Envisioning a Human-AI collaborative system to transform policies into decision models

TL;DR

This paper explores a novel AI-driven approach to transform eligibility policies into decision models, aiming to enhance transparency, interpretability, and automation in government decision-making processes.

Contribution

It introduces a semi-automatic method leveraging AI, NLP, and Knowledge Graphs to convert policy documents into executable decision models, addressing a key challenge in digital governance.

Findings

Potential to significantly reduce policy-to-decision model conversion time

Improves transparency and interpretability of decision-making

Lays groundwork for scalable, AI-assisted policy automation

Abstract

Regulations govern many aspects of citizens' daily lives. Governments and businesses routinely automate these in the form of coded rules (e.g., to check a citizen's eligibility for specific benefits). However, the path to automation is long and challenging. To address this, recent global initiatives for digital government, proposing to simultaneously express policy in natural language for human consumption as well as computationally amenable rules or code, are gathering broad public-sector interest. We introduce the problem of semi-automatically building decision models from eligibility policies for social services, and present an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. Despite the many open domain challenges, in this position paper we explore the enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.