MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in
  Language Models, More than Outcomes
Authors: name: Yu Ying Chiu, name: Michael S. Lee, name: Rachel Calcott, name: Brandon Handoko, name: Paul de Font-Reaulx, name: Paula Rodriguez, name: Chen Bo Calvin Zhang, name: Ziwen Han, name: Udari Madhushani Sehwag, name: Yash Maurya, name: Christina Q Knight, name: Harry R. Lloyd, name: Florence Bacus, name: Mantas Mazeika, name: Bing Liu, name: Yejin Choi, name: Mitchell L Gordon, name: Sydney Levine

As AI systems progress, we rely more on them to make decisions with us and
for us. To ensure that such decisions are aligned with human values, it is
imperative for us to understand not only what decisions they make but also how
they come to those decisions. Reasoning language models, which provide both
final responses and (partially transparent) intermediate thinking traces,
present a timely opportunity to study AI procedural reasoning. Unlike math and
code problems which often have objectively correct answers, moral dilemmas are
an excellent testbed for process-focused evaluation because they allow for
multiple defensible conclusions. To do so, we present MoReBench: 1,000 moral
scenarios, each paired with a set of rubric criteria that experts consider
essential to include (or avoid) when reasoning about the scenarios. MoReBench
contains over 23 thousand criteria including identifying moral considerations,
weighing trade-offs, and giving actionable recommendations to cover cases on AI
advising humans moral decisions as well as making moral decisions autonomously.
Separately, we curate MoReBench-Theory: 150 examples to test whether AI can
reason under five major frameworks in normative ethics. Our results show that
scaling laws and existing benchmarks on math, code, and scientific reasoning
tasks fail to predict models' abilities to perform moral reasoning. Models also
show partiality towards specific moral frameworks (e.g., Benthamite Act
Utilitarianism and Kantian Deontology), which might be side effects of popular
training paradigms. Together, these benchmarks advance process-focused
reasoning evaluation towards safer and more transparent AI.