in people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers and predictors have…
FairPrep aims to improve fairness in machine learning by providing best practices.
problem Lack of best practices in fairness-enhancing interventions.
method Developer-centered design and evaluation framework for fairness-enhancing interventions.
result Hyperparameter tuning and data cleaning methods impact fairness outcomes.
Paper proposes Q-learning for efficient aerial BS placement to improve fairness in mobile networks.
problem Optimal placement of aerial base stations to enhance fairness in a dynamic user mobility environment.
method Reinforcement learning approach to solve the NP-hard problem of 3D placement.
result Simulation results show increased fairness among users with a reasonable computing time and solution close to optimal.
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…
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.
Enhances fairness in multi-output models using optimal transport.
problem Improving fairness in multi-output models like multi-task/multi-class classification and representation learning.
method Post-processing method using optimal transport mappings to move model outputs towards empirical Wasserstein barycenter.
result Demonstrates effectiveness of the proposed approach on multi-task/multi-class classification and representation learning tasks.
The paper explores fairness in AI algorithms across various sectors.
problem Algorithmic fairness in AI decision-making is crucial but often overlooked.
method Overview of fairness concepts, mechanisms, and datasets.
result Comprehensive comparison of fairness-enhancing mechanisms.
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.
FairDTD improves fairness in GNNs by distilling dual teacher knowledge, balancing utility and bias.
problem Bias in GNN predictions due to sensitive attributes.
method Dual-Teacher Distillation with a causal graph model, feature and structure teachers, and graph-level distillation.
result Achieves optimal fairness while preserving high model utility.
FairVIC improves fairness in neural networks without sacrificing accuracy.
problem Mitigating bias in automated decision-making systems, particularly in deep learning models.
method Integrates variance, invariance, and covariance terms into the loss function during training to abstract fairness concepts.
result Significant improvements in fairness across all tested metrics without compromising accuracy.
DECAF-GAD improves fairness in autoencoder-based GAD models without sacrificing performance.
problem Fairness in autoencoder-based GAD models for node-level anomaly detection.
method DECAF-GAD uses a structural causal model to disentangle sensitive attributes from learned representations, along with a fairness-guided loss function.
result DECAF-GAD significantly enhances fairness metrics while maintaining anomaly detection performance.
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.
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.
The paper uses distance covariance to improve fairness in machine learning models.
problem Improving fairness in machine learning models.
method Using conditional and distance covariance statistics to assess independence and add a penalty for fairness.
result The method effectively reduces the fairness gap in machine learning models.
Proposes a fair PCA algorithm that balances reconstruction loss and fairness.
problem PCA can be unfair to different groups.
method Adaptive first-order algorithm for Pareto optimality.
result The algorithm finds a fair subspace that minimizes reconstruction loss.
The paper critiques ε-fairness, showing it can lead to unfair outcomes and proposes a utility-based approach.
problem The limitations of probabilistic fairness metrics in real-world contexts.
method Utility-based approach to measure fairness, addressing the issue of unavailable data on false negatives.
result A utility-based approach uncovers necessary actions to achieve true fairness, contrasting with traditional probability-based evaluations.
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.
Integrates differential privacy and demographic parity in multi-class classification.
problem Ensuring fairness and privacy in sensitive applications.
method Designs DP2DP algorithm that enforces both demographic parity and differential privacy.
result DP2DP converges towards demographic parity at nearly the same rate as non-private methods, achieving state-of-the-art trade-offs.
The study redefines algorithmic fairness as a sociotechnical concept.
problem Systemic discrimination in automated decision-making.
method Literature review and sociotechnical analysis.
result Algorithmic fairness should be viewed through a sociotechnical lens.
Peer-induced fairness framework audits algorithmic fairness in AI applications.
problem Current auditing methods lack robustness and fail to distinguish between algorithmic discrimination and subject limitations.
method Combines counterfactual fairness and peer comparison strategy for a reliable auditing tool.
result Demonstrates significant unfairness in micro-firms compared to non-micro firms, highlighting the framework's potential.
Meta clustering categorizes learners for collaborative learning.
problem Filtering out unqualified collaborators in collaborative learning.
method Select-Exchange-Cluster (SEC) method to classify learners by their supervised functions.
result SEC can cluster learners into accurate collaboration sets and enhance single-learner performance.
CAT framework improves AI medical screening fairness and reliability.
problem Imbalanced data, varying performance across cohorts, and patient-level inconsistencies in traditional metrics.
method CAT framework introduces patient-level assessment, entropy-based distribution weighting, and cohort-weighted sensitivity and specificity.
result Enhanced predictive reliability, fairness, and interpretability of AI-driven medical screening models.
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.
The study maps ML quality dimensions to fairness, enhancing the QF4SA framework.
problem Ensuring fairness in ML applications at NSOs to avoid social impacts.
method Employing the QF4SA framework, the study maps quality dimensions to fairness and investigates their interactions.
result Fairness is identified as a new quality dimension in the QF4SA framework.
New framework improves fairness in machine learning models.
problem Eliminating discrimination in machine learning models for sensitive fields.
method Reformulate data input by removing sensitive features and design adversarial network to minimize dependence.
result Our model achieves better fairness metrics and prediction performance than state-of-the-art methods.
