SYMBIOSIS: Systems Thinking and Machine Intelligence for Better Outcomes in Society

2025-03-07

AI Summary

This paper addresses the high barrier to systems thinking and the resulting causal modeling inaccuracies and biases among AI developers due to insufficient access to community-sourced domain knowledge. To this end, we propose a generative co-piloting framework that synergistically integrates systems thinking with AI. Methodologically, we introduce (i) a novel bidirectional generation mechanism between natural language and causal graphs; (ii) deep integration of Community-Based System Dynamics (CBSD) into AI problem formulation; and (iii) the first open-source systems thinking model repository organized by United Nations Sustainable Development Goals (SDGs). Our contributions are threefold: (i) substantially lowering the technical threshold for modeling complex socio-technical systems, enabling causal reasoning by non-expert users; (ii) bridging domain knowledge gaps to enhance the social adaptability and fairness of AI interventions; and (iii) providing a scalable methodology and open-source toolchain for responsible, socially embedded AI.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• Making Systems Thinking accessible for societal challenges.
• Bridging the gap between ML developers and community-specific knowledge.
• Enhancing AI systems through causal and abductive reasoning frameworks.

Innovation

• AI-powered framework for Systems Thinking accessibility
• Generative co-pilot translates complex systems into natural language
• Open-source repository categorized by Sustainable Development Goals