Deontic Temporal Logic for Formal Verification of AI Ethics

TL;DR

This paper introduces a deontic temporal logic framework for formal verification of AI ethics, enabling the assessment of ethical properties like fairness over time in real-world AI systems.

Contribution

It presents a novel formalization combining deontic and temporal logic to specify and verify ethical requirements in AI systems, demonstrated on COMPAS and loan prediction models.

Findings

Both AI systems failed key fairness and non-discrimination properties.

The formalization successfully identified ethical issues in real-world AI applications.

Automated theorem proving verified compliance with specified ethical properties.

Abstract

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.