Fairness

Algorithmic monoculture harms applicant selection and market efficiency; this paper introduces a model to analyze its effects in two-sided matching markets.

DPR, a novel bias mitigation method, robustly improves model performance by leveraging disagreement probability without needing bias labels, achieving state-of-the-art results.

Data balancing in machine learning can hurt fairness and robustness; this paper reveals when and why, offering solutions for safer AI.

AI bias amplification in decision-making is uncovered, showing how fair prediction scores can become discriminatory after thresholding, urging stronger regulatory oversight.

Localized Adaptive Risk Control (L-ARC) improves fairness and reliability of online prediction by providing localized statistical risk guarantees, surpassing existing methods in high-stakes applicatio…

Problem (FAIR) framework and FORM algorithm achieve flexible multi-stakeholder fairness in online recommendation systems, balancing platform revenue with user and item fairness.

Online fair division algorithm achieves Õ(T²/³) regret while guaranteeing envy-freeness or proportionality in expectation, a result proven tight.

Oracle-efficient algorithms conquer online multi-group learning, achieving sublinear regret even with massive, overlapping groups, paving the way for fair and efficient large-scale online systems.

FairWire tackles structural bias in graph machine learning, proposing a novel fairness regularizer and a fair graph generation framework for unbiased link prediction and graph generation.

Fairness-aware estimation of graphical models (GMs) tackles bias in GM estimations by integrating graph disparity error and a tailored loss function into multi-objective optimization, effectively miti…

FairnessWithoutHarm achieves fairer ML models without sacrificing accuracy by using an influence-guided active sampling method that doesn’t require sensitive training data.

Fairness in social influence maximization is achieved via optimal transport, optimizing both outreach and a new ‘mutual fairness’ metric that considers variability in outreach scenarios.

First non-wasteful algorithm achieving 1/2-approximation for class envy-freeness, class proportionality, and utilitarian social welfare in online class matching.

Fair Wasserstein Coresets (FWC) efficiently generates fair, representative subsets of large datasets for downstream machine learning tasks, improving fairness and utility.

Fair algorithms can leverage biased predictions to improve performance while guaranteeing fairness for all candidates.

This paper proposes a novel online bilateral trading algorithm maximizing the fair gain from trade and provides tight regret bounds under various settings.

Fair GLASSO ensures fair Gaussian graphical models by introducing novel bias metrics and a penalized maximum likelihood estimator to mitigate group biases in data.

FairBiNN, a novel bilevel neural network, achieves Pareto optimal solutions by simultaneously optimizing for accuracy and fairness, outperforming existing methods.

This paper presents novel, scalable algorithms for fair and efficient constrained resource allocation under uncertainty using robust and CVaR optimization.

This paper presents optimal fair mechanisms for dynamic auction design, maximizing seller revenue while guaranteeing minimum allocations to multiple buyer groups.