  Large Language Models (LLMs) have shown remarkable capabilities in tasks such
as summarization, arithmetic reasoning, and question answering. However, they
encounter significant challenges in the domain of moral reasoning and ethical
decision-making, especially in complex scenarios with multiple stakeholders.
This paper introduces the Skin-in-the-Game (SKIG) framework, aimed at enhancing
moral reasoning in LLMs by exploring decisions' consequences from multiple
stakeholder perspectives. Central to SKIG's mechanism is simulating
accountability for actions, which, alongside empathy exercises and risk
assessment, is pivotal to its effectiveness. We validate SKIG's performance
across various moral reasoning benchmarks with proprietary and opensource LLMs,
and investigate its crucial components through extensive ablation analyses.
