Learning Reduced Order Dynamics via Geometric Representations
Imran Nasim, Melanie Weber
SCML 2024
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 are gathering broad public-sector interest. We present a visionary approach to shorten the route from policy documents to executable, interpretable, standardized decision models using AI, NLP and Knowledge Graphs. Despite the many domain challenges, 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.
Imran Nasim, Melanie Weber
SCML 2024
Ronen Feldman, Martin Charles Golumbic
Ann. Math. Artif. Intell.
Leonid Karlinsky, Joseph Shtok, et al.
CVPR 2019
Hironori Takeuchi, Tetsuya Nasukawa, et al.
Transactions of the Japanese Society for Artificial Intelligence