Approximating Defeasible Logics to Improve Scalability

Defeasible logic enables AI systems to handle exceptions and context, making it useful for representing legal documents and creating AI systems that can explain their reasoning—both critical needs for responsible AI deployment.

Scalability is a practical barrier to responsible AI: The paper addresses how defeasible logic systems can become computationally slow as they grow larger, which limits their real-world application in high-stakes domains like law and compliance.

Approximation can maintain accuracy while improving speed: The research shows that under certain conditions, a faster approximation method ($DL(\partial_{||})$) can produce identical conclusions to more rigorous approaches, suggesting a path to deploy responsible AI systems without sacrificing correctness.

Hybrid approaches balance competing demands: Rather than choosing between speed and accuracy, the paper proposes using the faster approximation method alongside the more thorough one, allowing systems to quickly resolve some cases while carefully analyzing others—a pragmatic model for responsible AI implementation.

Transparency in AI reasoning requires computational feasibility: For AI systems to be truly explainable and trustworthy, the underlying logical frameworks must be implementable at scale; this work addresses that technical prerequisite for responsible AI in practice.