arXiv Machine Learning
Jun 26
Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking
arXiv:2606. 26369v1 Announce Type: cross Abstract: Scoring functions are used to represent the relevance of individual documents.
A fairness-aware extension of Stochastic Multicriteria Acceptability Analysis for ranking
arXiv:2606. 17756v1 Announce Type: new Abstract: Fairness has become a central concern in ranking problems involving individuals or social groups, particularly under the Responsible Artificial Intelligence agenda.
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arXiv Machine Learning
Aug 11
Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study
arXiv:2608. 09899v1 Announce Type: new Abstract: In fair ranked link prediction, demographic parity ($\Delta_\mathrm{DP}$) is a common fairness metric.
arXiv Machine Learning
Jun 9
Generalizing Fair Top-$k$ Selection: An Integrative Approach
arXiv:2603. 04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention.
arXiv AI
Aug 7
Stability of Ranking-dependent Pair-wise Comparison Patterns in the Analytic Hierarchy Process
arXiv:2608. 05958v1 Announce Type: new Abstract: The paper addresses several ranking-dependent decision support methods.
arXiv AI
Jun 9
PAFO: Pareto Fairness Optimization for Personalized Reward Modeling
arXiv:2606. 07988v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on reward models to align their outputs with diverse user preferences.
arXiv Machine Learning
Jun 9
Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints
arXiv:2601. 23221v2 Announce Type: replace Abstract: As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical resort.
arXiv Machine Learning
Jun 9
Nonparametric LLM Evaluation from Preference Data
arXiv:2601. 21816v2 Announce Type: replace Abstract: Evaluating the performance of large language models (LLMs) from human preference data is crucial for obtaining LLM leaderboards.
arXiv AI
Jul 28
Fairness Interventions in Classification: A Study on AI Explainability
arXiv:2407. 14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds.
arXiv Machine Learning
Jun 2
Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents
arXiv:2606. 00826v1 Announce Type: new Abstract: Strategic machine learning investigates scenarios where agents manipulate their features to receive favorable decisions from predictive models.
arXiv Machine Learning
Jun 5
A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth
arXiv:2601. 21817v2 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on open-ended tasks without ground-truth labels is increasingly done via the LLM-as-a-judge paradigm.
arXiv AI
Jun 2
Demystifying the Optimal Fair Classifier in Multi-Class Classification
arXiv:2606. 00656v1 Announce Type: cross Abstract: Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent in machine learning models.
arXiv Machine Learning
Aug 10
Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning
arXiv:2608. 06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation.