arXiv research

Paper proposes a federated learning framework for relative fairness.

A new approach to group fairness treats it as a bargaining problem.

Proposes a method to select fair performance metrics through metric elicitation.

Ditto improves fairness and robustness in federated learning.

The paper develops a method to achieve fairness in predictions using Wasserstein barycenters.

Since many critical decisions impacting human lives are increasingly being made by algorithms, it is important to ensure that the treatment of individuals under such algorithms is demonstrably fair under reasonable notions of fairness. One compelling notion proposed in the literature is that of individual fairness (IF)…

Study achieves fairness without demographic info, improving regression tasks.

A fair policy for hiring candidates from different groups is proposed in a linear contextual bandit problem.

The paper explores fair clustering, a niche area in machine learning.

DFL framework improves action and outcome fairness in policy learning.

This paper improves fairness in recommendation systems by learning individual preferences across multiple dimensions.

A new method learns fair classifiers without sacrificing accuracy.

The paper explores fairness in credit scoring using machine learning.

PropFair algorithm ensures fair performance in federated learning.

In the paper, we introduce the notion of a local regular supermartingale relative to a convex set of equivalent measures and prove for it the necessary and sufficient conditions of optional Doob decomposition in the discrete case. This Theorem is a generalization of the famous Doob decomposition onto the case of superm…

In the paper, the martingales and super-martingales relative to a convex set of equivalent measures are systematically studied. The notion of local regular super-martingale relative to a convex set of equivalent measures is introduced and the necessary and sufficient conditions of the local regularity of it in the disc…

The paper connects counterfactual fairness to robust prediction and group fairness using causal context.

FADE framework improves fairness and accuracy in ensemble learning.

This paper surveys fairness notions in ML and recommends the most suitable one for real-world scenarios.

Paper examines fairness of data augmentation methods, finding vanilla Mixup outperforms Fair Mixup.

The study analyzes the conflict between group fairness and individual fairness in machine learning.

New approach to algorithmic fairness for human-AI collaboration considers compliance with human decisions.

This paper develops a new method for eliciting more flexible metrics, improving fairness and applicability.

People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: giving adequate success rates to specifically protected groups. I…

UDJ-FL framework achieves multiple distributive justice-based fairness metrics in federated learning.

Develops causal framework for fair survival analysis in healthcare.

This paper critiques machine learning fairness from a consequentialist perspective.

The paper proposes a method to measure fairness through equality of effort using algorithmic recourse.

The paper tackles fair sharing of exploration costs across groups in online learning.

FWC creates fair synthetic samples for machine learning tasks.

We clarify what fairness guarantees we can and cannot expect to follow from unconstrained machine learning. Specifically, we characterize when unconstrained learning on its own implies group calibration, that is, the outcome variable is conditionally independent of group membership given the score. We show that under r…

The paper tackles fair correlation clustering with fairness constraints.

Proposes a method to balance fairness and utility in ranking models.

Unified framework TERM improves fairness and robustness.

Approach to fairness in machine learning models using regularization and Gaussian processes.

Improves AUC for disadvantaged groups by adding features.

This paper studies the stochastic modeling of market drawdown events and the fair valuation of insurance contracts based on drawdowns. We model the asset drawdown process as the current relative distance from the historical maximum of the asset value. We first consider a vanilla insurance contract whereby the protectio…

We study the problem of finding probability densities that match given European call option prices. To allow prior information about such a density to be taken into account, we generalise the algorithm presented in Neri and Schneider (2011) to find the maximum entropy density of an asset price to the relative entropy c…

Develops risk-averse fair multi-class classification methods.

Predictive models are increasingly deployed for the purpose of determining access to services such as credit, insurance, and employment. Despite potential gains in productivity and efficiency, several potential problems have yet to be addressed, particularly the potential for unintentional discrimination. We present an…

Extends micro-price concept to RFQ markets for fair pricing.

Local discovery method uncovers direct unfairness in complex systems.

Study shows fairness metrics are unreliable for small datasets in NLP tasks.

EL framework certifies and flags bias in ML models without distributional assumptions.

This paper studies a two-person trading game in continuous time that generalizes Garivaltis (2018) to allow for stock prices that both jump and diffuse. Analogous to Bell and Cover (1988) in discrete time, the players start by choosing fair randomizations of the initial dollar, by exchanging it for a random wealth whos…

Two simple methods learn fair metrics from data to improve fairness in ML tasks.

New concept of within-group fairness improves AI fairness without sacrificing accuracy.

A new fairness metric for decision-making algorithms, conditioning on known fair variables.