Verifiable Machine Ethics in Changing Contexts
Keywords:Morality & Value-based AI
AbstractMany systems proposed for the implementation of ethical reasoning involve an encoding of user values as a set of rules or a model. We consider the question of how changes of context affect these encodings. We propose the use of a reasoning cycle, in which information about the ethical reasoner's context is imported in a logical form, and we propose that context-specific aspects of an ethical encoding be prefaced by a guard formula. This guard formula should evaluate to true when the reasoner is in the appropriate context and the relevant parts of the reasoner's rule set or model should be updated accordingly. This architecture allows techniques for the model-checking of agent-based autonomous systems to be used to verify that all contexts respect key stakeholder values. We implement this framework using the hybrid ethical reasoning agents system (HERA) and the model-checking agent programming languages (MCAPL) framework.
How to Cite
Dennis, L. A., Bentzen, M. M., Lindner, F., & Fisher, M. (2021). Verifiable Machine Ethics in Changing Contexts. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 11470-11478. https://doi.org/10.1609/aaai.v35i13.17366
AAAI Technical Track on Philosophy and Ethics of AI