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

Before this research, AI safety and fairness largely focused on model scalability and alignment. Companies like OpenAI prioritized making models larger and more powerful, assuming that greater capability equated to greater safety. Alignment, ensuring that AI systems act in accordance with human values, was also seen as a crucial path to safe AI. However, these approaches have not sufficiently addressed systemic issues that arise when multiple AI agents interact within a system. Imagine a city where every building is designed to be earthquake-proof, but the city's layout still makes it vulnerable to fires spreading rapidly. The individual buildings are safe, but the city as a whole is not because of how everything is connected.

The research identified specific failure modes that arise not from the scale or alignment of individual AI models, but from the way these models interact. Ordering Instability is one such pathology, where the order of agent interactions leads to inconsistent and unpredictable outcomes. Think of a jury deliberation where the first person to speak unduly influences the others, regardless of the merits of their argument. Information Cascades are another failure mode, occurring when early decisions disproportionately influence later ones, leading to potentially skewed consensus. Similarly, Functional Collapse refers to the degradation of a system's capabilities due to poor interaction structures, resulting in a loss of diversity in decision-making. These pathologies are systemic, rooted in the interaction topology of the agents.

The core insight of this paper is that the safety and fairness of agentic AI systems depend more on their interaction topology than on the scale or alignment of individual models. This shifts the traditional focus from improving single agents to examining collective behavior. Model Scale Irrelevance highlights that simply increasing model size does not solve issues like ordering instability or information cascades. In fact, larger models can exacerbate these problems by reinforcing consensus. This insight challenges the prevailing belief that scaling and aligning individual agents inherently lead to safer systems. Instead, it suggests that understanding and designing the right interaction topologies are crucial for addressing systemic pathologies.

The architecture proposed in this research involves Multi-Agent Simulation to study how different interaction topologies affect system outcomes. By creating environments where multiple AI models interact under various topologies, researchers can observe the impact of interaction structures on safety and fairness. This approach departs from traditional model-centric evaluations by focusing on information flow and decision coupling as the main determinants of system outcomes. The architecture is designed to highlight the influence of topologies, such as Sequential Deliberations and Parallel Voting Systems, on systemic pathologies.

Sequential Deliberations and Parallel Voting Systems are two types of interaction structures explored in this research. In Sequential Deliberations, agents make decisions one after another, which can lead to ordering instability as early decisions heavily influence later ones. This setup mimics scenarios like jury deliberations or committee meetings, where the sequence of interactions can significantly impact the outcome. On the other hand, Parallel Voting Systems allow agents to make decisions simultaneously, reducing the influence of ordering. However, this structure can still lead to information cascades if not managed properly. Both methods exemplify how interaction topology can affect systemic pathologies.

The study's results revealed consistent issues across different model families and scales, demonstrating that topological elements significantly influence system behavior. Systemic Pathologies like ordering instability, information cascades, and functional collapse emerged due to interaction structures, not model attributes. Notably, increasing model capability did not alleviate these problems; instead, it exacerbated them by solidifying consensus formation. Topology Influence was evident as different interaction structures consistently led to similar issues, emphasizing the need to prioritize topology design over scaling models.

This research suggests a Paradigm Shift in AI development, moving from a focus on model scalability and alignment to prioritizing interaction topology. This shift could change how AI systems are designed, particularly in sensitive domains like finance and healthcare. For instance, even advanced, aligned models can produce biased outcomes if their interactions aren't properly structured. Safety in Finance/Healthcare underscores the importance of considering interaction topologies in AI systems used in these areas. This shift may prompt product teams to reevaluate architectural designs and prioritize interaction topology assessments for future AI deployments.

Despite its insights, this research opens up several Future Research Directions. There is a need to explore new interaction topologies and their effects on AI safety. This includes investigating alternative structures that could mitigate systemic pathologies identified in the study. While the focus on topology provides a new perspective, it also presents challenges in designing and testing these structures at scale. Open questions remain about how best to implement and evaluate these topologies in real-world applications.

For product managers and developers, the implications of this research are significant. It suggests rethinking the design of AI systems, especially those used in critical areas like finance and healthcare, where safety and fairness are paramount. Understanding and designing the right interaction topologies can prevent systemic pathologies and ensure more reliable outcomes. This research challenges the reliance on model scalability and alignment alone, advocating for a more holistic approach to AI safety that considers how agents interact and influence each other.