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Evidence record 5092 · automatically gathered

Position: Safety and Fairness in Agentic AI Depend on Interaction Topology, Not on Model Scale or Alignment

As large language models are increasingly deployed as interacting agents in high-stakes decisions, the AI safety community assumes that safety properties of individual models will compose into safe multi-agent behavior. This position paper argues that this assumption is fundamentally mistaken. In agentic AI, safety is determined by interaction topology, not model weights. When agents deliberate sequentially or aggregate via parallel voting with a judge, the structure of information flow and deci

Record details

Published: 1 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (1 May 2026), “Position: Safety and Fairness in Agentic AI Depend on Interaction Topology, Not on Model Scale or Alignment,” evidence record 5092, https://ethics.ai/record/5092 (originally published by arXiv).

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