Envisioning a Human-AI collaborative system to transform policies into decision models
This addresses the problem for government agencies and policy experts by enabling more efficient and accountable policy automation, though it is an incremental step given the open domain challenges.
The paper tackles the challenge of automating the transformation of eligibility policies for social services into executable decision models, proposing an AI-assisted approach that aims to shorten the process and improve transparency and interpretability.
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.