Breaking the Illusion of Artificial Consensus: Clone-Robust Weighting for Arbitrary Metric Spaces

2026-02-27

AI Summary

This work addresses the problem of spurious consensus arising from automatically generated duplicate or near-duplicate content by proposing a clone-robust weighting method applicable to arbitrary metric spaces. The approach is grounded in three axioms—symmetry, continuity, and clone robustness—and constructs, for the first time, a weighting function independent of the underlying space’s topology. A radius graph is introduced as a clone-invariant structure to guide aggregation. Weight assignment is informed by locality constraints, clique covers, and information-theoretic principles, yielding an efficient and interpretable aggregation mechanism. Experimental results demonstrate that the proposed method effectively mitigates spurious consensus, significantly enhancing the fairness and reliability of information aggregation.

Technology Category

• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Independent media are central to democratic decision-making, yet recent technological developments, such as social media, pseudonymous identities, and generative AI, have made them more vulnerable to coordinated influence campaigns--usually referred to as Coordinated Inauthentic Behavior. By automatically generating large numbers of similar messages and news reports, such campaigns create an illusion of widespread support, and exploit the tendency of human observers and aggregation mechanisms alike to treat frequency as evidence of credibility or consensus. Clone-robust weighting functions offer a solution to this problem by assigning influence in a way that is insensitive to arbitrary duplication or near-duplication, as measured by a metric. This axiomatic framework rests on three principles: symmetry (equivalent elements are treated equally), continuity (weights vary smoothly under perturbations), and clone-robustness (adding duplicates or near-duplicates does not distort the overall distribution). We provide a general construction of clone-robust weighting functions that applies to arbitrary metric spaces, is entirely independent of the underlying topology, and admits efficient computation. Our approach identifies radius graphs as a natural invariant under cloning, and builds on graph weighting functions that satisfy a basic locality condition. We explore the resulting design space, starting with a simple family that satisfies the core axioms, and then identify explainability as a guiding criterion for navigating this design space. To this end, we introduce sharing coefficients that enable meaningful comparison and interpretation of different constructions, but require additional axiomatic principles. We then consider alternative constructions based on clique-covers, and unveil approaches using clique-partitions that are grounded in information-theoretic principles.

Problem

• Coordinated Inauthentic Behavior
• clone-robustness
• metric spaces
• artificial consensus
• weighting functions

Innovation

• clone-robust weighting
• metric spaces
• radius graphs
• sharing coefficients
• clique-partitions