Fairness

Multicalibration post-processing isn’t always necessary; models often implicitly achieve it, especially calibrated ones. For uncalibrated models, though, it significantly improves fairness.

This paper introduces Causally Fair DRO, a novel framework for robust optimization that addresses individual fairness concerns by incorporating causal structures and sensitive attributes, providing th…

Recommendation systems must balance user satisfaction with fair item exposure. This research provides a theoretical model and empirical validation showing that user preference diversity can significan…

VFair achieves harmless Rawlsian fairness in regression tasks without relying on sensitive demographic data by minimizing the variance of training losses.

Researchers unveil the origins of degree bias in Graph Neural Networks (GNNs), proving high-degree nodes’ lower misclassification probability and proposing methods to alleviate this bias for fairer GN…

Finetuning’s impact on worst-group accuracy is surprisingly nuanced, with common class-balancing methods sometimes hurting performance; a novel mixture method consistently outperforms others.

Novel algorithms trace the optimal balance between clustering quality and fairness, revealing all non-dominated solutions for various objectives.

SureMap, a new method, significantly boosts accuracy in single and multi-task disaggregated evaluations of AI models using limited data by transforming the problem into Gaussian mean estimation and cl…

Ensuring fair regression predictions without using sensitive attributes? This paper presents a novel post-processing algorithm, achieving demographic parity with strong theoretical guarantees and comp…

RashomonGB tackles predictive multiplicity in gradient boosting by introducing a novel inference technique to efficiently identify and mitigate conflicting model predictions, improving model selection…

This paper introduces proportionally fair non-centroid clustering, achieving fairness guarantees via novel algorithms and auditing methods, demonstrating significant improvements over traditional meth…

This paper reveals the surprising connection between individual and proportional fairness in clustering, showing that any approximation to one directly implies an approximation to the other, enabling …

This paper presents novel algorithms for online matching markets that prioritize fairness among dynamic agents, achieving asymptotic optimality in various scenarios and offering extensions to group-le…

This paper introduces efficient algorithms that leverage social choice theory to aggregate multiple individual preferences, resulting in a desirable collective AI policy.

Learning-augmented mechanisms for fair allocation achieve constant-factor approximation with accurate predictions and near-optimal approximation even with inaccurate ones.

First parameterized approximation schemes for fair-range k-median & k-means in Euclidean spaces are presented, offering faster (1+ε)-approximation algorithms.

OxonFair: a new open-source toolkit for enforcing fairness in binary classification, supporting NLP, Computer Vision, and tabular data, optimizing any fairness metric, and minimizing performance degra…

This paper reveals the surprising computational hardness of achieving fairness in low-rank approximation while offering efficient approximation algorithms.

This paper solves the open problem of achieving no-regret learning in online multi-agent Nash social welfare maximization.

Multi-group Proportional Representation (MPR) tackles skewed search results by measuring representation across intersectional groups, improving fairness in image retrieval.