An Empirical Approach to Capture Moral Uncertainty in AI

As AI Systems become increasingly autonomous they are expected to engage in complex moral decision-making processes. For the purpose of guidance of such processes theoretical and empirical solutions have been sought. In this research we integrate both theoretical and empirical lines of thought to address the matters of moral reasoning in AI Systems. We reconceptualize a metanormative framework for decision-making under moral uncertainty within the Discrete Choice Analysis domain and we operationalize it through a latent class choice model. The discrete choice analysis-based formulation of the metanormative framework is theory-rooted and practical as it captures moral uncertainty through a small set of latent classes. To illustrate our approach we conceptualize a society in which AI Systems are in charge of making policy choices. In the proof of concept two AI systems make policy choices on behalf of a society but while one of the systems uses a baseline moral certain model the other uses a moral uncertain model. It was observed that there are cases in which the AI Systems disagree about the policy to be chosen which we believe is an indication about the relevance of moral uncertainty.