Deontic Temporal Logic for Formal Verification of AI Ethics
This addresses the critical need for formal verification of AI ethics to prevent discrimination and unfairness in high-stakes applications like criminal justice and finance, though it is an incremental step in applying existing logical frameworks to this domain.
The paper tackled the problem of ensuring ethical behavior in AI systems by proposing a formalization based on deontic temporal logic to specify and verify ethical properties like fairness and explainability. It evaluated this on real-world systems (COMPAS and loan prediction), finding that both failed to meet key ethical criteria, demonstrating the method's effectiveness in identifying issues.
Ensuring ethical behavior in Artificial Intelligence (AI) systems amidst their increasing ubiquity and influence is a major concern the world over. The use of formal methods in AI ethics is a possible crucial approach for specifying and verifying the ethical behavior of AI systems. This paper proposes a formalization based on deontic logic to define and evaluate the ethical behavior of AI systems, focusing on system-level specifications, contributing to this important goal. It introduces axioms and theorems to capture ethical requirements related to fairness and explainability. The formalization incorporates temporal operators to reason about the ethical behavior of AI systems over time. The authors evaluate the effectiveness of this formalization by assessing the ethics of the real-world COMPAS and loan prediction AI systems. Various ethical properties of the COMPAS and loan prediction systems are encoded using deontic logical formulas, allowing the use of an automated theorem prover to verify whether these systems satisfy the defined properties. The formal verification reveals that both systems fail to fulfill certain key ethical properties related to fairness and non-discrimination, demonstrating the effectiveness of the proposed formalization in identifying potential ethical issues in real-world AI applications.