SYMBIOSIS: Systems Thinking and Machine Intelligence for Better Outcomes in Society
Sameer Sethi, Donald Martin, Emmanuel Klu
TL;DR
SYMBIOSIS tackles the gap between AI systems and societal context by building an open, centralized repository of systems thinking models and an AI co-pilot that translates diagrams to natural language. It integrates causal loop and stock-and-flow representations with SDG classification and topic modeling to support problem understanding and responsible AI development. Key contributions include an XMILE-based data extraction pipeline, an Elasticsearch-backed repository, and a generative co-pilot that bridges notation and natural language. The work promises to enable more context-aware, system-aware AI in high-stakes domains by lowering entry barriers and fostering community-informed problem formulation.
Abstract
This paper presents SYMBIOSIS, an AI-powered framework and platform designed to make Systems Thinking accessible for addressing societal challenges and unlock paths for leveraging systems thinking frameworks to improve AI systems. The platform establishes a centralized, open-source repository of systems thinking/system dynamics models categorized by Sustainable Development Goals (SDGs) and societal topics using topic modeling and classification techniques. Systems Thinking resources, though critical for articulating causal theories in complex problem spaces, are often locked behind specialized tools and intricate notations, creating high barriers to entry. To address this, we developed a generative co-pilot that translates complex systems representations - such as causal loop and stock-flow diagrams - into natural language (and vice-versa), allowing users to explore and build models without extensive technical training. Rooted in community-based system dynamics (CBSD) and informed by community-driven insights on societal context, we aim to bridge the problem understanding chasm. This gap, driven by epistemic uncertainty, often limits ML developers who lack the community-specific knowledge essential for problem understanding and formulation, often leading to ill informed causal assumptions, reduced intervention effectiveness and harmful biases. Recent research identifies causal and abductive reasoning as crucial frontiers for AI, and Systems Thinking provides a naturally compatible framework for both. By making Systems Thinking frameworks more accessible and user-friendly, SYMBIOSIS aims to serve as a foundational step to unlock future research into responsible and society-centered AI. Our work underscores the need for ongoing research into AI's capacity to understand essential characteristics of complex adaptive systems paving the way for more socially attuned, effective AI systems.