The paper introduces a new algorithm for fair decision-making in outcome control tasks.
problem Fair and equitable automated decision-making in outcome control tasks.
method Causal analysis and optimization to ensure fairness in decision-making.
result Developed an algorithm for maximizing Y while ensuring causal fairness. Fair insurance contracts are designed to handle default risk using cooperative game theory.
problem Designing fair insurance contracts in the presence of default risk.
method Cooperative game theory to specify premiums and participation in benefit.
result Fair benefit participation emerges as a game outcome involving residual risks.
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context…
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.
PyCFRL helps ensure fair reinforcement learning policies from offline data.
problem Ensuring fairness in reinforcement learning policies for disadvantaged groups.
method Sequential data preprocessing to learn counterfactually fair policies.
result PyCFRL implements a novel algorithm for fair RL policy learning.
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.
Study finds incorporating fairness in healthcare models doesn't improve performance or net benefit.
problem Addressing health inequities in healthcare through algorithmic fairness.
method Empirical case study using models to estimate atherosclerotic cardiovascular disease risk.
result Incorporating fairness considerations into model training objective does not improve model performance or net benefit.
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.
Semi-supervised learning benefits the rich more than the poor, affecting fairness.
problem Disparate impact of semi-supervised learning on different sub-populations.
method Theoretical and empirical analysis of a broad family of SSL algorithms using pseudo-labels.
result Semi-supervised learning benefits the rich more than the poor, potentially violating fairness.
Ditto improves fairness and robustness in federated learning.
problem Fairness and robustness in statistically heterogeneous federated learning networks.
method Personalized federated learning framework (Ditto) with a scalable solver.
result Ditto achieves competitive performance and superior fairness and robustness compared to existing methods.
New algorithm improves group fairness in social classification problems by exploiting performativity.
problem Inequities in social classification problems due to performativity.
method Develops algorithmic fairness practices that leverage performativity to achieve stronger group fairness guarantees.
result Achieves stronger group fairness guarantees compared to non-performative settings.
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.
The study tests and optimizes fairness in credit scoring models.
problem Discrimination in credit scoring models based on protected attributes.
method Formal testing and variable identification to optimize fairness and performance.
result Guidance on monitoring and improving algorithmic fairness in credit scoring.
The paper explores fairness, welfare, and equity in personalized pricing across various applications.
problem Interplay of fairness, welfare, and equity in personalized pricing based on customer features.
method Comprehensive literature review and observational metrics without underlying valuation distribution assumptions.
result Personalized pricing can expand access, improve welfare, and increase revenue or budget utilization.
The potential lack of fairness in the outputs of machine learning algorithms has recently gained attention both within the research community as well as in society more broadly. Surprisingly, there is no prior work developing tree-induction algorithms for building fair decision trees or fair random forests. These metho…
Recent studies on fairness in automated decision making systems have both investigated the potential future impact of these decisions on the population at large, and emphasized that imposing ''typical'' fairness constraints such as demographic parity or equality of opportunity does not guarantee a benefit to disadvanta…
This paper introduces a novel framework for designing fair and sustainable unemployment benefits, grounded in cooperative game theory and real-time fiscal policy. The labor market is modeled as a coalitional game, where a random subset of participants is employed, generating stochastic economic output. To ensure fairne…
Bayesian framework uses unlabeled data to improve fairness assessment.
problem Reliable fairness assessment with limited labeled data.
method Hierarchical latent variable model with Bayesian inference.
result Significant reduction in estimation error for fairness metrics.
New clustering method ensures fairness and community preservation.
problem Fairness in clustering, especially for data points representing people.
method Developed an approach to extend k-center algorithms to satisfy pairwise fairness and community preservation. result Reasonable approximations of optimal clustering can be achieved while maintaining fairness.
New approach for fair predictions under changing data distributions.
problem Fairness in classification algorithms under covariate shift.
method Proposes a robust predictor that satisfies fairness and maintains statistical properties of source data.
result Demonstrates improved fairness and target performance on benchmark tasks.
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these au…
We tackle the problem of algorithmic fairness, where the goal is to avoid the unfairly influence of sensitive information, in the general context of regression with possible continuous sensitive attributes. We extend the framework of fair empirical risk minimization to this general scenario, covering in this way the wh…
Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming …
Various types of structures that enable a group of individuals to pool their mortality risk have been proposed in the literature. Collectively, the structures are called pooled annuity funds. Since the pooled annuity funds propose different methods of pooling mortality risk, we investigate the connections between them …
Fair classification has been a topic of intense study in machine learning, and several algorithms have been proposed towards this important task. However, in a recent study, Friedler et al. observed that fair classification algorithms may not be stable with respect to variations in the training dataset -- a crucial con…
The paper explores fairness in machine learning, focusing on Equalized Odds.
problem Whether Equalized Odds fairness can always be achieved and if it leads to better prediction performance.
method Analyzes the attainability and optimality of Equalized Odds fairness in various settings.
result Equalized Odds can be achieved under certain conditions and can lead to better prediction performance.
