arXiv:2507.05488v2 Announce Type: replace Abstract: We present OLG++, a semantic extension of the Obligation Logic Graph (OLG) for modeling regulatory and legal rules in municipal and interjurisdictional contexts. OLG++ introduces richer node and edge types, including spatial, temporal, party group, defeasibility, and logical grouping constructs, enabling nuanced representations of legal obligations, exceptions, and hierarchies. The model supports structured representation of rules with contextual conditions, precedence, and complex triggers. We demonstrate its use through examples from food-b
The increasing complexity of AI systems and their application in regulated environments necessitates more robust and semantically rich frameworks for legal and ethical compliance modeling.
This development is important for strategic readers as it offers a formal method to encode and enforce regulatory compliance in AI applications, crucial for legal, ethical, and societal integration of advanced AI.
The ability to model regulatory and legal rules with increased nuance, including spatial, temporal, and defeasibility aspects, changes how AI systems can interpret and operate within complex legal frameworks.
- · AI governance platforms
- · LegalTech (AI-powered)
- · Regulatory bodies
- · Compliance software vendors
- · Companies with opaque AI/ML systems
- · Purely statistical AI compliance models
Improved automated legal and regulatory compliance checking for AI systems.
Reduced legal risks and increased trustworthiness for AI applications in sensitive sectors like finance and healthcare.
The potential for AI systems to generate legal arguments or identify loopholes in complex regulatory texts autonomously.
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Read at arXiv cs.AI