arXiv:2604.17805v2 Announce Type: replace-cross Abstract: Pairwise ranking systems based on Maximum Likelihood Estimation (MLE), such as the Bradley-Terry model, are widely used to aggregate preferences from pairwise comparisons. However, their robustness under strategic data manipulation remains insufficiently understood. In this paper, we study the vulnerability of MLE-based ranking systems to adversarial perturbations. We formulate the manipulation task as a constrained combinatorial optimization problem and propose an Adaptive Subset Selection Attack (ASSA) to efficiently identify high-imp
The proliferation of AI-driven ranking and recommendation systems necessitates a deeper understanding of their vulnerabilities to manipulation, especially as they become more integrated into critical decision-making processes.
This research highlights a fundamental vulnerability in widely used ranking systems, posing risks to fairness, data integrity, and trust in AI-driven aggregation platforms across various sectors.
The ability to strategically perturb pairwise data to manipulate ranking systems means that traditional robustness assumptions for MLE-based models may be insufficient, requiring new defensive mechanisms and increased scrutiny of data provenance.
- · Cybersecurity researchers
- · AI ethics and safety organizations
- · Developers of robust AI ranking algorithms
- · Platforms relying solely on naive MLE-based ranking
- · Users vulnerable to manipulated rankings
- · Data integrity in AI systems
Increased research and development into adversarial robustness for machine learning models, especially ranking and recommendation systems, will accelerate.
New regulatory frameworks and compliance standards might emerge to ensure the integrity and fairness of AI-driven ranking systems.
Public distrust in AI-generated rankings could grow, potentially influencing user behavior and platform adoption, or leading to demand for more transparent and explainable AI.
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Read at arXiv cs.AI