Using experimental game theory to transit human values to ethical AI
This work addresses the challenge of ensuring ethical AI for developers and policymakers, though it appears incremental by building on existing game theory and ethics frameworks.
The paper tackles the problem of aligning AI with human ethical values by developing a mathematical framework that connects moral philosophy concepts to AI ethics standards, and demonstrates how experimental game theory can be used to derive human values and test AI algorithms for ethical behavior.
Knowing the reflection of game theory and ethics, we develop a mathematical representation to bridge the gap between the concepts in moral philosophy (e.g., Kantian and Utilitarian) and AI ethics industry technology standard (e.g., IEEE P7000 standard series for Ethical AI). As an application, we demonstrate how human value can be obtained from the experimental game theory (e.g., trust game experiment) so as to build an ethical AI. Moreover, an approach to test the ethics (rightness or wrongness) of a given AI algorithm by using an iterated Prisoner's Dilemma Game experiment is discussed as an example. Compared with existing mathematical frameworks and testing method on AI ethics technology, the advantages of the proposed approach are analyzed.