Moral Reasoning under Uncertainty

Abstract

We present a Logic Programming framework for moral reasoning under uncertainty. It is enacted by a coherent combination of our two previously implemented systems, Evolution Prospection for decision making, and P-log for probabilistic inference. It allows computing available moral judgments via distinct kinds of prior and post preferences. In introducing various aspects of uncertainty into cases of classical trolley problem moral dilemmas, we show how they may appropriately influence moral judgments, allowing decision makers to opt for different choices, and for these to be externally appraised, even when subject to incomplete evidence, as in courts.