  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.