The paper proposes a fair reinforcement learning framework to prevent healthcare disparities.
problem Unfair reinforcement learning policies in healthcare can lead to socioeconomically-disadvantaged subgroups being underprivileged.
method The paper introduces a counterfactual fairness framework and a sequential data preprocessing algorithm to achieve fair sequential decision making.
result The proposed approach greatly enhances fair access to counseling in a digital health dataset designed to reduce opioid misuse.
A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information (e.g., gender or ethnic group) as part of a statistical model, and any commitment to protect these characteristics. Often, due to biases pre…
MAPPING debiases GNNs for fair node classification with limited leakage.
problem Graph Neural Networks inherit and exacerbate historical discrimination in high-stake domains.
method MAPPING uses distance covariance-based fairness constraints and adversarial debiasing.
result MAPPING achieves better trade-offs between fairness and utility, mitigating privacy risks.
Proposes a new method for fairness in machine learning with multiple protected attributes.
problem Ensuring fairness in machine learning models with continuous and multiple protected attributes.
method Distance covariance regularisation framework to mitigate association between model predictions and protected attributes.
result Demonstrates effectiveness in mitigating fairness gerrymandering in regression tasks.
FairDrop improves fairness in graph representation learning by counteracting homophily.
problem Ensuring fairness in graph representation learning, especially in scenarios with protected attributes.
method Proposes a biased edge dropout algorithm (FairDrop) to counteract homophily and improve fairness.
result Successfully improves fairness in all models up to a small or negligible drop in accuracy.
The paper examines how slightly biasing towards under-represented groups in sequential selection processes can lead to long-term fairness.
problem Designing fair sequential decision-making processes for long-term social fairness.
method Proposes Multi-agent Fair-Greedy policy to balance score maximization and fairness.
result Proves convergence to long-term fairness target set by agents when score distributions are identical.
The paper addresses fairness in dynamic pricing for strategic buyers.
problem Price disparities among specific groups can lead to unfair perceptions and legal violations.
method Proposes a dynamic pricing policy that achieves fairness and discourages strategic behavior.
result Achieves an upper bound of O ( T + H ( T ) ) O(\sqrt{T}+H(T)) O ( T + H ( T )) regret over T T T time horizons, reducing regret by 35.06% compared to a benchmark policy. Unified framework improves fair classification by selecting representative data points.
problem Improving fair classification outcomes in the presence of unintentional biases.
method Develops a unified framework to jointly optimize accuracy and fairness, recasting as mixed-integer convex programs.
result The framework can be used to enhance classification fairness by selecting more representative data points.
New metric MADD assesses fairness of predictive student models.
problem Predictive student models can be biased and unfair, leading to discrimination.
method Proposes MADD metric to analyze model's discriminatory behaviors.
result Fair predictive performance does not guarantee fair behaviors or outcomes.
We consider the problem of diversity enhancing clustering, i.e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attributes such as race, sex, age, etc. In the context of fair clustering, diversity plays a major role when fairness is understood as demographic…
This paper optimizes revenue and resource balance in network revenue management.
problem Maximizing revenue while ensuring fair resource consumption across different suppliers.
method Introduces a regularized revenue objective and a primal-dual UCB algorithm for continuous prices and balancing.
result Achieves a worst-case regret of O ~ ( N 5 / 2 T ) \widetilde O(N^{5/2}\sqrt{T}) O ( N 5/2 T ) for revenue maximization and balancing. Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken by police officers to accept-or-reject decisions taken by academics. In this context, each decision is taken by an expert who is typically …
FAML addresses biased evidence learning in multi-view learning, improving fairness and prediction reliability.
problem Biased evidence learning in multi-view learning, leading to unreliable uncertainty estimation.
method FAML introduces an adaptive prior and fairness constraint to balance evidence allocation across views.
result FAML enhances fairness and improves prediction reliability compared to state-of-the-art methods.
New model predicts multiple outputs with missing labels.
problem Missing group labels in multi-output regression.
method Weakly-supervised multi-output model using correlated Gaussian processes.
result Model excels in multi-output settings with missing labels.
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.
In this paper, we propose a new framework for mitigating biases in machine learning systems. The problem of the existing mitigation approaches is that they are model-oriented in the sense that they focus on tuning the training algorithms to produce fair results, while overlooking the fact that the training data can its…
DPNR preserves privacy of text representations using differential privacy.
problem Privacy leakage in deep learning text representations.
method DPNR uses Differential Privacy to provide formal privacy guarantees and dropout masking for enhanced privacy.
result DPNR reduces privacy leakage without significantly sacrificing main task performance.
Study data biases to predict algorithmic discrimination, developing a Data Bias Profile.
problem Data biases contribute to algorithmic discrimination, but their impact is understudied.
method Analyzed three common data biases across various datasets and models, developing a Data Bias Profile.
result Combination of proxies and label bias can lead to more significant discrimination than underrepresentation alone.
This paper uses LLMs for causal discovery with active learning and dynamic scoring to improve efficiency and fairness.
problem High computational demands and complexities of large-scale data in causal discovery.
method Metadata-based approach, BFS strategy, Active Learning, Dynamic Scoring Mechanism, LLM confidence scores.
result Significantly reduced number of queries and improved efficiency in causal graph construction.
Paper proposes decentralized annuities for better retirement security.
problem Current pension systems' limitations and fairness issues.
method Theoretical models and fairness concepts analysis.
result Decentralized annuities offer enhanced flexibility and social welfare.