Human input plays a critical role in modern AI systems. As machines take on increasingly nuanced tasks, it becomes essential for the community to embrace subjectivity and diverse perspectives. However, research on sensitive topics often fails to incorporate diverse and balanced perspectives. This paper makes a key contribution to participatory AI design in the context of conflicts between nuclear adversaries (India and Pakistan); where disagreement between stakeholders is anticipated. The paper explores the notion of hope speech detection -- detecting de-escalating content in the context of nuclear adversaries on the brink of war -- through the lens of participatory AI design and vicarious interactions. We release a dataset of 10,081 social web posts annotated by raters from India and Pakistan and examine the bipartisan nature of the language of de-escalation. Our study reveals that vicarious perspectives can be useful for modeling out-group preferences.