This primer tackles biases in machine learning for image analysis, proposing solutions.
problem Causal and statistical biases in machine learning methods for image analysis.
method Introduction of causal and statistical structures that induce failure, highlighting two problems: no fair lunch and subgroup separability.
result Current fair representation learning methods fail to solve these problems, suggesting new paths forward.
New approach to explain fairness in machine learning models.
problem Detect, understand, and mitigate unfairness in machine learning models.
method Shapley value paradigm and meta algorithm for training-time fairness interventions.
result Meta algorithm provides insight into accuracy-fairness trade-off.
When an AI system interacts with multiple users, it frequently needs to make allocation decisions. For instance, a virtual agent decides whom to pay attention to in a group setting, or a factory robot selects a worker to deliver a part. Demonstrating fairness in decision making is essential for such systems to be broad…
Proposes SSFair to improve fairness in machine learning using unlabeled data.
problem Fairness issues in machine learning systems due to lack of labeled data.
method Semi-supervised learning with neural networks to leverage unlabeled data.
result Improves fairness of decision-making processes without requiring labeled data.
Improves model fairness under changing bias between labels and sensitive groups.
problem Fairness of models deteriorates when bias between labels and sensitive groups changes.
method Introduces correlation shifts to explicitly capture bias changes and proposes a pre-processing step to adjust data ratios.
result Our approach effectively improves model accuracy and fairness, both synthetic and real datasets.
The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk a…
Paper protects privacy and fairness in deep learning models.
problem Ensuring fairness in deep learning models while protecting sensitive data.
method Uses differential privacy and Lagrangian duality to design fair predictors.
result Demonstrates improved model performance on prediction tasks.
In this paper we explore an identity in distribution of hitting times of a finite variation process (Yor's process) and a diffusion process (geometric Brownian motion with affine drift), which arise from various applications in financial mathematics. As a result, we provide analytical solutions to the fair charge of va…
New research shows fairness in machine learning can sometimes make disadvantaged groups worse off.
problem The impact of fairness constraints in machine learning on different groups.
method Unified, population-level (Bayes) framework for binary classification under prevalent group fairness notions.
result Fairness in machine learning can lead to leveling down, making one or both groups worse off.
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.
The paper explores the tradeoff between fairness and accuracy in regression models.
problem Characterizing the tradeoff between fairness and accuracy in regression models.
method Provided a lower bound on the error of any fair regressor and extended the result to joint error using Wasserstein distance.
result Lower bounds on the error of fair regressors and their connection to Wasserstein distance.
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.
As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair …
FairBatch optimizes model fairness without changing data or model training.
problem Improving model fairness without altering data or model training.
method Bilevel optimization with an outer optimizer for adaptive batch selection.
result FairBatch improves model fairness without changing data or model training.
DQ4FairIM uses RL to maximize influence while ensuring fairness across all groups.
problem Fairness in influence maximization in social networks.
method Fairness-aware deep RL method using Structure2Vec network embedding.
result DQ4FairIM achieves higher fairness than fairness-agnostic and fairness-aware baselines.
The paper explores fair predictors in supervised learning using IPMs and Kolmogorov distance.
problem Achieving fairness in supervised learning with significant demographic effects.
method Identifying conditions for SP-fair predictors and using IPMs to measure unfairness.
result Fair predictors can improve accuracy and are computationally efficient.
Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies. These models have differential performance across race and gender groups with inconsistent behavior a…
Unified framework TERM improves fairness and robustness.
problem Outliers and subgroup fairness in empirical risk minimization.
method Unified framework TERM with a hyperparameter tilt.
result TERM improves fairness and robustness.
Online TERM improves robustness and fairness in streaming data.
problem Streaming data's lack of worst-case fairness and robustness in ERM.
method Proposes an online TERM formulation to balance average-case accuracy with worst-case fairness and robustness.
result Negative tilting effectively suppresses outlier influence, positive tilting improves recall with minimal precision loss.
CAP adapts optimization to class attributes for better fairness.
problem Heterogeneities across classes impede classification performance.
method CAP generates class-specific learning strategies based on attributes.
result CAP improves over naive approach and is competitive with prior art.
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.
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 …